Previous Article in Journal
Quality Management in a Hemostasis Laboratory
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Non-Coding RNAs in Health and Disease: From Biomarkers to Therapeutic Targets

by
Marios A. Diamantopoulos
,
Michaela A. Boti
,
Triantafyllia Sarri
and
Andreas Scorilas
*
Department of Biochemistry and Molecular Biology, Faculty of Biology, National and Kapodistrian University of Athens, Panepistimiopolis, 15701 Athens, Greece
*
Author to whom correspondence should be addressed.
LabMed 2025, 2(3), 17; https://doi.org/10.3390/labmed2030017
Submission received: 10 July 2025 / Revised: 14 August 2025 / Accepted: 15 September 2025 / Published: 17 September 2025

Abstract

Non-coding RNAs (ncRNAs) are critical regulators of gene expression, taking part in the modulation of multiple biological functions across a range of cell types. Initially dismissed as transcriptional noise, ncRNAs are now recognized for their significant roles in key cellular mechanisms, including differentiation, apoptosis, and proliferation, as well as their profound implications for the pathogenesis of numerous human diseases. Due to their remarkable stability, tissue-specific expression patterns, and abundance in body fluids, ncRNAs hold significant promise as non-invasive biomarkers for diagnosis, prognosis, and therapeutic monitoring. Furthermore, advances in RNA-targeted therapeutics have introduced novel strategies to modulate ncRNA activity, although challenges related to delivery efficiency, specificity, and clinical validation remain. This review comprehensively summarizes the classification, biogenesis, and molecular functions of ncRNAs, elucidates their involvement in health and disease, and evaluates their potential as clinical biomarkers and therapeutic targets. Additionally, it discusses the emerging technologies for RNA manipulation, including CRISPR-based RNA editing, that can advance ncRNA research and revolutionize ncRNA-based therapeutics.

1. Introduction

The central dogma of biology describes the flow of genetic information from DNA to messenger RNA (mRNA) and subsequently to protein synthesis [1], defining genes primarily as DNA sequences that encode proteins. However, the completion of the Human Genome Project (HGP) and the advancements in high-throughput sequencing technologies and bioinformatics led to the expansion of the term “gene” beyond this classical protein-coding definition, improving our understanding of the genome and its functions [2]. Early expectations posited that most transcribed sequences would be translated into proteins, with the genome serving mainly as a repository of protein-coding genes. Contrary to our original assumptions, today it is known that less than 3% of the genome encodes proteins [3]. Data from the ENCODE project further revealed transcriptional activity in approximately 97% of the previously considered “junk DNA”, producing a diverse array of RNA molecules that do not encode proteins, known as non-coding RNAs (ncRNAs) [4].
Initially ignored as transcriptional noise, ncRNAs are now recognized as key players in essential biological processes across organisms. They play crucial roles in controlling gene expression, maintaining cellular homeostasis, and regulating normal developmental process, while also contributing significantly to the pathogenesis of diseases including cancer, cardiovascular disorders, neurodegenerative diseases, and metabolic syndromes [5,6,7,8,9,10]. Moreover, their distinct expression profiles in tissues and body fluids highlight their potential as valuable biomarkers for diagnosing diseases, predicting outcomes, and monitoring treatment responses. For instance, the most extensively studied class of ncRNAs, microRNAs (miRNAs), were found to exhibit distinct expression patterns between healthy and cancerous tissues, and even among different subtypes of the same cancer, underscoring their potential utility as diagnostic biomarkers [11]. These observations raised the possibilities of their exploitation in clinical use, not only in disease detection and outcome prediction, but also in the selection of the optimal treatment strategy. Utilizing such biomarkers to guide therapeutic decisions can set the founding for the establishment of personalized medicine and the introduction of effective therapies [12]. Consequently, ncRNAs are emerging as pivotal tools in advancing precision and personalized approaches for cancer treatment [13]. Except from their utility as diagnostic biomarkers for various diseases, ncRNAs are also emerging as powerful therapeutic agents in “liquid therapeutics” due to their ability to regulate gene networks. Multiple strategies are in place to inhibit or mimic ncRNAs, with several candidates already progressing through clinical trials. The therapeutic potential of ncRNAs gained ground following the FDA’s approval of a ncRNA-based drug for hereditary transthyretin amyloidosis (hATTR) in 2018, marking a milestone that has accelerated efforts to exploit ncRNAs for the treatment of diverse medical conditions [14].
This review aims to provide a comprehensive overview of the roles of ncRNAs in human health and disease, highlighting their potential utility primarily as biomarkers and, secondarily, as therapeutic targets. In the present review, we provide a general background of the ncRNAs in normal conditions, including their classification, biogenesis pathways, and key mechanisms of action, as well as their implication in the development and progression of various diseases. In addition, we discuss the growing interest in the emerged ncRNA-targeted therapeutics and the recent technological advances such as CRISPR-based RNA editing, that are about to advance ncRNA research and revolutionize ncRNA-based therapeutics.

2. Classification and Biogenesis of ncRNAs

ncRNAs are classified based on their length into two main categories: long non-coding RNAs (lncRNAs), which constitute of more than 200 nucleotides, and small non-coding RNAs, which are shorter than 200 nucleotides [15]. Fu further proposed a functional classification of ncRNAs into two groups: housekeeping and regulatory. Housekeeping ncRNAs, including ribosomal RNAs (rRNAs), transfer RNAs (tRNAs), small nuclear RNAs (snRNAs) and small nucleolar (snoRNAs), play essential roles in fundamental cellular processes [16,17], while regulatory ncRNAs are primarily involved in modulating gene expression. Among these regulatory molecules, lncRNAs—further classified into linear lncRNAs and circular RNAs (circRNAs)—account for approximately 81.8% of all ncRNAs and contribute to normal cellular function and tissue homeostasis. Small non-coding RNAs, which include miRNAs, snoRNAs, and PIWI- interacting RNAs (piRNAs), primarily act as negative controllers of gene expression at a post-transcriptional level, but have more recently emerged as key players in RNA metabolism at the transcriptional and translational levels [18,19]. Additionally, fragments derived from housekeeping RNAs, such as tRNA fragments (tRFs) and rRNA fragments (rRFs), are now recognized as distinct functional molecules rather than byproducts of RNA degradation, representing an emerging class of small ncRNAs with diverse and active roles in molecular pathways [20]. This section offers a detailed overview of the classification and biogenesis of ncRNAs, providing essential context for understanding their molecular diversity, origins, and functional relevance in cellular processes.

2.1. miRNAs: Master Regulators of Gene Expression

miRNAs are among the most extensively studied small ncRNAs, and their discovery has significantly advanced our knowledge regarding gene regulation [21]. The identification of lin-4 and let-7 in C. elegans laid the foundation for elucidating a highly conserved post-transcriptional regulatory mechanism mediated by base pairing between miRNAs and their target mRNAs [22,23,24,25]. To date, about 30,000 miRNAs have been identified across various species, with around 2000 of them reported in humans [26].
miRNAs are small, single-stranded RNA molecules approximately 22 nucleotides in length, that regulate gene expression post-transcriptionally (Table 1). The most critical feature of a mature miRNA is the seed region, which is a conserved heptametrical sequence mainly situated at positions 2–7 from the miRNA 5′-end. This region is essential for target recognition and exhibits perfect complementarity to its target mRNA. These regulatory molecules are typically transcribed by RNA polymerase II from endogenous sequences that form hairpin structures but some, particularly those associated with Alu elements, are transcribed by RNA polymerase III (Figure 1) [27,28]. Approximately half of the known miRNAs are organized into clusters and are transcribed as polycistronic units, while others have independent promoters. In terms of genomic localization, miRNAs can be located within introns or exons of both coding and non-coding genes. Numerous miRNAs exist in multiple paralogous forms and are grouped into families based on their sequence similarity, particularly within their seed region [27]. This allows a single miRNA to target multiple mRNAs, and vice versa, establishing complex regulatory networks [29,30].

Mechanisms of miRNA Biogenesis: Canonical and Beyond

Biogenesis of miRNAs is a complex process that involves multiple steps from transcription in the nucleus to formation of mature miRNA in the cytoplasm [31]. Canonical miRNA biogenesis begins in the nucleus, where primary miRNA transcripts (pri-miRNAs) fold into stem-loop structures. These are processed by the Drosha-DGCR8 complex and form precursor miRNAs (pre-miRNAs), each with a length of approximately 70 nucleotides and a characteristic 2-nucleotide 3′ overhang. Subsequently, pre-miRNAs are exported to the cytoplasm via Exportin 5 (XPO5) in a Ran/GTP-dependent manner [32,33]. In the cytoplasm, Dicer cleaves pre-miRNA, producing a double-stranded RNA duplex consisting of the mature miRNA and its complementary strand, known as passenger strand [34]. The duplex is then unwound, and the mature miRNA is loaded into the RNA-induced silencing complex (RISC) by Argonaute (AGO) protein, while the passenger strand is degraded. Finally, mature miRNA directs RISC to its complementary sequences, most often in the 3′ untranslated regions (UTRs) of target mRNAs, to mediate translational repression or mRNA degradation (Figure 1) [35,36]. However, miRNA binding sites have also been identified in 5′ UTRs and even within the coding regions of mRNAs, even though less frequently [37,38].
In addition to the canonical pathway, several non-canonical miRNA biogenesis mechanisms have been characterized. These alternative pathways are classified as either Drosha/DGCR8-independent or Dicer-independent pathways. In Drosha/DGCR8-independent processing, the generated pre-miRNAs structurally resemble Dicer substrates, but bypass Drosha cleavage. A well-known example is mitrons, which are produced from the intronic sequences of mRNAs during splicing. Another example involves 7-methylguanosine (m7G)-capped pre-miRNAs, which are exported to the cytoplasm through exportin 1 without the need for Drosha-mediated cleavage [39]. In contrast, in Dicer-independent pathways, the initial processing step is carried out by the Drosha complex from endogenous short hairpin RNA (shRNA) precursors. The final maturation step takes place in the cytoplasm, where the Argonaute 2 (AGO2) cleaves the precursor to form the functional mature miRNA [40,41].

2.2. Linear lncRNAs: New Players in Regulation of Gene Expression

Linear lncRNAs, simply known as lncRNAs, comprise a diverse class of RNA molecules longer than 200 nucleotides that are not translated into functional proteins [42,43]. Although initially unrecognized, the introduction of high-throughput sequencing technologies, which enabled the massively parallel sequencing of nucleic acids, revealed the existence of additional genes that are transcribed into RNA and function as significant regulators of various biological processes [2], including gene expression regulation, chromatin remodeling, and cellular differentiation (Table 1) [44].
These ncRNAs exhibit several features similar to mRNAs in terms of both transcriptional and post-transcriptional regulation [45]. More precisely, lncRNAs are primarily transcribed by RNA polymerase II—similar to mRNAs (Figure 1)—and undergo post-transcriptional modifications such as 5′ capping, splicing, and polyadenylation. LncRNAs frequently comprise multiple exons and are subject to alternative splicing, resulting in the generation of multiple transcript isoforms from a single gene locus [46]. The structure of their corresponding genes exhibits considerable variability. Additionally, lncRNA promoters are frequently unique and can be associated with specific transcription factors, contributing to their tissue-specific expression patterns. However, unlike mRNAs, lncRNAs typically lack significant open reading frames (ORFs) and are not translated into functional proteins, while their abundance in cells is low [47]. Moreover, the majority of these regulatory RNAs contain fewer exons than mature mRNAs, and their length tends to be longer.
LncRNAs are integral components of the cellular regulatory network, controlling gene expression at multiple levels, functioning as decoys, scaffolds or guides (Figure 1) [47]. Their diverse structures and functions underscore their importance in maintaining cellular homeostasis and responding to environmental signals. Understanding the roles of lncRNAs in various biological contexts is essential for unraveling the complexities of gene regulation and cellular function.

Mapping lncRNAs: A Classification Based on Genomic Loci

Based on their genomic location relative to neighboring protein-coding genes, lncRNAs are classified into sense and antisense, intergenic, intronic, and bidirectional, each defined by its orientation and positioning relative to coding sequences [48]. More precisely, sense or antisense lncRNAs locate within or overlap with the exons of the associated protein-coding gene on the same, or opposite strand, respectively, with an antisense lncRNA transcribed in the opposite direction of the protein-coding gene [49]. Intergenic lncRNAs are located between protein-coding genes and they are transcribed from intergenic regions, while intronic lncRNAs originate from the introns of protein-coding genes and may be co-expressed with their host genes. Lastly, bidirectional lncRNAs are transcribed from promoter regions in close proximity to protein-coding genes but in the opposite direction, typically within 1000 base pairs, and may share regulatory elements with their coding neighboring gene [50].
This classification reflects the structural complexity and heterogeneity of this class, highlighting the diverse mechanisms through which they regulate gene expression. Their genomic position relative to protein-coding genes is often indicative of their potential functional interactions, including transcriptional regulation, RNA processing, and epigenetic modification [44]. Understanding the genomic organization of lncRNAs provides a fundamental framework for investigating their roles in both normal physiology and diseases.

2.3. circRNAs: RNA Loops with Regulatory Functions

circRNAs were identified in the early 1990s but were initially dismissed as splicing artifacts. However, interest in these molecules grew after studies revealed their stable and abundant expression across various tissues, suggesting they may play important regulatory roles [51]. One of the key findings proceed from the experiments conducted by Nigro et al., is that the exons of the studied gene were found to be joined in a non-linear order—specifically, the 3′ end of a downstream exon spliced back to the 5′ end of an upstream exon. This phenomenon, which is termed as back-splicing, leads to the formation of closed RNA loops known as circRNAs (Figure 1) [52]. In addition to exonic circRNAs, circular transcripts can also be generated through other mechanisms, including template switching during reverse transcription, tandem gene duplication, and RNA trans-splicing. The generation of circRNAs can be explained by two main mechanisms, direct and indirect back-splicing. More precisely, direct back-splicing occurs when a downstream splice donor directly connects with an upstream splice acceptor, causing the intervening RNA segment to loop back on itself and form a circle. On the other hand, indirect back-splicing involves a process called exon skipping. During this process, a lariat structure that includes exons is first formed, and this lariat subsequently undergoes back-splicing to produce a circular RNA molecule [53].
circRNAs are evolutionarily conserved and expressed abundantly in various human tissues [54]. They serve as scaffolds or decoys for other RNAs and RNA-binding proteins, thus influencing gene transcription and translation [51]. A well-characterized function of many circRNAs is their role as miRNA sponges or competing endogenous RNAs (ceRNAs). Specifically, circRNAs can bind miRNAs through complementary miRNA response elements (MREs), preventing their interaction with target mRNAs and leading to increased levels of the latter [55]. Similarly, lncRNAs derived from pseudogenes can also function as ceRNAs. For example, PTENP1, a pseudogene of the tumor suppressor gene PTEN, harbors MREs and modulates gene expression by sponging miRNAs (Table 1) [56]. The interplay between ceRNAs, miRNAs, and their targets create complex regulatory networks that fine-tune gene expression during developmental processes, stress responses, and other cellular functions.

2.4. piRNAs: Regulators in Germline Cells and Beyond

piRNAs are a class of small single-stranded RNAs, typically 21–35 nucleotides (nt) long, uniquely characterized by a 2′-O-methylation at their 3′ ends. Unlike miRNAs, piRNAs are generated through a Dicer-independent pathway from long single-stranded precursor transcripts, which are transcribed from distinct genomic loci known as piRNA clusters [57]. piRNAs are abundantly expressed in spermatogenic cells, where they bind to PIWI proteins—a subfamily of the Argonaute family—to suppress transposable elements (TEs) and maintain genome integrity (Table 1) [1]. Although firstly associated with germline cells, recent studies have revealed their presence in somatic cells, where they participate in the regulation of gene expression through transcriptional silencing, translational control, and modulation of mRNA stability [58,59].

2.4.1. piRNA Biogenesis: Primary Pathway and Ping-Pong Amplification Cycle

piRNA biogenesis occurs via two main pathways, the primary processing pathway and the Ping-Pong amplification cycle. After nuclear transcription, precursors are exported to the cytoplasm, adhered, modified, and loaded onto PIWI proteins to form mature piRNA-PIWI complexes [60]. The primary piRNA processing pathway generates sense and antisense piRNAs from transcripts produced from the transcription of piRNA clusters [61]. Once exported to the cytoplasm, RNA precursors are cleaved by the endonuclease Zucchini (Zuc) in Yb bodies, forming piRNA-PIWI complexes. The resulting piRNAs are further processed: their 3′ ends are trimmed, and the 2′-O-methylation is added by the methyltransferase HEN1, producing mature piRNAs. Then, these molecules are transported back to the nucleus, where they induce epigenetic silencing of complementary sequences [59,62].
The ping-pong pathway serves as a secondary mechanism that amplifies piRNA populations through reciprocal cleavage of complementary RNAs [63]. In this process, an initial piRNA bound to a PIWI protein recognizes and cleaves a complementary target RNA, producing a 5′-monophosphorylated fragment known as a pre-pre-piRNA, which is subsequently loaded onto a second PIWI protein. An additional cleavage step—dependent or independent of Zuc- forms a responder pre-piRNA, which is further trimmed and methylated to become a mature piRNA. Importantly, each initiator–responder pair shares a characteristic 10-nt 5′ overlap, a molecular hallmark of the Ping-Pong cycle. The 3′ fragment serves as a new pre-pre-piRNA, continuing the cycle and producing trailing piRNAs downstream of the original cleavage site. Concluding, the ping-pong cycle serves as a self-reinforcing loop that amplifies piRNA populations and enhances their ability to suppress transposable elements and maintain genomic stability [64].

2.4.2. snoRNAs: RNA Modifiers and miRNA-like Regulators

snoRNAs, having a length of 60–300 nt [65], are responsible for the chemical modification of other RNA molecules, particularly rRNAs and snRNAs, a process that is essential for their proper function [66]. Their name originates from their presence in the nucleolus, but they can also be found in other cellular compartments. These RNAs play crucial roles in RNA maturation and processing, including ribosome biogenesis, alternative splicing, as well as in the maintenance of genomic stability (Table 1). snoRNAs contain highly conserved structural motifs that classify them into three groups: (a) C/D box snoRNAs, (b) H/ACA snoRNAs, and (c) small Cajal body-specific RNAs (scaRNAs) [67].
More precisely, C/D box snoRNAs guide 2′-O-methylation, while H/ACA snoRNAs direct pseudouridylation of rRNA or other RNAs. Both modifications are essential for proper ribosome function [66]. In contrast, scaRNAs also follow C/D-H/ACA classification, but some of them contain both C/D and H/ACA structures, constituting an undivided snoRNA subcategory [68]. These RNAs interact with distinct proteins to form small ribonucleoprotein complexes, which take part in crucial biological processes. In addition to their primary function, snoRNAs have been recognized as precursors for smaller RNA fragments with regulatory functions. Some of these snoRNA-derived fragments act like miRNAs, contributing to post-transcriptional gene regulation [66]. For example, it has been shown that snoRNA ACA45 is processed by DICER into small molecules that resemble miRNAs. Unlike the canonical miRNA biosynthesis pathway, this processing bypasses the Drosha enzyme, which is typically required for miRNA maturation [17].

2.5. Other Classes of ncRNAs as Emerging Players in Gene Regulation

The analysis of diverse datasets obtained from small RNA-seq revealed the presence of short RNA fragments originating from larger RNA molecules, similar to miRNAs. The identification of these small RNA fragments is generally attributed to the high sensitivity of deep sequencing systems, which can detect low-abundance RNAs, including products of degradation. Interestingly, previous studies have suggested that these fragments may still have regulatory functions. Following this suggestion, Li et al. provided evidence that supported the potential functionality of these fragments, demonstrating that sequences with a length of ~20 nt derived from tRNAs and rRNAs but not from mRNAs, are produced via specific cleavage events at the 3′ or 5′ ends of the corresponding molecules [69].

2.5.1. tRFs: tRNA-Derived Fragments with Regulatory Functions

tRNA-derived fragments represent a distinct class of small ncRNAs generated through precise cleavage of mature or precursor tRNAs [20]. These fragments, typically 13–30 nt in length, carry a 5′-phosphate group and a 3′-hydroxyl group, and predominantly map to the 5′ or 3′ ends of tRNAs, suggesting a regulated biogenesis rather than random degradation. Based on their origin within the tRNA structure, tRFs are generally categorized into three main types: tRF-3 fragments arise from the TψC loop of mature tRNAs and retain the characteristic 3′ CCA tail, tRF-5 fragments that derive from the 5′ end and typically include the D-loop region, and tRF-1 fragments that are produced from precursor tRNAs and are distinguished by a 3′ poly-U sequence [20,70].
These molecules are evolutionarily highly conserved and have been implicated in various regulatory processes. Functionally, tRFs act similarly to miRNAs by forming stable complexes with Argonaute proteins and participating in RNA interference (RNAi)-induced gene silencing. Furthermore, they can regulate translation by interacting with translation initiation factors such as eIF4G/eIF4A or by binding to the small ribosomal subunit, thereby inhibiting translation initiation phase [70]. A related subclass, tRNA halves, which are longer fragments of 30–40 nt, are generated under stress conditions by enzymes like angiogenin. These molecules are known as stress-induced tRNA fragments (tiRNAs or tsRNAs) and play important roles in translation regulation during cellular stress responses (Table 1) [71].

2.5.2. rRFs: Regulators of Vital Cellular Processes

Although initially dismissed as degradation products, rRNA-derived RNA fragments gained recognition after Lee et al. identified a novel rRNA-derived small RNA (qiRNA) in the fungus Neurospora crassa that was produced in response to DNA damage. These qiRNAs bind to the Argonaute protein QDE-2 and mediate gene silencing through an RNAi-like mechanism, suggesting a role in cellular respond to stress (Table 1) [72]. Subsequent studies identified additional rRFs originating from various rRNA subunits (18S, 5.8S, 28S) across species, establishing rRFs as a new class of small ncRNAs [73]. These fragments, often originating from the 3′ ends of rRNAs, are evolutionary conserved and have been found to be upregulated under stress conditions. Like tRFs, rRFs can interact with Argonaute proteins and participate in the formation of ribonucleoprotein complexes [74]. Some rRFs also act as precursors to miRNAs or share sequence similarity with piRNAs, further supporting their involvement in gene regulation and silencing [73].

3. Functional Roles of ncRNAs in Normal Physiology and Their Involvement in Disease Pathogenesis

ncRNAs have been recognized not only as important regulators of cellular homeostasis, but also as key players in the development of diseases [75,76,77,78]. In normal cells, ncRNAs actively participate in vital biological processes such as cell differentiation, proliferation, apoptosis, DNA repair, metabolism, and cell cycle control, via regulating gene expression at both transcriptional and post-transcriptional levels (Table 1) [75,79,80]. Through mechanisms involving chromatin remodeling, RNA splicing, mRNA stability, and translational regulation, ncRNAs contribute to the maintenance of the tightly regulated gene expression landscapes necessary for normal cellular function [81,82,83]. Nonetheless, the same properties that render ncRNAs indispensable for normal cellular processes, also make them susceptible to dysregulation in pathological conditions. Aberrant expression or alterations in expression levels of certain ncRNAs have been strongly associated with the development and progression of a wide spectrum of diseases, including—but not limited to—cancer, cardiovascular diseases, neurodegenerative conditions, and immune-related diseases (Table 2) [77,84,85,86]. Understanding the roles of ncRNAs in both normal physiology and disease is essential for exploiting their potential as biomarkers for diagnosis, prognosis and treatment monitoring, as well as targets for novel RNA-based therapeutic strategies.

3.1. ncRNAs as Key Regulators of Normal Cellular Processes

Like other types of RNAs, regulatory ncRNAs play essential roles in cells, extending well beyond passive transcriptional noise. They are critical for maintaining cellular homeostasis and orchestrating complex biological processes [87,88]. Through diverse mechanisms, ncRNAs are involved in every level of gene expression regulation, contributing to differentiation, apoptosis, proliferation, stress responses, and angiogenesis (Table 1) [82,89,90,91,92]. Among the most studied ncRNAs are miRNAs, lncRNAs, and circRNAs which participate in the regulation of multi-faceted signaling pathways and vital cellular processes. These molecules exhibit distinct expression patterns depending on cell type and developmental stage, highlighting their importance in maintaining cellular function [92]. This section discusses key physiological roles of ncRNAs, focusing on their involvement in gene regulation, lineage specification, immune responses, angiogenesis and developmental processes.

3.1.1. Regulation of Gene Expression

ncRNAs are central regulators of gene expression at both the transcriptional and post-transcriptional levels. Among them, lncRNAs can recruit chromatin-modifying complexes like histone methyltransferases and acetyltransferases, to specific genomic loci, altering chromatin structure and, thereby, modulating transcription (Figure 1) [42,87]. A well-known example is XIST, a lncRNA essential for X-chromosome inactivation, which recruits silencing complexes to X chromosome, resulting in its transcriptional repression [93]. Transcriptional regulation can also be mediated by circRNAs, which can interact with RNA polymerase II or act as enhancers through cis-regulatory interactions [93,94]. For example, previous studies have revealed that circEIF3J and circPAIP2 interact with the RNA polymerase II complex and U1 snRNPs in the nucleus, facilitating the recruitment of transcriptional activators to the promoters of their parental genes, thus enhancing transcription [82]. At the post-transcriptional level, the master regulators of gene expression are miRNAs. These molecules recognize their target mRNAs and bind to them due to sequence complementarity, leading to mRNA degradation or translational repression (Figure 1) [95,96]. The miRNA-mediated silencing offers cells the benefit of a speedy regulation of protein expression as a response to changes in environmental stimulants.

3.1.2. Roles of ncRNAs in Differentiation, Apoptosis, and Immune Responses

ncRNAs play significant roles in cell fate decisions, including differentiation and apoptosis. For instance, during hematopoiesis, miRNAs miR-223 and miR-150 are critical for directing progenitor cells toward granulocytic and lymphoid lineages, respectively [97,98]. lncRNAs are also involved to lineage specification via the regulation of transcription factor activity or chromatin remodeling [99]. Moreover, within the immune system, ncRNAs regulate the activation, proliferation, and function of immune cells. Specifically, miR-155 enhances T-cell development and activation, and cytokine production [100,101], while other ncRNAs control B-cell development and antigen presentation [102]. Additionally, lncRNAs NEAT1 and MALAT1 have been found to regulate the expression of inflammatory genes in macrophages, controlling both innate and adaptive immune responses [103,104]. Abnormal expression of ncRNAs is associated with immune dysregulation and chronic inflammation [105,106].

3.1.3. ncRNAs in Embryogenesis, Neurogenesis, and Angiogenesis

Precise temporal and spatial expression of ncRNAs are essential for proper embryonic tissue patterning, axis development, and organogenesis. Many ncRNAs exhibit stage- and tissue-specific expression patterns, supporting the dynamic regulation of gene networks during embryogenesis [107]. In neurogenesis, previous studies have reported that miR-124 and miR-9 promote neuronal differentiation, migration, and synaptic plasticity by repressing non-neuronal gene expression and stabilizing neuron-specific transcriptional profiles [108,109]. Similarly, lncRNAs regulate neurodevelopmental processes, since they are critical for brain development, organization and function [110]. Furthermore, ncRNAs play integral roles in angiogenesis, the formation of new blood vessels. Previous studies have reported that miR-126 enhances endothelial cell proliferation and maintains vascular integrity [111], while lncRNA MEG3 negatively regulates endothelial cell proliferation and angiogenesis in vascular endothelial cells [112].

3.2. ncRNA-Mediated Mechanisms in Disease Development

ncRNA are now emerging as key molecules in the development and progression of numerous diseases (Table 2) [5,113]. Aberrant ncRNA expression patterns have been linked to the disruption of gene regulatory networks, contributing to cellular dysfunction and, thus, leading to pathological conditions [77,114,115,116]. Multiple classes of ncRNAs, including miRNAs, lncRNAs and circRNAs, have been implicated in the pathogenesis of various diseases, particularly cancer, as well as cardiovascular disorders, neurological conditions, autoimmune diseases, and infections [5,70]. In this section, we examine the implication of ncRNAs in disease mechanisms, discussing how their dysregulation contributes to molecular and cellular dysfunction.

3.2.1. Cancer: Oncogenic and Tumor Suppressive Roles of ncRNAs

ncRNAs, particularly miRNAs and lncRNAs, play significant roles in regulating gene expression, especially of genes involved in cancer development and progression. Depending on their targets, these RNAs can function either as oncogenes or tumor suppressors, promoting or inhibiting tumorigenesis, respectively [117,118]. Their dysregulation contributes to multiple hallmarks of cancer, including sustained proliferative signaling, resistance to apoptosis, angiogenesis, invasion, and metastasis [119,120,121,122,123,124].
Oncogenic ncRNAs typically display high expression levels in tumors and promote carcinogenesis by downregulating tumor suppressor genes. In contrast, tumor-suppressive ncRNAs are often silenced or downregulated in cancer, leading to the activation of oncogenic pathways [117,125].
Regarding lncRNAs, HOTAIR exemplifies how ncRNAs can promote tumor progression through epigenetic regulation. In more detail, HOTAIR interacts with chromatin-remodeling complexes like PRC2 in order to alter histone methylation and repress gene expression, thereby facilitating metastasis and resulting in poor clinical outcomes [126]. Similarly, MALAT1 is another oncogenic lncRNA that regulates alternative splicing and epigenetic modifications, promoting tumor cell migration and metastasis across multiple cancer types [127,128].

3.2.2. ncRNAs as Emerging Drivers in Cardiovascular Diseases

ncRNAs have recently emerged as important regulators in the pathogenesis of cardiovascular diseases, including cardiac hypertrophy, atherosclerosis, and myocardial infarction (Table 2) [129,130]. Specific miRNAs like miR-133 and miR-1, are critical molecules in regulating cardiomyocyte hypertrophy and contractility [131], whereas lncRNAs like HOTAIR, ANRIL and MeXis contribute to the regulation of vascular inflammation, endothelial function, and lipid metabolism [129]. Since ncRNAs target genes that are involved in cellular signaling and structural integrity, their dysregulated expression is linked to pathological cardiac remodeling, structural changes, and post-infarction fibrosis. Due to their tissue-specific expression patterns and detectability in circulation, ncRNAs are being actively investigated as potential biomarkers for early diagnosis, risk stratification, and therapeutic targeting in preclinical models of cardiovascular diseases [5,132].

3.2.3. ncRNAs in Neurodegenerative Disease Pathogenesis

The non-coding RNA molecules are becoming recognized as critical regulators of neuronal survival, synaptic plasticity, and neuroinflammation in neurodegenerative diseases like Alzheimer’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis (ALS) (Table 2) [133,134]. Key pathological mechanisms, such as aberrant protein aggregation, oxidative stress, impaired autophagy, and neuronal apoptosis, have been associated with altered expression patterns of particular miRNAs and lncRNAs [133,135]. For instance, by controlling the expression of BACE1, a crucial enzyme in the synthesis of β-amyloid (Aβ), miR-29 is implicated in aberrant Aβ metabolism and is markedly downregulated in Alzheimer’s disease [136]. According to Modarresi et al., lncRNA BACE1-AS also stabilizes BACE1 mRNA, increasing its expression levels and contributing to Aβ accumulation and plaque formation [137]. These ncRNAs appear to contribute to the molecular signatures of neurodegenerative conditions and may represent new therapeutic targets for enhancing neuronal resilience and improving patients’ outcomes.

3.2.4. The Implication of ncRNAs in Autoimmune and Infectious Diseases

This class of RNAs regulate host–pathogen interactions in both autoimmune and infectious diseases. Abnormal expression of miRNAs and lncRNAs alters immune cell activation, differentiation, and cytokine production, thereby leading to the development of disorders like systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), and chronic viral infections. For example, higher levels of miR-155 and lower levels of miR-146a are frequently observed in inflamed tissues compared to normal and are correlated with disease activity in autoimmune conditions. These miRNA control inflammatory signaling pathways including NF-κB and interferon responses, which are central to both immune regulation and autoimmunity [138].
On the other hand, several viruses produce their own viral ncRNAs to manipulate host defenses. For example, Epstein–Barr virus (EBV) produces multiple viral miRNAs (e.g., BART miRNAs) that target host immune genes to suppress antiviral responses and promote viral latency [139]. Similarly, Kaposi’s sarcoma-associated herpesvirus (KSHV) expresses PAN RNA, a lncRNA that is required for late KSHV gene expression, supporting lytic replication [140]. In addition to the detection of viral ncRNAs in human tissues or fluids, DNA methylation pattern of genes encoding endogenous miRNAs has emerged as a valuable indicator of infectious disease progression. For example, hypermethylation of hsa-miR-124-2 has been frequently observed in cervical lesions linked to high-risk human papillomavirus (HPV) genotypes, including types 16, 18, and 59. These findings suggest that the methylation status of specific miRNA genes can serve as an early biomarker in the detection and follow-up of HPV-related lesions, potentially preventing their progression to cervical cancer [141].

4. ncRNAs as Biomarkers: A Molecular Toolkit for Disease Insight

A biomarker is described as “a defined specific that’s measured as an indicator of normal biological processes, pathogenic processes or responses to an exposure or intervention” [142,143]. According to Biomarkers, Endpoints, and other Tools (BEST) framework, biomarkers can be broadly divided into seven main categories: (i) diagnostic, (ii) monitoring, (iii) pharmacodynamic or response, (iv) prognostic, (v) safety, (vi) susceptibility, and (vii) risk [142]. Biomarkers play a critical role in modern medicine, offering tools for early disease detection, prognosis assessment, treatment response monitoring, and personalized therapeutic strategies. Among the emerging molecular candidates, ncRNAs, including miRNAs, exosomal RNAs (exRNAs), circRNAs, piRNAs, and lncRNAs, have been recognized as essential molecules in cellular functions with strong associations to cancer development and progression (Table 2). Their abnormal expression profiles provide valuable insights that support early detection, prognosis evaluation, and the design of personalized treatment approaches across various diseases, particularly in oncology [65].
Up to date, every type of tumor studied through miRNA profiling has exhibited significantly altered miRNA expression patterns compared to normal cells from the same tissue [144]. As a result, there is growing interest in investigating the potential of ncRNAs in distinguishing benign and malignant tumors, as well as accurately differentiating cancer stages with greater sensitivity and specificity. In this section, we present the major categories of ncRNAs and discuss key findings that support their utility as biomarkers, highlighting their potential roles as diagnostic and prognostic markers in cancer and other diseases with significant public health impact (Table 3).

4.1. miRNAs as Biomarkers: Small RNAs with Big Impact in the Development of Diseases

As the most extensively studied ncRNAs, miRNAs have been identified as key regulatory molecules with well-established roles in a variety of diseases, especially in cancer. Among them, miR-21 and miR-155 are two of the most widely investigated miRNAs that are associated not only with malignancies but also with cardiovascular diseases, diabetes mellitus, and neurodegenerative disorders [145]. These miRNAs are classified as oncogenic miRNAs, or “oncomiRs”, since they are frequently found to be overexpressed across multiple cancer types. More precisely, miR-21 promotes tumorigenesis by targeting and inhibiting key tumor suppressors like PTEN, TPM1, and PDCD4 [146,147,148]. This suppression facilitates the activation of oncogenic signaling pathways, including Ras/MEK/ERK and PI3K/Akt/mTOR, thereby promoting cancer cell proliferation, survival, and metastasis (Table 1) [149,150,151]. Similarly, miR-155 promotes cancinogenesis by driving abnormal B cell growth [152], while it plays a crucial role in bone cancer development by regulating cell cycle control, apoptosis, irruption, metastasis, and hormone receptor signaling [153,154]. Recent findings have also underscored the role of miRNAs in oncogenesis, particularly in hepatocellular carcinoma (HCC). A recent study investigating miRNA-mediated mRNA regulatory networks in liver cancer cells identified 300 differentially expressed miRNAs, five of which were significantly associated with overall survival and could serve as independent prognostic markers. Notably, miR-5003-3p was shown to directly suppress the expression of ABAT, BHMT, and SHMT1—genes positively associated with favorable prognosis. Functional enrichment analyses revealed that these targets are involved in butyric acid and amino acid metabolism, suggesting that miR-5003-3p contributes to liver cancer progression by disrupting key metabolic pathways. These findings highlight the potential of miRNAs not only as diagnostic and prognostic biomarkers but also as therapeutic targets, offering novel insights into the molecular mechanisms of HCC [155].
Beyond oncology, miRNAs are increasingly recognized for their diagnostic and prognostic utility in other diseases as well. A recent study investigated urinary miRNA expression profiles in pre-eclampsia (PE), a severe pregnancy complication contributing significantly to maternal and fetal morbidity. Using urine samples collected in the first and second trimesters, researchers identified several differentially expressed miRNAs in women who later developed PE. Notably, miR-184, miR-203a-3p, miR-205-5p, and miR-223-3p were consistently downregulated, while miR-1-3p and miR-206 were upregulated during disease progression. These findings support the potential of urinary miRNAs as non-invasive early biomarkers for PE and highlight the dynamic nature of miRNA expression during pregnancy [156].
Another important group of miRNAs, known as myomiRs and including miR-1, miR-133a, miR-133b, miR-206, miR-208a, miR-208b, miR-499a, and miR-499b, are highly expressed in skeletal and cardiac tissues and are involved in essential biological processes like cellular proliferation, differentiation, and metabolism [131,157,158,159]. Notably, aberrant expression of miR-133b and miR-206 has been observed in patients with cardiovascular diseases, offering a more thorough and conclusive evaluation of the potential clinical value of myomiRs as biomarkers for cardiovascular diseases [132].

4.2. lncRNAs as Biomarkers: Unveiling Their Diagnostic and Prognostic Potential

Several cancer-associated lncRNAs which act as either tumor promoters or suppressors, exhibit higher levels in the serum and plasma of cancer patients compared to healthy individuals, highlighting their potential utility as biomarkers. MALAT1, HOTAIR, NORAD and PVT1 are tumor-promoting lncRNAs that may serve as diagnostic biomarkers [160]. Studies assessing the expression levels of MALAT1 in peripheral blood from both non-small cell lung cancer (NSCLC) patients and healthy individuals demonstrated that this lncRNA is upregulated in NSCLC cases, indicating its potential utility as a biomarker for detecting the disease at early stages. Furthermore, combining MALAT1 with established markers such as carcinoembryonic antigen (CEA) has been suggested to enhance the overall diagnostic accuracy in NSCLC [161].
Conversely, the tumor-suppressor lncRNAs LED, MEG3, GUARDIN, NEAT1, Linc-p21, DINO, and PTENP1 interact with protein p53 to suppress cancer growth [160]. Based on previous studies, the upregulation of NEAT1 correlates with worse 5-year overall survival in patients with renal cell carcinoma (RCC), suggesting that NEAT1 may serve as a predictive biomarker for RCC [162]. Although further research is needed to validate the diagnostic or predictive value of these molecules in clinical practice, lncRNAs hold great promise as liquid biopsy markers.
Recent studies have underscored the widespread disruption of transcription factor–lncRNA networks in cancer, highlighting the involvement of numerous lncRNAs in key regulatory pathways that drive tumorigenesis. The myogenic regulator MyoD1 that functions as transcription factor, is significantly downregulated in gastric cancer. Overexpression of MyoD1 suppresses cancer cell proliferation, partly by altering the expression of 47 lncRNAs involved in key pathways. The differential expression of lncRNAs under MyoD1 control suggests a broader transcriptional regulatory role, positioning MyoD1–lncRNA networks as key modulators of gastric cancer progression and promising candidates for early gastric cancer detection and therapeutic targeting [114].

4.3. circRNAs as Biomarkers: Novel Stable Players in Tumor Detection

To date, numerous circRNAs have emerged as promising non-invasive biomarkers for cancer. Due to their circular structure, circRNAs exhibit exceptional stability and enhanced resistance to degradation by nucleases, making them particularly attractive and promising candidates compared to other ncRNAs [163]. A prominent case is circHIPK3 which exhibit aberrant expression in bladder [164], breast [165], colorectal [166], and prostate cancer [167], highlighting the broad pattern of circRNAs dysregulation in tumors.
Beyond their diagnostic potential, recent studies have also highlighted the functional relevance of circRNAs in cancer progression. More precisely, they demonstrated that hsa_circ_0079875 is significantly upregulated in HCC, where it promotes tumor cell proliferation, invasion, and migration, while inhibiting apoptosis. Mechanistically, it acts as a ceRNA by sponging miR-519d-5p and upregulating NRAS, thereby facilitating HCC progression. Given its association with larger tumor size, microvascular invasion, and poor prognosis, hsa_circ_0079875 may serve as both a prognostic biomarker and a potential therapeutic target for the treatment of HCC [168]. Similarly, in esophageal squamous cell carcinoma (ESCC), circ_0050444 has been identified as significantly downregulated in patient tissues and cell lines. Although its functional role was previously unclear, recent studies have demonstrated that circ_0050444 acts as a tumor-suppressive circRNA by inhibiting ESCC cell proliferation, migration, and invasion. More precisely, circ_0050444 functions by sponging miR-486-3p, thereby inhibiting its repressive effects on downstream targets. This interaction highlights the complex regulatory crosstalk between circRNAs and miRNAs and underscores the crucial role of ncRNA networks in cancer pathogenesis [169].

4.4. piRNAs as Biomarkers: Emerging Indicators of Cardiovascular Diseases and Cancer

Recent studies have highlighted the potential of piRNAs as emerging diagnostic biomarkers for myocardial infarction. Analyses of serum samples from patients with myocardial infarction as well as healthy individuals have revealed distinct piRNA expression patterns between the two groups, suggesting that these molecules could serve as novel diagnostic markers. More precisely, piR_2106027 was found significantly upregulated in myocardial infarction patients and correlated with the release of cardiac troponin I, a key marker of myocardial injury. Additionally, piR-hsa-9010, piR-hsa-28646, and piR-hsa-23619 were also overexpressed, suggesting they might be valuable biomarkers and therapeutic targets for acute myocardial infarction [64].
Besides cardiovascular diseases, piRNAs can be exploited as biomarkers in cancer diagnosis, prognosis and treatment [60]. For example, the high levels of piR-1245 in gastric juice are associated with gastric cancer, supporting the utility of the corresponding piRNA as a non-invasive biomarker for both cancer detection and prognosis [170]. Additionally, the well-studied piR-651 has shown diagnostic potential across multiple cancer types [171], while piR-54265 has been proposed as an early-stage diagnostic biomarker for colorectal cancer in the general population [172].

4.5. snoRNAs as Biomarkers: Deciphering Their Diagnostic Value in Cancer

Dysregulation of snoRNAs has been closely linked to cancer progression, highlighting their potential utility as diagnostic biomarkers. However, this promising direction requires further investigation and validation in large-scale clinical studies. SNORA42 is a promising snoRNA, reported to be significantly upregulated in ESCC cell lines, tumor tissues, and serum samples from ESCC patients [173]. Similarly, SNORD78 demonstrates increased expression levels in NSCLC tumors compared to adjacent normal tissues [174]. Significantly, a recent study reports that SNORD15A, SNORD35B, and SNORD60 were all upregulated in both the tissues and urine sediments of RCC patients, suggesting their potential utility as novel diagnostic biomarkers for this malignancy [175].

4.6. tRFs and rRFs as Biomarkers: Small Fragments with Great Potential in Diagnosis

tRFs have been shown to be involved in many aspects of oncogenesis, including cancer cell proliferation, angiogenesis, metastasis, and metabolism [176], with various studies indicating that tRFs isolated from serum can serve as valuable diagnostic biomarkers. For example, high levels of tRF-Pro-AGG-004 and tRF-Leu-CAG-002 have been identified in patients with pancreatic cancer [177], while tRF-Gln-TTG-006 is upregulated in the mitochondrial fraction of patients’ serum suffering from HCC, with strong evidence supporting its potential use in distinguishing early-stage HCC cases from healthy individuals [178].
rRFs, a recently identified class of small RNAs, are gaining recognition for their roles in normal physiology and diseases, including cancer [179]. A recent clinical study further underscores their potential as prognostic indicators, showing that elevated 28S rRF levels in prostate cancer patients are strongly associated with a greater risk of disease recurrence after treatment [180].

4.7. Circulating ncRNAs and Liquid Biopsy

In recent years, liquid biopsy has emerged as an innovative, non-invasive approach for sample collection and analysis [181]. More precisely, the term “liquid biopsy” describes the collection and examination of non-solid biological fluids such as blood, saliva, and urine, for various clinical applications, including disease diagnosis, treatment monitoring, and post-therapy follow- up. Unlike traditional tissue biopsies which are often invasive and limited in accessibility, liquid biopsies offer a non-invasive (e.g., saliva, urine, stool) or minimally invasive (e.g., blood samples) way to estimate the status of internal organs, even in challenging-to-reach body parts [8,182]. The growing interest in this type of biopsy stems from its capacity to detect and analyze a wide range of biomarkers, like circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), and miRNAs which are encapsulated in exosomes that are present in blood, plasma, serum, or sputum [181]. The expression profiles of exosome-derived ncRNAs can effectively distinguish cancer patients from healthy individuals, since they have demonstrated high sensitivity in differentiating between malignant and benign conditions, highlighting their potential use as tools for early cancer detection, disease prognosis and treatment [183].

4.8. Enhanced Stability, Sensitivity and Specificity of ncRNAs Upgrades Liquid Biopsy

Traditional biomarkers often demonstrate significant limitations such as low sensitivity, specificity and accuracy, a fact that hinders the effective distinction of early-stage cancers from other pathological conditions. Chen et al. first highlighted the potential of circulating ncRNAs as non-invasive diagnostic biomarkers by identifying a group of stable and easily detectable miRNAs in patients’ serum [183]. Shortly after, Lawrie et al. underscored the significance of exosomal miRNAs in diffuse large B-cell lymphoma, reporting higher levels of miR-155, miR-210, and miR-21 in patient samples, with increased miR-21 expression being associated with worse prognosis [184].
Unlike traditional biomarkers that typically rely on proteins or DNA mutations, ncRNAs can provide a more dynamic view of disease progression. Circulating and exosomal ncRNAs, are typically associated with protein complexes like AGO2 [185], and high-density lipoprotein (HDL) [186]. This mode of packaging plays a crucial role in safeguarding ncRNAs from degradation by RNases, enzymes that are abundant in body fluids, and also ensures their stability during collection, storage, and repeated freeze–thaw cycles [161,187]. In addition to their stability, ncRNAs demonstrate higher sensitivity compared to protein biomarkers, primarily due to the PCR-based detection techniques, which rely on the amplification of the examined molecules [161]. Several studies have demonstrated the superior performance of ncRNAs over traditional protein biomarkers, while combining circulating ncRNAs with conventional tumor markers significantly improve diagnostic sensitivity at early disease stages [163,188,189,190]. For instance, the combination of serum-derived exosomal miR-17-5p with tumor markers CEA, CYFRA21-1, and SCCA achieved a sensitivity of 76.4% and specificity of 76.6% for diagnosing NSCLC. In contrast, the individual sensitivities of CEA, CYFRA21-1, and SCCA were only 26.4%, 59.7%, and 26.4%, respectively, underscoring the significance of integrating exosomal miRNAs to improve diagnostic performance [191]. Similarly, exosomal lncRNA RP5-977B1 has shown strong diagnostic potential in distinguishing NSCLC patients from healthy individuals, outperforming the conventional marker CEA. In early-stage NSCLC (stages I and II), RP5-977B1 achieved an area under the curve (AUC) of 0.865, while its higher expression levels were correlated with poorer prognosis in patients with this malignancy [192].

4.9. Challenges in Translating ncRNA-Based Biomarkers in Clinical Practice

The utilization of ncRNAs in clinical settings as reliable biomarkers requires rigorous assessment through well-designed studies that ensure reproducibility, specificity, and sensitivity across diverse clinical settings [193]. Although several studies have demonstrated that ncRNAs can reliably distinguish diseased from healthy states across independent cohorts, most ncRNAs with potential as biomarkers remain in early research or clinical evaluation stages and have not yet achieved widespread clinical application. These RNA molecules are frequently associated with multiple conditions, serving as markers across various pathologies, which complicates their clinical use [187]. To overcome this limitation, diagnostic panels that combine multiple ncRNAs have been designed and developed, which offer greater specificity compared to single biomarkers. However, these panels are still less frequently validated and reproduced across independent studies [145]. Likewise, strong evidence supports that circRNA panels, like those of lncRNAs and miRNAs, provide better diagnostic performance than individual circRNAs [194]. Furthermore, newly identified classes of ncRNAs show tissue-specific expression patterns allowing for a more precise disease detection and progression. For example, piRNAs and circRNAs are predominantly expressed in cardiac tissues, and their potential in clinical use as diagnostic biomarkers should be further investigated. Given their tissue-specific expression patterns and stability, these molecules can offer improved diagnostic accuracy for cardiovascular diseases, especially in distinguishing them from other conditions more effectively [195,196]. However, accurately characterizing the expression profiles of these ncRNAs remains technically challenging due to the limitations in current standard RNA sequencing methodologies [195,196]. Additionally, the management of the enormous sequencing datasets that are generated is still a complex task.
To overcome these challenges, advanced bioinformatics tools and machine learning algorithms are increasingly developed and employed [15]. These approaches facilitate the handling of the large datasets, converting them into forms that are easier to interpret and analyze [197]. The integration of evolutionary supervised artificial intelligence (AI) learning methods plays a crucial role in achieving early cancer diagnosis and guiding treatment strategies, ultimately contributing to increased remission rates and improved patient survival. An example of this AI approach is CancerSig, which uses a bi-objective combinatorial genetic algorithm to identify miRNA signatures supporting early detection of various cancers [198].
Despite the previously discussed promising findings regarding the exploitation of ncRNAs as biomarkers, additional limitations are concerned for clinical use. One major challenge is the inter- and intra-method reproducibility of ncRNA assays, which remains inconsistent. It is essential to standardize detection methods and set specific cut-off points that will be configurated based on each medical condition [163]. Notably, the expression of ncRNAs may be affected by biological variables that are not included in the studies, such as aging, sex, medication, etc., thus such parameters must be carefully considered in study design to ensure biomarker robustness across populations [187,199].
An ideal ncRNA biomarker must be applicable across diverse patient populations and supported by accurate, cost-effective, and user-friendly detection methods with standardized protocols for sample collection, processing, and analysis [187]. Furthermore, beyond demonstrating clinical performance, regulatory approval processes require to follow strict guidelines from agencies like the Food and Drug Administrator (FDA) and European Medicines Agency (EMA), which evaluate clinical utility, safety, and reproducibility of the proposed assay [200]. In addition to regulatory evaluation, successful clinical translation of ncRNA biomarkers also depends on well-structured clinical trial designs. These should include appropriate patient stratification based on disease stage or molecular profile, clearly defined clinical endpoints such as progression-free survival or response to treatment, and long-term follow-up when needed. Moreover, multicenter validation and prospective cohort studies are essential to confirm the diagnostic or prognostic value of ncRNAs in diverse populations. Without such rigorous clinical evidence, regulatory approval and widespread clinical adoption remain unlikely. Collaborative frameworks that integrate clinicians, researchers, and regulatory bodies will be crucial for overcoming these translational barriers [86,197].
In summary, while ncRNAs exhibit a significant potential as biomarkers due to their enhanced sensitivity, specificity, and accessibility compared to traditional markers, their translation in clinical practice is limited because of technical, analytical and regulatory barriers.

5. ncRNAs as Therapeutic Targets: Approaches and Applications

Extensive research on ncRNAs, particularly their roles as vital regulators of gene expression, has led to the development of novel therapeutic strategies. The implication of ncRNAs in the molecular mechanisms of many diseases has led to the development of new ncRNA-based therapies. The idea of these therapeutic strategies is based on restoring the levels of dysregulated ncRNAs, thereby reactivating disrupted gene regulatory networks [201]. Among the different ncRNA classes, miRNAs are the most extensively studied and commonly used for the development of RNA-targeted therapeutic approaches. Their dysregulation in disease states can be therapeutically harnessed through two main strategies: miRNA replacement therapy, which involves the use of synthetic miRNA mimics to compensate for downregulated miRNAs, and miRNA inhibition, which uses antisense oligonucleotides to block the function of overexpressed miRNAs [202,203].
Although RNA-based therapies hold significant promise and represent a major advancement toward personalized medicine, several challenges must be addressed before they can be widely adopted in clinical practice. Key obstacles include achieving targeted delivery, ensuring molecular stability, and minimizing off-target effects [204]. This section presents the recent advances in ncRNA-based therapeutic strategies and their potential applications in disease treatment.

5.1. Therapeutic Modulation of ncRNAs: Tools and Mechanisms of RNA-Based Strategies

The rapid expansion of research into ncRNAs has revolutionized therapeutic development in molecular medicine. Modulating the expression and function of dysregulated miRNAs and lncRNAs offers a promising tool for the treatment of various pathological conditions, including cancer, viral infections, and cardiovascular diseases [201,205,206]. Although several RNA-based technologies have been introduced as well-characterized approaches for modulating gene expression at the RNA level, the most widely used are miRNA mimics and inhibitors, antisense oligonucleotides (ASOs), and small interfering RNAs (siRNAs).
miRNA mimics are chemically synthesized double-stranded RNAs designed to restore cellular levels of tumor-suppressive miRNAs that are downregulated in disease. By mimicking endogenous miRNAs, these synthetic molecules redress the repression of oncogenic target genes and normalize cellular function [202]. A prominent example is MRX34, a liposomal mimic of miR-34a, which entered clinical trials for evaluating its therapeutic potential in liver cancer and other solid tumors. Although the Phase I trial was terminated due to immune-related adverse events, it became proof of concept for miRNA mimic-based therapies [207]. Another promising candidate is Remlarsen (MRG-201), a synthetic mimic of miR-29, which was found to decrease the expression of pro-fibrotic genes, reducing fibrosis in animal models [208]. It has been evaluated in a Phase I trial for keloid scars and holds potential for treating idiopathic pulmonary fibrosis (IPF) and other fibrotic diseases [209]. On the contrary, miRNA inhibitors like antagomiRs or locked nucleic acid (LNA)-modified oligonucleotides, are engineered to silence oncogenic miRNAs that are upregulated. These inhibitors act by “sponging” the target miRNA, preventing its interaction with mRNAs and, thereby, restoring normal gene expression patterns [202]. A representative example is MRG-106 (cobomarsen), an LNA inhibitor of miR-155, that has been evaluated in clinical trials for the treatment of T-cell lymphomas and other hematological malignancies [202,210,211].
ASOs represent another strategy for RNA-targeted approaches. ASOs are short, single-stranded DNA or RNA synthetic molecules that bind to complementary RNA transcripts, including lncRNAs, to block their function or induce their degradation via RNase H-mediated cleavage [212]. Regarding lncRNAs, ASOs can suppress transcripts implicated in tumor progression or cancer-related signaling pathways. Previous studies have shown that ASO-mediated knockdown of MALAT1 can reduce metastatic capacity in lung cancer, making this lncRNA a suitable target for anti-metastatic therapy [211]. Other approaches are based on siRNAs to mediate degradation of the overexpressed RNAs. These are double-stranded RNA molecules that exploit the RNAi pathway to silence specific lncRNAs [213]. In detail, after their insertion into the cell, siRNAs are incorporated into the RISC, guiding it to the complementary lncRNA and leading to its degradation [214]. Based on a previous study, silencing lncRNA ANRIL via siRNA-based approach resulted in inhibition of cellular proliferation and increased apoptosis rate in gastric cancer, suggesting ANRIL as a potential therapeutic target for the development of effective treatments for this malignancy [215].
Collectively, these RNA-based tools offer precise, sequence-specific therapeutic strategies that can be adjusted to individual molecular profiles, advancing personalized medicine. Several of these approaches have advanced into early-phase clinical trials, highlighting their potential as therapeutic targets for future treatment strategies.

5.2. Targeted Delivery Systems for ncRNA-Based Therapies

One of the major challenges in developing ncRNA-based therapies is achieving efficient and targeted delivery to the target cells [216]. Naked RNA molecules are inherently unstable—since they degrade rapidly in biological fluids-, highly susceptible to enzymatic degradation, and may cause unintended off-target effects, necessitating the use of advanced and efficient delivery systems. To overcome these limitations, several advanced delivery platforms have been developed.
Among the carriers used for RNA delivery, the most extensively studied systems are lipid nanoparticles (LNPs), viral vectors, and extracellular vesicles, particularly exosomes [217,218,219,220,221]. In detail, LNPs are among the most widely used carriers for RNA therapeutics. They encapsulate RNA molecules in a protective lipid bilayer, improving stability and facilitating cellular uptake via endocytosis [222]. LNPs have been successfully used in approved siRNA and mRNA therapies [223,224,225], particularly for liver-targeted delivery due to their natural tropism for hepatocytes [226,227]. On the other hand, viral vectors, such as adenoviruses and adeno-associated viruses (AAVs), offer high transduction efficiency and long-lasting gene silencing or induced expression. Although they are effective in delivering molecules, their clinical application is limited by concerns over immunogenicity and manufacturing complexity [228]. Continuing, extracellular vesicles (EVs), particularly exosomes, are naturally secreted lipid-bound vesicles that mediate intercellular communication [229]. They can carry ncRNAs and deliver them to recipient cells with high biocompatibility and low immunogenicity. Their ability to cross biological barriers makes them a promising tool for targeting hard-to-reach tissues, though massive production and standardization remain challenging [228]. A recent study exemplified their therapeutic potential using human umbilical cord mesenchymal stem cell-derived exosomes (hucMSC-exosomes), naturally enriched with miR-132-3p, to treat myocardial ischemia–reperfusion injury (MIRI). These exosomes reduced cardiomyocyte apoptosis and improved myocardial energy metabolism by enhancing glucose uptake. Mechanistically, miR-132-3p downregulated PTEN, thereby activating the AKT pathway and promoting GLUT4 translocation. This study highlights the utility of stem cell-derived exosomes as effective, cell-free carriers of therapeutic ncRNAs capable of modulating disease-relevant molecular pathways in vivo [230].
Alongside biological carriers, chemically defined delivery systems have also demonstrated substantial promise. A well-established approach is the GalNAc (N-acetylgalactosamine) conjugation technology, designed to deliver siRNAs or ASOs directly to the liver. This system exploits the high expression of asialoglycoprotein receptors (ASGPRs) on hepatocytes and selectively delivers RNA molecules to them, minimizing off-target uptakes in non-hepatic tissues [231]. The technology has demonstrated high specificity and efficacy and has been incorporated into multiple FDA-approved RNA-based therapies, highlighting its clinical significance (Figure 2) [224,232].

5.3. Challenges in Clinical Implementation of ncRNA-Based Therapies

Although there has been considerable progress in the field, several critical limitations continue to hinder the establishment of ncRNA-based medical treatments [201,233]. One of the most critical barriers is targeting specificity. In particular, RNA therapeutics can result in unintended on-target effects in non-target cells, or off-target effects due to sequence similarity, mismatches, or excessive dosing [234]. These effects compromise both the efficacy and safety of a likely therapy. Intracellular delivery of RNA constructs also presents several limitations. Unmodified RNA molecules are inherently unstable in biological fluids, rapidly degraded by nucleases [235,236]. Even when delivered, RNAs often become trapped in endosomes, necessitating effective endosomal escape mechanisms to reach their cytoplasmic targets [237]. Furthermore, the absence of cell-type-specific delivery systems further complicates the selective release of the RNA load. Last but not least, immune activation, off-target toxicities, and the pleiotropic nature of numerous ncRNAs—many of which regulate multiple genes or pathways—make selective modulation challenging [238]. As a result, some RNA-based drug candidates have been discontinued in trials due to limited efficacy or adverse effects.

6. CRISPR-Cas13: The State-of-the-Art RNA-Based Approach for Potential Targeting of ncRNAs and Future Directions

In recent years, the rapid evolution of high-throughput sequencing technologies has transformed our understanding of ncRNAs from transcripts with unknown functions to key regulators of gene expression and disease pathology [2]. As the field consistently progresses, innovative tools and strategies are introduced that are expected to upgrade ncRNA research and its clinical applications. The discovery and ongoing advancement of CRISPR-Cas editing technologies have revolutionized functional genomics, including the study of ncRNAs [239,240]. While the initial focus of CRISPR systems was on protein-coding genes, subsequent adjustments of the implemented methods enabled the precise targeting and manipulation of ncRNAs [241,242]. As follows, these technologies facilitated the multifaceted study of ncRNAs, shedding light into their role in gene expression regulation and their involvement in disease and progression [242].
The Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) system, originally discovered as an adaptive immune defense mechanism in bacteria and archaea, has revolutionized molecular biology by enabling precise and efficient nucleic acid editing [240]. While the CRISPR-Cas9 system is widely utilized for DNA editing in research and clinical applications, especially for drug discovery, the newly introduced CRISPR-Cas13 has generated growing interest in the context of RNA manipulation. Unlike Cas9, which modifies the genome, Cas13 cleaves single-stranded RNA, allowing for dynamic, reversible, and non-permanent modulation of gene expression and making it an attractive option for applications where DNA integrity must be preserved [243].
CRISPR-Cas13 systems, comprising various endonucleases subtypes such as Cas13a, Cas13b, and Cas13d, functions by forming a complex with a custom-designed guide RNA (gRNA) that directs the enzyme to a complementary RNA target. Upon binding, Cas13 cleaves the RNA, effectively reducing its cellular levels [243]. This mechanism has proven especially useful for targeting pathogenic transcripts in diseases such as neurodegeneration, viral infections, and cancer [244]. Since Cas13 does not interact with DNA, it minimizes the risk of off-target genetic mutations and offers a safer form of intervention, suitable for diseases that require reversible or regulated therapeutic responses.
One of the greatest advantages of Cas13 systems lies in their versatility. More precisely, they can be programmed to degrade specific RNA transcripts (knockdown), interfere with RNA processing and splicing, or even edit RNA bases at the transcript level using engineered variants (Figure 3). These features enable researchers to evaluate the function of individual ncRNAs, deciphering their roles in gene regulation and disease mechanisms [245,246]. For example, CRISPR-Cas13 has been successfully used to knockdown oncogenic lncRNAs in preclinical cancer models, revealing their implications to tumor development and metastasis [242]. In addition to effective knockdown, the evolving CRISPR-Cas13 system is being modified for broader manipulation of RNA. In detail, catalytically inactive Cas13 (dCas13) fused with effector domains can be used for targeted RNA imaging, localization studies, and modulation of translation or RNA stability (Figure 3) [242,247]. Furthermore, these systems are being developed to achieve site-specific RNA base editing, suggesting an alternative approach for treating diseases caused by pathogenic point mutations at the RNA level.
Surprisingly, although CRISPR-Cas13 systems are a relatively recent addition in the research field, they have already advanced into clinical trials, highlighting their potential in drug development. HuidaGene Therapeutics, a biotechnology company at the forefront of RNA editing technologies, has launched two major first-in-human clinical programs using CRISPR-Cas13 system: the HERO trial for MECP2 Duplication Syndrome (MDS) and the SIGHT-I/BRIGHT trials for neovascular age-related macular degeneration (nAMD) [248].
The HERO trial investigates HG204, a Cas13-based RNA therapy designed to target and degrade overexpressed MECP2 mRNA, which is the underlying cause of MDS, a severe neurodevelopmental disorder [248]. This condition, which predominantly affects males, is characterized by intellectual disability, motor dysfunction, and recurrent infections due to the toxic effects of excess MECP2 protein [249]. The first patient was dosed in November 2024, and early post-treatment data indicate no major adverse events alongside preliminary signs of therapeutic benefit, signifying a major advancement in the development of therapies for this rare and debilitating neurodevelopmental disorder [248].
Meanwhile, HG202, another Cas13-based therapeutic approach developed by the same company, is being evaluated in the SIGHT-I (China) and BRIGHT (U.S.) trials for nAMD. This condition, caused by the pathological overexpression of vascular endothelial growth factor A (VEGFA) in the retina, leads to vision loss in older adults [250]. HG202 is the first RNA-editing therapy targeting VEGFA mRNA in the human eye. The BRIGHT trial received FDA clearance in November 2024, making it the first Cas13 RNA-editing therapy approved for human testing in the United States [251].
Notably, Cas13 is a powerful tool for targeting and silencing ncRNAs implicated in severe pathological conditions including cancer, neurological disorders, and cardiovascular diseases [244]. The ability to suppress these regulatory RNAs without affecting genomic integrity is valuable for the development of novel, more effective therapeutic approaches.

7. Conclusions

ncRNAs are vital regulators of gene expression, influencing development, immune responses, and cellular homeostasis. The dysregulation of ncRNAs can lead to various diseases, making them important both as biomarkers and therapeutic targets, while their stability in body fluids further enhances their diagnostic potential. Numerous studies have demonstrated that specific ncRNAs, such as lncRNAs and miRNAs, exhibit distinct expression patterns between patients and healthy individuals across a range of pathological conditions, including cancer and cardiovascular and autoimmune diseases. This differential expression highlights their potential utility as diagnostic, prognostic, and treatment-monitoring biomarkers. In terms of therapy, efforts to modulate ncRNA activity have led to the development of miRNA mimics and inhibitors, as well as siRNAs targeting lncRNAs that aim to restore gene expression levels and, thus, cellular function. The emergence of CRISPR-Cas13 technology marks a significant breakthrough in ncRNA research and therapy. Unlike Cas9, which edits DNA, Cas13 targets RNA directly, allowing for precise, reversible modulation of pathogenic RNAs without altering the genome. Clinical trials using CRISPR-Cas13 RNA therapies are already underway for protein-coding genes, demonstrating the technology’s potential to transform precision medicine by offering safer, targeted interventions that can be exploited in the development of ncRNA-based treatment approaches.

Author Contributions

Writing—original draft preparation, M.A.D., T.S. and M.A.B.; writing—review and editing, M.A.B. and T.S.; visualization, M.A.D.; supervision, A.S.; project administration, A.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

No new data were created or analyzed in this study. Data sharing is not applicable to this article.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Diamantopoulos, M.A.; Tsiakanikas, P.; Scorilas, A. Non-coding RNAs: The riddle of the transcriptome and their perspectives in cancer. Ann. Transl. Med. 2018, 6, 241. [Google Scholar] [CrossRef]
  2. Djebali, S.; Davis, C.A.; Merkel, A.; Dobin, A.; Lassmann, T.; Mortazavi, A.; Tanzer, A.; Lagarde, J.; Lin, W.; Schlesinger, F.; et al. Landscape of transcription in human cells. Nature 2012, 489, 101–108. [Google Scholar] [CrossRef]
  3. Saw, P.E.; Xu, X.; Chen, J.; Song, E.-W. Non-coding RNAs: The new central dogma of cancer biology. Sci. China Life Sci. 2021, 64, 22–50. [Google Scholar] [CrossRef]
  4. Pennisi, E. Genomics. ENCODE project writes eulogy for junk DNA. Science 2012, 337, 1159–1161. [Google Scholar] [CrossRef] [PubMed]
  5. Searles, C.D. MicroRNAs and Cardiovascular Disease Risk. Curr. Cardiol. Rep. 2024, 26, 51–60. [Google Scholar] [CrossRef] [PubMed]
  6. Alkhazaali-Ali, Z.; Sahab-Negah, S.; Boroumand, A.R.; Tavakol-Afshari, J. MicroRNA (miRNA) as a biomarker for diagnosis, prognosis, and therapeutics molecules in neurodegenerative disease. Biomed. Pharmacother. 2024, 177, 116899. [Google Scholar] [CrossRef] [PubMed]
  7. Chen, J.; Luo, M.; Xing, Z.; Chen, Y.; Peng, C.; Li, D. Start small, think big: MicroRNAs in diabetes mellitus and relevant cardiorenal-liver metabolic health spectrum. Metabolism 2025, 165, 156153. [Google Scholar] [CrossRef]
  8. Toden, S.; Goel, A. Non-coding RNAs as liquid biopsy biomarkers in cancer. Br. J. Cancer 2022, 126, 351–360. [Google Scholar] [CrossRef]
  9. Gao, J.; Zhang, X.; Ding, J.; Zhang, H.; Zhang, X.; Jiang, J.; Chen, W. The characteristic expression of circulating MicroRNAs in osteoporosis: A systematic review and meta-analysis. Front. Endocrinol. 2024, 15, 1481649. [Google Scholar] [CrossRef]
  10. Chen, Y.; Dai, J.; Chen, P.; Dai, Q.; Chen, Y.; Li, Y.; Lu, M.; Qin, S.; Wang, Q. Long non-coding RNAs-sphingolipid metabolism nexus: Potential targets for cancer treatment. Pharmacol. Res. 2024, 210, 107539. [Google Scholar] [CrossRef]
  11. Iorio, M.V.; Croce, C.M. MicroRNA dysregulation in cancer: Diagnostics, monitoring and therapeutics. A comprehensive review. EMBO Mol. Med. 2012, 4, 143–159. [Google Scholar] [CrossRef] [PubMed]
  12. Metcalf, G.A.D. MicroRNAs: Circulating biomarkers for the early detection of imperceptible cancers via biosensor and machine-learning advances. Oncogene 2024, 43, 2135–2142. [Google Scholar] [CrossRef] [PubMed]
  13. Kim, T.; Croce, C.M. MicroRNA: Trends in clinical trials of cancer diagnosis and therapy strategies. Exp. Mol. Med. 2023, 55, 1314–1321. [Google Scholar] [CrossRef]
  14. Mathew, V.; Wang, A.K. Inotersen: New promise for the treatment of hereditary transthyretin amyloidosis. Drug Des. Dev. Ther. 2019, 13, 1515–1525. [Google Scholar] [CrossRef] [PubMed]
  15. Loganathan, T.; Doss, C.G. Non-coding RNAs in human health and disease: Potential function as biomarkers and therapeutic targets. Funct. Integr. Genom. 2023, 23, 33. [Google Scholar] [CrossRef]
  16. Fu, X.D. Non-coding RNA: A new frontier in regulatory biology. Natl. Sci. Rev. 2014, 1, 190–204. [Google Scholar] [CrossRef]
  17. Ender, C.; Krek, A.; Friedlander, M.R.; Beitzinger, M.; Weinmann, L.; Chen, W.; Pfeffer, S.; Rajewsky, N.; Meister, G. A human snoRNA with microRNA-like functions. Mol. Cell 2008, 32, 519–528. [Google Scholar] [CrossRef]
  18. Chen, X.; Fan, S.; Song, E. Noncoding RNAs: New Players in Cancers. Adv. Exp. Med. Biol. 2016, 927, 1–47. [Google Scholar] [CrossRef]
  19. Dawar, P.; Adhikari, I.; Mandal, S.N.; Jayee, B. RNA Metabolism and the Role of Small RNAs in Regulating Multiple Aspects of RNA Metabolism. Non-Coding RNA 2025, 11, 1. [Google Scholar] [CrossRef]
  20. Lee, Y.S.; Shibata, Y.; Malhotra, A.; Dutta, A. A novel class of small RNAs: tRNA-derived RNA fragments (tRFs). Genes. Dev. 2009, 23, 2639–2649. [Google Scholar] [CrossRef]
  21. Kay, M.A. Ruvkun and Ambros recognized for miRNAs. Mol. Ther. Nucleic Acids 2024, 35, 102379. [Google Scholar] [CrossRef]
  22. Lee, R.C.; Feinbaum, R.L.; Ambros, V. The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell 1993, 75, 843–854. [Google Scholar] [CrossRef] [PubMed]
  23. Wightman, B.; Ha, I.; Ruvkun, G. Posttranscriptional regulation of the heterochronic gene lin-14 by lin-4 mediates temporal pattern formation in C. elegans. Cell 1993, 75, 855–862. [Google Scholar] [CrossRef] [PubMed]
  24. Reinhart, B.J.; Slack, F.J.; Basson, M.; Pasquinelli, A.E.; Bettinger, J.C.; Rougvie, A.E.; Horvitz, H.R.; Ruvkun, G. The 21-nucleotide let-7 RNA regulates developmental timing in Caenorhabditis elegans. Nature 2000, 403, 901–906. [Google Scholar] [CrossRef] [PubMed]
  25. Pasquinelli, A.E.; Reinhart, B.J.; Slack, F.; Martindale, M.Q.; Kuroda, M.I.; Maller, B.; Hayward, D.C.; Ball, E.E.; Degnan, B.; Muller, P.; et al. Conservation of the sequence and temporal expression of let-7 heterochronic regulatory RNA. Nature 2000, 408, 86–89. [Google Scholar] [CrossRef]
  26. Ma, C.P.; Lo, S.J.; Chin-Ming Tan, B. Good things come in small packages: The discovery of small RNAs in the smallest animal model. Biomed. J. 2025, 48, 100832. [Google Scholar] [CrossRef]
  27. Bartel, D.P. MicroRNAs: Genomics, biogenesis, mechanism, and function. Cell 2004, 116, 281–297. [Google Scholar] [CrossRef]
  28. Lee, Y.; Kim, M.; Han, J.; Yeom, K.H.; Lee, S.; Baek, S.H.; Kim, V.N. MicroRNA genes are transcribed by RNA polymerase II. EMBO J. 2004, 23, 4051–4060. [Google Scholar] [CrossRef]
  29. Lagos-Quintana, M.; Rauhut, R.; Lendeckel, W.; Tuschl, T. Identification of novel genes coding for small expressed RNAs. Science 2001, 294, 853–858. [Google Scholar] [CrossRef]
  30. Lee, R.C.; Ambros, V. An extensive class of small RNAs in Caenorhabditis elegans. Science 2001, 294, 862–864. [Google Scholar] [CrossRef]
  31. Conrad, T.; Marsico, A.; Gehre, M.; Orom, U.A. Microprocessor activity controls differential miRNA biogenesis In Vivo. Cell Rep. 2014, 9, 542–554. [Google Scholar] [CrossRef]
  32. Lee, Y.; Jeon, K.; Lee, J.T.; Kim, S.; Kim, V.N. MicroRNA maturation: Stepwise processing and subcellular localization. EMBO J. 2002, 21, 4663–4670. [Google Scholar] [CrossRef] [PubMed]
  33. Yi, R.; Qin, Y.; Macara, I.G.; Cullen, B.R. Exportin-5 mediates the nuclear export of pre-microRNAs and short hairpin RNAs. Genes. Dev. 2003, 17, 3011–3016. [Google Scholar] [CrossRef] [PubMed]
  34. Bartel, D.P. Metazoan MicroRNAs. Cell 2018, 173, 20–51. [Google Scholar] [CrossRef] [PubMed]
  35. Kim, V.N.; Han, J.; Siomi, M.C. Biogenesis of small RNAs in animals. Nat. Rev. Mol. Cell Biol. 2009, 10, 126–139. [Google Scholar] [CrossRef]
  36. McGeary, S.E.; Lin, K.S.; Shi, C.Y.; Pham, T.M.; Bisaria, N.; Kelley, G.M.; Bartel, D.P. The biochemical basis of microRNA targeting efficacy. Science 2019, 366, eaav1741. [Google Scholar] [CrossRef]
  37. Lytle, J.R.; Yario, T.A.; Steitz, J.A. Target mRNAs are repressed as efficiently by microRNA-binding sites in the 5’ UTR as in the 3’ UTR. Proc. Natl. Acad. Sci. USA 2007, 104, 9667–9672. [Google Scholar] [CrossRef]
  38. Broughton, J.P.; Lovci, M.T.; Huang, J.L.; Yeo, G.W.; Pasquinelli, A.E. Pairing beyond the Seed Supports MicroRNA Targeting Specificity. Mol. Cell 2016, 64, 320–333. [Google Scholar] [CrossRef]
  39. Ruby, J.G.; Jan, C.H.; Bartel, D.P. Intronic microRNA precursors that bypass Drosha processing. Nature 2007, 448, 83–86. [Google Scholar] [CrossRef]
  40. Yang, J.S.; Maurin, T.; Robine, N.; Rasmussen, K.D.; Jeffrey, K.L.; Chandwani, R.; Papapetrou, E.P.; Sadelain, M.; O’Carroll, D.; Lai, E.C. Conserved vertebrate mir-451 provides a platform for Dicer-independent, Ago2-mediated microRNA biogenesis. Proc. Natl. Acad. Sci. USA 2010, 107, 15163–15168. [Google Scholar] [CrossRef]
  41. O’Brien, J.; Hayder, H.; Zayed, Y.; Peng, C. Overview of MicroRNA Biogenesis, Mechanisms of Actions, and Circulation. Front. Endocrinol. 2018, 9, 402. [Google Scholar] [CrossRef]
  42. Mercer, T.R.; Dinger, M.E.; Mattick, J.S. Long non-coding RNAs: Insights into functions. Nat. Rev. Genet. 2009, 10, 155–159. [Google Scholar] [CrossRef]
  43. Iyer, M.K.; Niknafs, Y.S.; Malik, R.; Singhal, U.; Sahu, A.; Hosono, Y.; Barrette, T.R.; Prensner, J.R.; Evans, J.R.; Zhao, S.; et al. The landscape of long noncoding RNAs in the human transcriptome. Nat. Genet. 2015, 47, 199–208. [Google Scholar] [CrossRef]
  44. Chen, L.L.; Kim, V.N. Small and long non-coding RNAs: Past, present, and future. Cell 2024, 187, 6451–6485. [Google Scholar] [CrossRef]
  45. Derrien, T.; Johnson, R.; Bussotti, G.; Tanzer, A.; Djebali, S.; Tilgner, H.; Guernec, G.; Martin, D.; Merkel, A.; Knowles, D.G.; et al. The GENCODE v7 catalog of human long noncoding RNAs: Analysis of their gene structure, evolution, and expression. Genome Res. 2012, 22, 1775–1789. [Google Scholar] [CrossRef]
  46. Deveson, I.W.; Brunck, M.E.; Blackburn, J.; Tseng, E.; Hon, T.; Clark, T.A.; Clark, M.B.; Crawford, J.; Dinger, M.E.; Nielsen, L.K.; et al. Universal Alternative Splicing of Noncoding Exons. Cell Syst. 2018, 6, 245–255.e245. [Google Scholar] [CrossRef] [PubMed]
  47. Mattick, J.S.; Amaral, P.P.; Carninci, P.; Carpenter, S.; Chang, H.Y.; Chen, L.-L.; Chen, R.; Dean, C.; Dinger, M.E.; Fitzgerald, K.A.; et al. Long non-coding RNAs: Definitions, functions, challenges and recommendations. Nat. Rev. Mol. Cell Biol. 2023, 24, 430–447. [Google Scholar] [CrossRef] [PubMed]
  48. Ma, L.; Bajic, V.B.; Zhang, Z. On the classification of long non-coding RNAs. RNA Biol. 2013, 10, 925–933. [Google Scholar] [CrossRef] [PubMed]
  49. Zhou, M.; Guo, X.; Wang, M.; Qin, R. The patterns of antisense long non-coding RNAs regulating corresponding sense genes in human cancers. J. Cancer 2021, 12, 1499–1506. [Google Scholar] [CrossRef]
  50. Grammatikakis, I.; Lal, A. Significance of lncRNA abundance to function. Mamm. Genome Off. J. Int. Mamm. Genome Soc. 2022, 33, 271–280. [Google Scholar] [CrossRef]
  51. Jeck, W.R.; Sharpless, N.E. Detecting and characterizing circular RNAs. Nat. Biotechnol. 2014, 32, 453–461. [Google Scholar] [CrossRef]
  52. Nigro, J.M.; Cho, K.R.; Fearon, E.R.; Kern, S.E.; Ruppert, J.M.; Oliner, J.D.; Kinzler, K.W.; Vogelstein, B. Scrambled exons. Cell 1991, 64, 607–613. [Google Scholar] [CrossRef] [PubMed]
  53. Chen, L.L.; Yang, L. Regulation of circRNA biogenesis. RNA Biol. 2015, 12, 381–388. [Google Scholar] [CrossRef] [PubMed]
  54. Jeck, W.R.; Sorrentino, J.A.; Wang, K.; Slevin, M.K.; Burd, C.E.; Liu, J.; Marzluff, W.F.; Sharpless, N.E. Circular RNAs are abundant, conserved, and associated with ALU repeats. RNA 2013, 19, 141–157. [Google Scholar] [CrossRef]
  55. Hansen, T.B.; Jensen, T.I.; Clausen, B.H.; Bramsen, J.B.; Finsen, B.; Damgaard, C.K.; Kjems, J. Natural RNA circles function as efficient microRNA sponges. Nature 2013, 495, 384–388. [Google Scholar] [CrossRef]
  56. Poliseno, L.; Salmena, L.; Zhang, J.; Carver, B.; Haveman, W.J.; Pandolfi, P.P. A coding-independent function of gene and pseudogene mRNAs regulates tumour biology. Nature 2010, 465, 1033–1038. [Google Scholar] [CrossRef]
  57. Vagin, V.V.; Sigova, A.; Li, C.; Seitz, H.; Gvozdev, V.; Zamore, P.D. A distinct small RNA pathway silences selfish genetic elements in the germline. Science 2006, 313, 320–324. [Google Scholar] [CrossRef]
  58. Ozata, D.M.; Gainetdinov, I.; Zoch, A.; O’Carroll, D.; Zamore, P.D. PIWI-interacting RNAs: Small RNAs with big functions. Nat. Rev. Genet. 2019, 20, 89–108. [Google Scholar] [CrossRef]
  59. Malone, C.D.; Brennecke, J.; Dus, M.; Stark, A.; McCombie, W.R.; Sachidanandam, R.; Hannon, G.J. Specialized piRNA pathways act in germline and somatic tissues of the Drosophila ovary. Cell 2009, 137, 522–535. [Google Scholar] [CrossRef]
  60. Chen, S.; Ben, S.; Xin, J.; Li, S.; Zheng, R.; Wang, H.; Fan, L.; Du, M.; Zhang, Z.; Wang, M. The biogenesis and biological function of PIWI-interacting RNA in cancer. J. Hematol. Oncol. 2021, 14, 93. [Google Scholar] [CrossRef]
  61. Siomi, M.C.; Sato, K.; Pezic, D.; Aravin, A.A. PIWI-interacting small RNAs: The vanguard of genome defence. Nat. Rev. Mol. Cell Biol. 2011, 12, 246–258. [Google Scholar] [CrossRef] [PubMed]
  62. Nishimasu, H.; Ishizu, H.; Saito, K.; Fukuhara, S.; Kamatani, M.K.; Bonnefond, L.; Matsumoto, N.; Nishizawa, T.; Nakanaga, K.; Aoki, J.; et al. Structure and function of Zucchini endoribonuclease in piRNA biogenesis. Nature 2012, 491, 284–287. [Google Scholar] [CrossRef] [PubMed]
  63. Czech, B.; Hannon, G.J. One Loop to Rule Them All: The Ping-Pong Cycle and piRNA-Guided Silencing. Trends Biochem. Sci. 2016, 41, 324–337. [Google Scholar] [CrossRef] [PubMed]
  64. Liu, Z.; Zhao, X. piRNAs as emerging biomarkers and physiological regulatory molecules in cardiovascular disease. Biochem. Biophys. Res. Commun. 2024, 711, 149906. [Google Scholar] [CrossRef]
  65. Esteller, M. Non-coding RNAs in human disease. Nat. Rev. Genet. 2011, 12, 861–874. [Google Scholar] [CrossRef]
  66. Huang, Z.-h.; Du, Y.-p.; Wen, J.-t.; Lu, B.-f.; Zhao, Y. snoRNAs: Functions and mechanisms in biological processes, and roles in tumor pathophysiology. Cell Death Discov. 2022, 8, 259. [Google Scholar] [CrossRef]
  67. van der Werf, J.; Chin, C.V.; Fleming, N.I. SnoRNA in Cancer Progression, Metastasis and Immunotherapy Response. Biology 2021, 10, 809. [Google Scholar] [CrossRef]
  68. Bratkovič, T.; Rogelj, B. The many faces of small nucleolar RNAs. Biochim. Biophys. Acta (BBA)-Gene Regul. Mech. 2014, 1839, 438–443. [Google Scholar] [CrossRef]
  69. Li, Z.; Ender, C.; Meister, G.; Moore, P.S.; Chang, Y.; John, B. Extensive terminal and asymmetric processing of small RNAs from rRNAs, snoRNAs, snRNAs, and tRNAs. Nucleic Acids Res. 2012, 40, 6787–6799. [Google Scholar] [CrossRef]
  70. Rosace, D.; Lopez, J.; Blanco, S. Emerging roles of novel small non-coding regulatory RNAs in immunity and cancer. RNA Biol. 2020, 17, 1196–1213. [Google Scholar] [CrossRef]
  71. Thompson, D.M.; Lu, C.; Green, P.J.; Parker, R. tRNA cleavage is a conserved response to oxidative stress in eukaryotes. RNA 2008, 14, 2095–2103. [Google Scholar] [CrossRef]
  72. Lee, H.-C.; Chang, S.-S.; Choudhary, S.; Aalto, A.P.; Maiti, M.; Bamford, D.H.; Liu, Y. qiRNA is a new type of small interfering RNA induced by DNA damage. Nature 2009, 459, 274–277. [Google Scholar] [CrossRef] [PubMed]
  73. Lambert, M.; Benmoussa, A.; Provost, P. Small Non-Coding RNAs Derived from Eukaryotic Ribosomal RNA. Non-Coding RNA 2019, 5, 16. [Google Scholar] [CrossRef] [PubMed]
  74. Guan, L.; Grigoriev, A. Computational meta-analysis of ribosomal RNA fragments: Potential targets and interaction mechanisms. Nucleic Acids Res. 2021, 49, 4085–4103. [Google Scholar] [CrossRef] [PubMed]
  75. Zong, Y.; Wang, X.; Cui, B.; Xiong, X.; Wu, A.; Lin, C.; Zhang, Y. Decoding the regulatory roles of non-coding RNAs in cellular metabolism and disease. Mol. Ther. J. Am. Soc. Gene Ther. 2023, 31, 1562–1576. [Google Scholar] [CrossRef]
  76. Scholda, J.; Nguyen, T.T.A.; Kopp, F. Long noncoding RNAs as versatile molecular regulators of cellular stress response and homeostasis. Hum. Genet. 2024, 143, 813–829. [Google Scholar] [CrossRef]
  77. Segal, D.; Dostie, J. The Talented LncRNAs: Meshing into Transcriptional Regulatory Networks in Cancer. Cancers 2023, 15, 3433. [Google Scholar] [CrossRef]
  78. Yin, J.; Park, G.; Lee, J.E.; Choi, E.Y.; Park, J.Y.; Kim, T.H.; Park, N.; Jin, X.; Jung, J.E.; Shin, D.; et al. DEAD-box RNA helicase DDX23 modulates glioma malignancy via elevating miR-21 biogenesis. Brain J. Neurol. 2015, 138, 2553–2570. [Google Scholar] [CrossRef]
  79. Kitagawa, M.; Kitagawa, K.; Kotake, Y.; Niida, H.; Ohhata, T. Cell cycle regulation by long non-coding RNAs. Cell. Mol. Life Sci. CMLS 2013, 70, 4785–4794. [Google Scholar] [CrossRef]
  80. Hu, W.; Alvarez-Dominguez, J.R.; Lodish, H.F. Regulation of mammalian cell differentiation by long non-coding RNAs. EMBO Rep. 2012, 13, 971–983. [Google Scholar] [CrossRef]
  81. Wei, J.W.; Huang, K.; Yang, C.; Kang, C.S. Non-coding RNAs as regulators in epigenetics (Review). Oncol. Rep. 2017, 37, 3–9. [Google Scholar] [CrossRef]
  82. Huang, A.; Zheng, H.; Wu, Z.; Chen, M.; Huang, Y. Circular RNA-protein interactions: Functions, mechanisms, and identification. Theranostics 2020, 10, 3503–3517. [Google Scholar] [CrossRef]
  83. Conn, V.M.; Hugouvieux, V.; Nayak, A.; Conos, S.A.; Capovilla, G.; Cildir, G.; Jourdain, A.; Tergaonkar, V.; Schmid, M.; Zubieta, C.; et al. A circRNA from SEPALLATA3 regulates splicing of its cognate mRNA through R-loop formation. Nat. Plants 2017, 3, 17053. [Google Scholar] [CrossRef]
  84. Li, S.; Peng, M.; Tan, S.; Oyang, L.; Lin, J.; Xia, L.; Wang, J.; Wu, N.; Jiang, X.; Peng, Q.; et al. The roles and molecular mechanisms of non-coding RNA in cancer metabolic reprogramming. Cancer Cell Int. 2024, 24, 37. [Google Scholar] [CrossRef] [PubMed]
  85. Chatterjee, S.; Gupta, S.K.; Bär, C.; Thum, T. Noncoding RNAs: Potential regulators in cardioncology. Am. J. Physiol. Heart Circ. Physiol. 2019, 316, H160–H168. [Google Scholar] [CrossRef] [PubMed]
  86. Hossam Abdelmonem, B.; Kamal, L.T.; Wardy, L.W.; Ragheb, M.; Hanna, M.M.; Elsharkawy, M.; Abdelnaser, A. Non-coding RNAs: Emerging biomarkers and therapeutic targets in cancer and inflammatory diseases. Front. Oncol. 2025, 15, 1534862. [Google Scholar] [CrossRef] [PubMed]
  87. Guh, C.Y.; Hsieh, Y.H.; Chu, H.P. Functions and properties of nuclear lncRNAs-from systematically mapping the interactomes of lncRNAs. J. Biomed. Sci. 2020, 27, 44. [Google Scholar] [CrossRef]
  88. Jie, M.; Feng, T.; Huang, W.; Zhang, M.; Feng, Y.; Jiang, H.; Wen, Z. Subcellular Localization of miRNAs and Implications in Cellular Homeostasis. Genes 2021, 12, 856. [Google Scholar] [CrossRef]
  89. Nadhan, R.; Isidoro, C.; Song, Y.S.; Dhanasekaran, D.N. Signaling by LncRNAs: Structure, Cellular Homeostasis, and Disease Pathology. Cells 2022, 11, 2517. [Google Scholar] [CrossRef]
  90. Dexheimer, P.J.; Cochella, L. MicroRNAs: From Mechanism to Organism. Front. Cell Dev. Biol. 2020, 8, 409. [Google Scholar] [CrossRef]
  91. Gebert, L.F.R.; MacRae, I.J. Regulation of microRNA function in animals. Nat. Rev. Mol. Cell Biol. 2019, 20, 21–37. [Google Scholar] [CrossRef]
  92. Claro-Linares, F.; Rojas-Ríos, P. PIWI proteins and piRNAs: Key regulators of stem cell biology. Front. Cell Dev. Biol. 2025, 13, 1540313. [Google Scholar] [CrossRef] [PubMed]
  93. Cerase, A.; Pintacuda, G.; Tattermusch, A.; Avner, P. Xist localization and function: New insights from multiple levels. Genome Biol. 2015, 16, 166. [Google Scholar] [CrossRef] [PubMed]
  94. Lu, D.; Xu, A.D. Mini Review: Circular RNAs as Potential Clinical Biomarkers for Disorders in the Central Nervous System. Front. Genet. 2016, 7, 53. [Google Scholar] [CrossRef]
  95. Bartel, D.P. MicroRNAs: Target recognition and regulatory functions. Cell 2009, 136, 215–233. [Google Scholar] [CrossRef] [PubMed]
  96. Saliminejad, K.; Khorram Khorshid, H.R.; Soleymani Fard, S.; Ghaffari, S.H. An overview of microRNAs: Biology, functions, therapeutics, and analysis methods. J. Cell. Physiol. 2019, 234, 5451–5465. [Google Scholar] [CrossRef]
  97. Chen, C.Z.; Li, L.; Lodish, H.F.; Bartel, D.P. MicroRNAs modulate hematopoietic lineage differentiation. Science 2004, 303, 83–86. [Google Scholar] [CrossRef]
  98. Xiao, C.; Calado, D.P.; Galler, G.; Thai, T.H.; Patterson, H.C.; Wang, J.; Rajewsky, N.; Bender, T.P.; Rajewsky, K. MiR-150 controls B cell differentiation by targeting the transcription factor c-Myb. Cell 2007, 131, 146–159. [Google Scholar] [CrossRef]
  99. Wang, K.C.; Yang, Y.W.; Liu, B.; Sanyal, A.; Corces-Zimmerman, R.; Chen, Y.; Lajoie, B.R.; Protacio, A.; Flynn, R.A.; Gupta, R.A.; et al. A long noncoding RNA maintains active chromatin to coordinate homeotic gene expression. Nature 2011, 472, 120–124. [Google Scholar] [CrossRef]
  100. O’Connell, R.M.; Kahn, D.; Gibson, W.S.; Round, J.L.; Scholz, R.L.; Chaudhuri, A.A.; Kahn, M.E.; Rao, D.S.; Baltimore, D. MicroRNA-155 promotes autoimmune inflammation by enhancing inflammatory T cell development. Immunity 2010, 33, 607–619. [Google Scholar] [CrossRef]
  101. Goncalves-Alves, E.; Saferding, V.; Schliehe, C.; Benson, R.; Kurowska-Stolarska, M.; Brunner, J.S.; Puchner, A.; Podesser, B.K.; Smolen, J.S.; Redlich, K.; et al. MicroRNA-155 Controls T Helper Cell Activation During Viral Infection. Front. Immunol. 2019, 10, 1367. [Google Scholar] [CrossRef]
  102. Brazão, T.F.; Johnson, J.S.; Müller, J.; Heger, A.; Ponting, C.P.; Tybulewicz, V.L. Long noncoding RNAs in B-cell development and activation. Blood 2016, 128, e10–e19. [Google Scholar] [CrossRef]
  103. Zhang, P.; Cao, L.; Zhou, R.; Yang, X.; Wu, M. The lncRNA Neat1 promotes activation of inflammasomes in macrophages. Nat. Commun. 2019, 10, 1495. [Google Scholar] [CrossRef]
  104. Zhao, Q.; Pang, G.; Yang, L.; Chen, S.; Xu, R.; Shao, W. Long Noncoding RNAs Regulate the Inflammatory Responses of Macrophages. Cells 2021, 11, 5. [Google Scholar] [CrossRef] [PubMed]
  105. Alexander, M.; O’Connell, R.M. Noncoding RNAs and chronic inflammation: Micro-managing the fire within. BioEssays News Rev. Mol. Cell. Dev. Biol. 2015, 37, 1005–1015. [Google Scholar] [CrossRef] [PubMed]
  106. Connell, R.M.; Rao, D.S.; Baltimore, D. microRNA Regulation of Inflammatory Responses. Annu. Rev. Immunol. 2012, 30, 295–312. [Google Scholar] [CrossRef] [PubMed]
  107. Ulitsky, I.; Shkumatava, A.; Jan, C.H.; Sive, H.; Bartel, D.P. Conserved function of lincRNAs in vertebrate embryonic development despite rapid sequence evolution. Cell 2011, 147, 1537–1550. [Google Scholar] [CrossRef]
  108. Coolen, M.; Katz, S.; Bally-Cuif, L. miR-9: A versatile regulator of neurogenesis. Front. Cell. Neurosci. 2013, 7, 220. [Google Scholar] [CrossRef]
  109. Veremeyko, T.; Kuznetsova, I.S.; Dukhinova, M.; Yung, A.W.Y.; Kopeikina, E.; Barteneva, N.S.; Ponomarev, E.D. Neuronal extracellular microRNAs miR-124 and miR-9 mediate cell-cell communication between neurons and microglia. J. Neurosci. Res. 2019, 97, 162–184. [Google Scholar] [CrossRef]
  110. Qureshi, I.A.; Mehler, M.F. Emerging roles of non-coding RNAs in brain evolution, development, plasticity and disease. Nat. Rev. Neurosci. 2012, 13, 528–541. [Google Scholar] [CrossRef]
  111. Jansen, F.; Yang, X.; Hoelscher, M.; Cattelan, A.; Schmitz, T.; Proebsting, S.; Wenzel, D.; Vosen, S.; Franklin, B.S.; Fleischmann, B.K.; et al. Endothelial Microparticle–Mediated Transfer of MicroRNA-126 Promotes Vascular Endothelial Cell Repair via SPRED1 and Is Abrogated in Glucose-Damaged Endothelial Microparticles. Circulation 2013, 128, 2026–2038. [Google Scholar] [CrossRef]
  112. He, C.; Yang, W.; Yang, J.; Ding, J.; Li, S.; Wu, H.; Zhou, F.; Jiang, Y.; Teng, L.; Yang, J. Long Noncoding RNA MEG3 Negatively Regulates Proliferation and Angiogenesis in Vascular Endothelial Cells. DNA Cell Biol. 2017, 36, 475–481. [Google Scholar] [CrossRef] [PubMed]
  113. Zhou, Z.; Sun, B.; Huang, S.; Zhao, L. Roles of circular RNAs in immune regulation and autoimmune diseases. Cell Death Dis. 2019, 10, 503. [Google Scholar] [CrossRef]
  114. Wu, F.; Zhang, J.; Jiang, Q.; Li, Q.; Li, F.; Li, J.; Lv, W.; Wang, X.; Qin, Y.; Huang, C.; et al. MyoD1 promotes the transcription of BIK and plays an apoptosis-promoting role in the development of gastric cancer. Cell Cycle 2024, 23, 573–587. [Google Scholar] [CrossRef] [PubMed]
  115. Yang, Z.; Huang, C.; Huang, W.; Yan, C.; Wen, X.; Hu, D.; Xie, H.; He, K.; Tsang, C.K.; Li, K. Exacerbated ischemic brain damage in type 2 diabetes via methylglyoxal-mediated miR-148a-3p decline. BMC Med. 2024, 22, 557. [Google Scholar] [CrossRef] [PubMed]
  116. Ling, C.; Vavakova, M.; Ahmad Mir, B.; Säll, J.; Perfilyev, A.; Martin, M.; Jansson, P.A.; Davegårdh, C.; Asplund, O.; Hansson, O.; et al. Multiomics profiling of DNA methylation, microRNA, and mRNA in skeletal muscle from monozygotic twin pairs discordant for type 2 diabetes identifies dysregulated genes controlling metabolism. BMC Med. 2024, 22, 572. [Google Scholar] [CrossRef]
  117. Do, H.; Kim, W. Roles of Oncogenic Long Non-coding RNAs in Cancer Development. Genom. Inform. 2018, 16, e18. [Google Scholar] [CrossRef]
  118. Inamura, K. Major Tumor Suppressor and Oncogenic Non-Coding RNAs: Clinical Relevance in Lung Cancer. Cells 2017, 6, 12. [Google Scholar] [CrossRef]
  119. Liu, S.J.; Dang, H.X.; Lim, D.A.; Feng, F.Y.; Maher, C.A. Long noncoding RNAs in cancer metastasis. Nat. Rev. Cancer 2021, 21, 446–460. [Google Scholar] [CrossRef]
  120. Weidle, U.H.; Birzele, F.; Kollmorgen, G.; Rüger, R. Long Non-coding RNAs and their Role in Metastasis. Cancer Genom. Proteom. 2017, 14, 143–160. [Google Scholar] [CrossRef]
  121. Solé, C.; Lawrie, C.H. MicroRNAs and Metastasis. Cancers 2019, 12, 96. [Google Scholar] [CrossRef] [PubMed]
  122. Salinas-Vera, Y.M.; Marchat, L.A.; Gallardo-Rincón, D.; Ruiz-García, E.; Astudillo-De La Vega, H.; Echavarría-Zepeda, R.; López-Camarillo, C. AngiomiRs: MicroRNAs driving angiogenesis in cancer (Review). Int. J. Mol. Med. 2019, 43, 657–670. [Google Scholar] [CrossRef] [PubMed]
  123. Le, X.F.; Merchant, O.; Bast, R.C.; Calin, G.A. The Roles of MicroRNAs in the Cancer Invasion-Metastasis Cascade. Cancer Microenviron. Off. J. Int. Cancer Microenviron. Soc. 2010, 3, 137–147. [Google Scholar] [CrossRef] [PubMed]
  124. Ahmad, M.; Weiswald, L.-B.; Poulain, L.; Denoyelle, C.; Meryet-Figuiere, M. Involvement of lncRNAs in cancer cells migration, invasion and metastasis: Cytoskeleton and ECM crosstalk. J. Exp. Clin. Cancer Res. 2023, 42, 173. [Google Scholar] [CrossRef]
  125. Nadhan, R.; Dhanasekaran, D.N. Decoding the Oncogenic Signals from the Long Non-Coding RNAs. Onco 2021, 1, 176–206. [Google Scholar] [CrossRef]
  126. Gupta, R.A.; Shah, N.; Wang, K.C.; Kim, J.; Horlings, H.M.; Wong, D.J.; Tsai, M.-C.; Hung, T.; Argani, P.; Rinn, J.L.; et al. Long non-coding RNA HOTAIR reprograms chromatin state to promote cancer metastasis. Nature 2010, 464, 1071–1076. [Google Scholar] [CrossRef]
  127. Tripathi, V.; Ellis, J.D.; Shen, Z.; Song, D.Y.; Pan, Q.; Watt, A.T.; Freier, S.M.; Bennett, C.F.; Sharma, A.; Bubulya, P.A.; et al. The nuclear-retained noncoding RNA MALAT1 regulates alternative splicing by modulating SR splicing factor phosphorylation. Mol. Cell 2010, 39, 925–938. [Google Scholar] [CrossRef]
  128. Sun, Y.; Ma, L. New Insights into Long Non-Coding RNA MALAT1 in Cancer and Metastasis. Cancers 2019, 11, 216. [Google Scholar] [CrossRef]
  129. Fang, Y.; Xu, Y.; Wang, R.; Hu, L.; Guo, D.; Xue, F.; Guo, W.; Zhang, D.; Hu, J.; Li, Y.; et al. Recent advances on the roles of LncRNAs in cardiovascular disease. J. Cell. Mol. Med. 2020, 24, 12246–12257. [Google Scholar] [CrossRef]
  130. Vausort, M.; Wagner, D.R.; Devaux, Y. Long noncoding RNAs in patients with acute myocardial infarction. Circ. Res. 2014, 115, 668–677. [Google Scholar] [CrossRef]
  131. Song, Z.; Gao, R.; Yan, B. Potential roles of microRNA-1 and microRNA-133 in cardiovascular disease. RCM 2020, 21, 57–64. [Google Scholar] [CrossRef]
  132. Crocco, P.; Montesanto, A.; La Grotta, R.; Paparazzo, E.; Soraci, L.; Dato, S.; Passarino, G.; Rose, G. The Potential Contribution of MyomiRs miR-133a-3p, -133b, and -206 Dysregulation in Cardiovascular Disease Risk. Int. J. Mol. Sci. 2024, 25, 12772. [Google Scholar] [CrossRef]
  133. Wu, Y.-Y.; Kuo, H.-C. Functional roles and networks of non-coding RNAs in the pathogenesis of neurodegenerative diseases. J. Biomed. Sci. 2020, 27, 49. [Google Scholar] [CrossRef]
  134. Zhang, Y.; Zhao, Y.; Ao, X.; Yu, W.; Zhang, L.; Wang, Y.; Chang, W. The Role of Non-coding RNAs in Alzheimer’s Disease: From Regulated Mechanism to Therapeutic Targets and Diagnostic Biomarkers. Front. Aging Neurosci. 2021, 13, 654978. [Google Scholar] [CrossRef] [PubMed]
  135. Kuo, M.C.; Liu, S.C.; Hsu, Y.F.; Wu, R.M. The role of noncoding RNAs in Parkinson’s disease: Biomarkers and associations with pathogenic pathways. J. Biomed. Sci. 2021, 28, 78. [Google Scholar] [CrossRef] [PubMed]
  136. Hébert, S.S.; Horré, K.; Nicolaï, L.; Papadopoulou, A.S.; Mandemakers, W.; Silahtaroglu, A.N.; Kauppinen, S.; Delacourte, A.; De Strooper, B. Loss of microRNA cluster miR-29a/b-1 in sporadic Alzheimer’s disease correlates with increased BACE1/beta-secretase expression. Proc. Natl. Acad. Sci. USA 2008, 105, 6415–6420. [Google Scholar] [CrossRef] [PubMed]
  137. Modarresi, F.; Faghihi, M.A.; Lopez-Toledano, M.A.; Fatemi, R.P.; Magistri, M.; Brothers, S.P.; van der Brug, M.P.; Wahlestedt, C. Inhibition of natural antisense transcripts in vivo results in gene-specific transcriptional upregulation. Nat. Biotechnol. 2012, 30, 453–459. [Google Scholar] [CrossRef]
  138. Qu, B.; Shen, N. miRNAs in the Pathogenesis of Systemic Lupus Erythematosus. Int. J. Mol. Sci. 2015, 16, 9557–9572. [Google Scholar] [CrossRef]
  139. Wang, M.; Yu, F.; Wu, W.; Wang, Y.; Ding, H.; Qian, L. Epstein-Barr virus-encoded microRNAs as regulators in host immune responses. Int. J. Biol. Sci. 2018, 14, 565–576. [Google Scholar] [CrossRef]
  140. Borah, S.; Darricarrère, N.; Darnell, A.; Myoung, J.; Steitz, J.A. A viral nuclear noncoding RNA binds re-localized poly(A) binding protein and is required for late KSHV gene expression. PLoS Pathog. 2011, 7, e1002300. [Google Scholar] [CrossRef]
  141. Peronace, C.; Cione, E.; Abrego-Guandique, D.M.; Fazio, M.D.; Panduri, G.; Caroleo, M.C.; Cannataro, R.; Minchella, P. FAM19A4 and hsa-miR124-2 Double Methylation as Screening for ASC-H- and CIN1 HPV-Positive Women. Pathogens 2024, 13, 312. [Google Scholar] [CrossRef]
  142. FDA-NIH Biomarker Working Group. BEST (Biomarkers, EndpointS, and other Tools) Resource [Internet]. Silver Spring (MD): Food and Drug Administration (US); Contents of a Biomarker Description. 2016. Available online: https://www.ncbi.nlm.nih.gov/books/NBK566059/ (accessed on 14 September 2025).
  143. Califf, R.M. Biomarker definitions and their applications. Exp. Biol. Med. 2018, 243, 213–221. [Google Scholar] [CrossRef]
  144. Calin, G.A.; Croce, C.M. MicroRNA signatures in human cancers. Nat. Rev. Cancer 2006, 6, 857–866. [Google Scholar] [CrossRef]
  145. Backes, C.; Meese, E.; Keller, A. Specific miRNA Disease Biomarkers in Blood, Serum and Plasma: Challenges and Prospects. Mol. Diagn. Ther. 2016, 20, 509–518. [Google Scholar] [CrossRef]
  146. Asangani, I.A.; Rasheed, S.A.K.; Nikolova, D.A.; Leupold, J.H.; Colburn, N.H.; Post, S.; Allgayer, H. MicroRNA-21 (miR-21) post-transcriptionally downregulates tumor suppressor Pdcd4 and stimulates invasion, intravasation and metastasis in colorectal cancer. Oncogene 2008, 27, 2128–2136. [Google Scholar] [CrossRef]
  147. Zhu, S.; Si, M.L.; Wu, H.; Mo, Y.Y. MicroRNA-21 targets the tumor suppressor gene tropomyosin 1 (TPM1). J. Biol. Chem. 2007, 282, 14328–14336. [Google Scholar] [CrossRef] [PubMed]
  148. Zhang, J.-g.; Wang, J.-j.; Zhao, F.; Liu, Q.; Jiang, K.; Yang, G.-h. MicroRNA-21 (miR-21) represses tumor suppressor PTEN and promotes growth and invasion in non-small cell lung cancer (NSCLC). Clin. Chim. Acta 2010, 411, 846–852. [Google Scholar] [CrossRef] [PubMed]
  149. Chawra, H.S.; Agarwal, M.; Mishra, A.; Chandel, S.S.; Singh, R.P.; Dubey, G.; Kukreti, N.; Singh, M. MicroRNA-21’s role in PTEN suppression and PI3K/AKT activation: Implications for cancer biology. Pathol. Res. Pract. 2024, 254, 155091. [Google Scholar] [CrossRef] [PubMed]
  150. Papagiannakopoulos, T.; Shapiro, A.; Kosik, K.S. MicroRNA-21 targets a network of key tumor-suppressive pathways in glioblastoma cells. Cancer Res. 2008, 68, 8164–8172. [Google Scholar] [CrossRef]
  151. Rhim, J.; Baek, W.; Seo, Y.; Kim, J.H. From Molecular Mechanisms to Therapeutics: Understanding MicroRNA-21 in Cancer. Cells 2022, 11, 2791. [Google Scholar] [CrossRef]
  152. Zheng, X.; Rui, H.; Liu, Y.; Dong, J. Proliferation and Apoptosis of B-Cell Lymphoma Cells under Targeted Regulation of FOXO3 by miR-155. Mediterr. J. Hematol. Infect. Dis. 2020, 12, e2020073. [Google Scholar] [CrossRef]
  153. Wang, J.; Wu, J. Role of miR-155 in breast cancer. Front. Biosci. 2012, 17, 2350–2355. [Google Scholar] [CrossRef] [PubMed]
  154. Li, X.; Dai, A.; Tran, R.; Wang, J. Identifying miRNA biomarkers for breast cancer and ovarian cancer: A text mining perspective. Breast Cancer Res. Treat. 2023, 201, 5–14. [Google Scholar] [CrossRef] [PubMed]
  155. Tang, J.; Li, S.; Zhou, Z.; Chang, W.; Wang, Y.; Mei, J.; Zhou, S. Identification and validation of key miRNAs and a microRNA-mRNA regulatory network associated with liver cancer. Cell Cycle 2024, 23, 353–368. [Google Scholar] [CrossRef] [PubMed]
  156. Illarionov, R.A.; Maltseva, A.R.; Pachuliia, O.V.; Postnikova, T.B.; Vashukova, E.S.; Popova, A.K.; Nasykhova, Y.A.; Bespalova, O.N.; Glotov, A.S. Urinary miRNA Expression in Pre-Eclampsia During Early and Mid-Pregnancy. Non-Coding RNA 2024, 10, 61. [Google Scholar] [CrossRef]
  157. Li, X.; Wang, J.; Jia, Z.; Cui, Q.; Zhang, C.; Wang, W.; Chen, P.; Ma, K.; Zhou, C. MiR-499 regulates cell proliferation and apoptosis during late-stage cardiac differentiation via Sox6 and cyclin D1. PLoS ONE 2013, 8, e74504. [Google Scholar] [CrossRef]
  158. Zhao, X.; Wang, Y.; Sun, X. The functions of microRNA-208 in the heart. Diabetes Res. Clin. Pract. 2020, 160, 108004. [Google Scholar] [CrossRef]
  159. Huang, X.H.; Li, J.L.; Li, X.Y.; Wang, S.X.; Jiao, Z.H.; Li, S.Q.; Liu, J.; Ding, J. miR-208a in Cardiac Hypertrophy and Remodeling. Front. Cardiovasc. Med. 2021, 8, 773314. [Google Scholar] [CrossRef]
  160. Sanchez Calle, A.; Kawamura, Y.; Yamamoto, Y.; Takeshita, F.; Ochiya, T. Emerging roles of long non-coding RNA in cancer. Cancer Sci. 2018, 109, 2093–2100. [Google Scholar] [CrossRef]
  161. Li, Y.; Xu, J.; Guo, X.; Li, Z.; Cao, L.; Liu, S.; Guo, Y.; Wang, G.; Luo, Y.; Zhang, Z.; et al. The collateral activity of RfxCas13d can induce lethality in a RfxCas13d knock-in mouse model. Genome Biol. 2023, 24, 20. [Google Scholar] [CrossRef]
  162. Ning, L.; Li, Z.; Wei, D.; Chen, H.; Yang, C. LncRNA, NEAT1 is a prognosis biomarker and regulates cancer progression via epithelial-mesenchymal transition in clear cell renal cell carcinoma. Cancer Biomark. 2017, 19, 75–83. [Google Scholar] [CrossRef] [PubMed]
  163. Chang, J.; Zhang, L.; Li, Z.; Qian, C.; Du, J. Exosomal non-coding RNAs (ncRNAs) as potential biomarkers in tumor early diagnosis. Biochim. Biophys. Acta (BBA)-Rev. Cancer 2024, 1879, 189188. [Google Scholar] [CrossRef] [PubMed]
  164. Wang, C.; Liu, T.; Wang, J.; Cheng, C.; Zhang, Z.; Zhang, J.; Huang, H.; Li, Y. CircHIPK3 negatively regulates autophagy by blocking VCP binding to the Beclin 1 complex in bladder cancer. Discov. Oncol. 2023, 14, 86. [Google Scholar] [CrossRef] [PubMed]
  165. Qi, L.; Sun, B.; Yang, B.; Lu, S. circHIPK3 (hsa_circ_0000284) Promotes Proliferation, Migration and Invasion of Breast Cancer Cells via miR-326. Onco Targets Ther. 2021, 14, 3671–3685. [Google Scholar] [CrossRef]
  166. Yan, Y.; Su, M.; Qin, B. CircHIPK3 promotes colorectal cancer cells proliferation and metastasis via modulating of miR-1207-5p/FMNL2 signal. Biochem. Biophys. Res. Commun. 2020, 524, 839–846. [Google Scholar] [CrossRef]
  167. Liu, F.; Fan, Y.; Ou, L.; Li, T.; Fan, J.; Duan, L.; Yang, J.; Luo, C.; Wu, X. CircHIPK3 Facilitates the G2/M Transition in Prostate Cancer Cells by Sponging miR-338-3p. Onco Targets Ther. 2020, 13, 4545–4558. [Google Scholar] [CrossRef]
  168. Liu, Y.; Luo, X.; Chen, W.; Dong, Z.; Cheng, T.; Chen, L.; Ju, L.; Cai, W.; Bian, Z. Hsa_circ_0079875 functions as a competitive endogenous RNA to promote hepatocellular carcinoma progression. Cell Cycle 2024, 23, 519–536. [Google Scholar] [CrossRef]
  169. Zhang, D.; Zhou, Y.; Jiao, C.; Kong, H.; Zhao, Z.; Li, Y. Circ_0050444 represses esophageal squamous cell carcinoma progression through sponging miR-486-3p to upregulate C10orf91. Cell Cycle 2024, 23, 693–702. [Google Scholar] [CrossRef]
  170. Zhou, X.; Liu, J.; Meng, A.; Zhang, L.; Wang, M.; Fan, H.; Peng, W.; Lu, J. Gastric juice piR-1245: A promising prognostic biomarker for gastric cancer. J. Clin. Lab. Anal. 2020, 34, e23131. [Google Scholar] [CrossRef]
  171. Cheng, J.; Guo, J.M.; Xiao, B.X.; Miao, Y.; Jiang, Z.; Zhou, H.; Li, Q.N. piRNA, the new non-coding RNA, is aberrantly expressed in human cancer cells. Clin. Chim. Acta 2011, 412, 1621–1625. [Google Scholar] [CrossRef]
  172. Mai, D.; Zheng, Y.; Guo, H.; Ding, P.; Bai, R.; Li, M.; Ye, Y.; Zhang, J.; Huang, X.; Liu, D.; et al. Serum piRNA-54265 is a New Biomarker for early detection and clinical surveillance of Human Colorectal Cancer. Theranostics 2020, 10, 8468–8478. [Google Scholar] [CrossRef] [PubMed]
  173. Shan, Y.; Wei, S.; Xiang, X.; Dai, S.; Cui, W.; Zhao, R.; Zhang, C.; Zhang, W.; Zhao, L.; Shan, B. SNORA42 promotes oesophageal squamous cell carcinoma development through triggering the DHX9/p65 axis. Genomics 2021, 113, 3015–3029. [Google Scholar] [CrossRef]
  174. Zheng, D.; Zhang, J.; Ni, J.; Luo, J.; Wang, J.; Tang, L.; Zhang, L.; Wang, L.; Xu, J.; Su, B.; et al. Small nucleolar RNA 78 promotes the tumorigenesis in non-small cell lung cancer. J. Exp. Clin. Cancer Res. 2015, 34, 49. [Google Scholar] [CrossRef] [PubMed]
  175. Zhang, Y.; Shang, X.; Yu, M.; Bi, Z.; Wang, K.; Zhang, Q.; Xie, L.; Song, X.; Song, X. A three-snoRNA signature: SNORD15A, SNORD35B and SNORD60 as novel biomarker for renal cell carcinoma. Cancer Cell Int. 2023, 23, 136. [Google Scholar] [CrossRef]
  176. Zhou, M.; He, X.; Zhang, J.; Mei, C.; Zhong, B.; Ou, C. tRNA-derived small RNAs in human cancers: Roles, mechanisms, and clinical application. Mol. Cancer 2024, 23, 76. [Google Scholar] [CrossRef]
  177. Jin, F.; Yang, L.; Wang, W.; Yuan, N.; Zhan, S.; Yang, P.; Chen, X.; Ma, T.; Wang, Y. A novel class of tsRNA signatures as biomarkers for diagnosis and prognosis of pancreatic cancer. Mol. Cancer 2021, 20, 95. [Google Scholar] [CrossRef]
  178. Zhan, S.; Yang, P.; Zhou, S.; Xu, Y.; Xu, R.; Liang, G.; Zhang, C.; Chen, X.; Yang, L.; Jin, F.; et al. Serum mitochondrial tsRNA serves as a novel biomarker for hepatocarcinoma diagnosis. Front. Med. 2022, 16, 216–226. [Google Scholar] [CrossRef]
  179. Wei, H.; Zhou, B.; Zhang, F.; Tu, Y.; Hu, Y.; Zhang, B.; Zhai, Q. Profiling and identification of small rDNA-derived RNAs and their potential biological functions. PLoS ONE 2013, 8, e56842. [Google Scholar] [CrossRef]
  180. Diamantopoulos, M.A.; Georgoulia, K.K.; Levis, P.; Kotronopoulos, G.; Stravodimos, K.; Kontos, C.K.; Avgeris, M.; Scorilas, A. 28S rRNA-Derived Fragments Represent an Independent Molecular Predictor of Short-Term Relapse in Prostate Cancer. Int. J. Mol. Sci. 2023, 25, 239. [Google Scholar] [CrossRef]
  181. Lobera, E.S.; Varela, M.A.; Jimenez, R.L.; Moreno, R.B. miRNA as biomarker in lung cancer. Mol. Biol. Rep. 2023, 50, 9521–9527. [Google Scholar] [CrossRef]
  182. Zen, K.; Zhang, C.Y. Circulating microRNAs: A novel class of biomarkers to diagnose and monitor human cancers. Med. Res. Rev. 2012, 32, 326–348. [Google Scholar] [CrossRef]
  183. Chen, X.; Ba, Y.; Ma, L.; Cai, X.; Yin, Y.; Wang, K.; Guo, J.; Zhang, Y.; Chen, J.; Guo, X.; et al. Characterization of microRNAs in serum: A novel class of biomarkers for diagnosis of cancer and other diseases. Cell Res. 2008, 18, 997–1006. [Google Scholar] [CrossRef]
  184. Lawrie, C.H.; Gal, S.; Dunlop, H.M.; Pushkaran, B.; Liggins, A.P.; Pulford, K.; Banham, A.H.; Pezzella, F.; Boultwood, J.; Wainscoat, J.S.; et al. Detection of elevated levels of tumour-associated microRNAs in serum of patients with diffuse large B-cell lymphoma. Br. J. Haematol. 2008, 141, 672–675. [Google Scholar] [CrossRef]
  185. Arroyo, J.D.; Chevillet, J.R.; Kroh, E.M.; Ruf, I.K.; Pritchard, C.C.; Gibson, D.F.; Mitchell, P.S.; Bennett, C.F.; Pogosova-Agadjanyan, E.L.; Stirewalt, D.L.; et al. Argonaute2 complexes carry a population of circulating microRNAs independent of vesicles in human plasma. Proc. Natl. Acad. Sci. USA 2011, 108, 5003–5008. [Google Scholar] [CrossRef]
  186. Li, C.; Ni, Y.Q.; Xu, H.; Xiang, Q.Y.; Zhao, Y.; Zhan, J.K.; He, J.Y.; Li, S.; Liu, Y.S. Roles and mechanisms of exosomal non-coding RNAs in human health and diseases. Signal Transduct. Target. Ther. 2021, 6, 383. [Google Scholar] [CrossRef]
  187. Condrat, C.E.; Thompson, D.C.; Barbu, M.G.; Bugnar, O.L.; Boboc, A.; Cretoiu, D.; Suciu, N.; Cretoiu, S.M.; Voinea, S.C. miRNAs as Biomarkers in Disease: Latest Findings Regarding Their Role in Diagnosis and Prognosis. Cells 2020, 9, 276. [Google Scholar] [CrossRef] [PubMed]
  188. Kim, S.; Park, B.K.; Seo, J.H.; Choi, J.; Choi, J.W.; Lee, C.K.; Chung, J.B.; Park, Y.; Kim, D.W. Carbohydrate antigen 19-9 elevation without evidence of malignant or pancreatobiliary diseases. Sci. Rep. 2020, 10, 8820. [Google Scholar] [CrossRef] [PubMed]
  189. Shinkins, B.; Nicholson, B.D.; Primrose, J.; Perera, R.; James, T.; Pugh, S.; Mant, D. The diagnostic accuracy of a single CEA blood test in detecting colorectal cancer recurrence: Results from the FACS trial. PLoS ONE 2017, 12, e0171810. [Google Scholar] [CrossRef] [PubMed]
  190. Wang, Z.; Yang, H.; Ma, D.; Mu, Y.; Tan, X.; Hao, Q.; Feng, L.; Liang, J.; Xin, W.; Chen, Y.; et al. Serum PIWI-Interacting RNAs piR-020619 and piR-020450 Are Promising Novel Biomarkers for Early Detection of Colorectal Cancer. Cancer Epidemiol. Biomark. Prev. 2020, 29, 990–998. [Google Scholar] [CrossRef]
  191. Zhang, Y.; Zhang, Y.; Yin, Y.; Li, S. Detection of circulating exosomal miR-17-5p serves as a novel non-invasive diagnostic marker for non-small cell lung cancer patients. Pathol. Res. Pract. 2019, 215, 152466. [Google Scholar] [CrossRef]
  192. Min, L.; Zhu, T.; Lv, B.; An, T.; Zhang, Q.; Shang, Y.; Yu, Z.; Zheng, L.; Wang, Q. Exosomal LncRNA RP5-977B1 as a novel minimally invasive biomarker for diagnosis and prognosis in non-small cell lung cancer. Int. J. Clin. Oncol. 2022, 27, 1013–1024. [Google Scholar] [CrossRef]
  193. Witwer, K.W. Circulating microRNA biomarker studies: Pitfalls and potential solutions. Clin. Chem. 2015, 61, 56–63. [Google Scholar] [CrossRef]
  194. Shi, E.; Ye, J.; Zhang, R.; Ye, S.; Zhang, S.; Wang, Y.; Cao, Y.; Dai, W. A Combination of circRNAs as a Diagnostic Tool for Discrimination of Papillary Thyroid Cancer. Onco Targets Ther. 2020, 13, 4365–4372. [Google Scholar] [CrossRef] [PubMed]
  195. Eshraghi, R.; Shafie, D.; Raisi, A.; Goleij, P.; Mirzaei, H. Circular RNAs: A small piece in the heart failure puzzle. Funct. Integr. Genomics 2024, 24, 102. [Google Scholar] [CrossRef] [PubMed]
  196. Li, B.; Wang, K.; Cheng, W.; Fang, B.; Li, Y.H.; Yang, S.M.; Zhang, M.H.; Wang, Y.H.; Wang, K. Recent advances of PIWI-interacting RNA in cardiovascular diseases. Clin. Transl. Med. 2024, 14, e1770. [Google Scholar] [CrossRef] [PubMed]
  197. Eldakhakhny, B.; Sutaih, A.M.; Siddiqui, M.A.; Aqeeli, Y.M.; Awan, A.Z.; Alsayegh, M.Y.; Elsamanoudy, S.A.; Elsamanoudy, A. Exploring the role of noncoding RNAs in cancer diagnosis, prognosis, and precision medicine. Non-Coding RNA Res. 2024, 9, 1315–1323. [Google Scholar] [CrossRef]
  198. Yerukala Sathipati, S.; Tsai, M.J.; Shukla, S.K.; Ho, S.Y. Artificial intelligence-driven pan-cancer analysis reveals miRNA signatures for cancer stage prediction. HGG Adv. 2023, 4, 100190. [Google Scholar] [CrossRef]
  199. Kumar, S.; Vijayan, M.; Bhatti, J.S.; Reddy, P.H. MicroRNAs as Peripheral Biomarkers in Aging and Age-Related Diseases. Prog. Mol. Biol. Transl. Sci. 2017, 146, 47–94. [Google Scholar] [CrossRef]
  200. Sauer, J.M.; Porter, A.C. Qualification of translational safety biomarkers. Exp. Biol. Med. 2021, 246, 2391–2398. [Google Scholar] [CrossRef]
  201. Nappi, F. Non-Coding RNA-Targeted Therapy: A State-of-the-Art Review. Int. J. Mol. Sci. 2024, 25, 3630. [Google Scholar] [CrossRef]
  202. Rupaimoole, R.; Slack, F.J. MicroRNA therapeutics: Towards a new era for the management of cancer and other diseases. Nat. Rev. Drug Discov. 2017, 16, 203–222. [Google Scholar] [CrossRef] [PubMed]
  203. Winkle, M.; El-Daly, S.M.; Fabbri, M.; Calin, G.A. Noncoding RNA therapeutics—challenges and potential solutions. Nat. Rev. Drug Discov. 2021, 20, 629–651. [Google Scholar] [CrossRef] [PubMed]
  204. Iacomino, G. miRNAs: The Road from Bench to Bedside. Genes 2023, 14, 314. [Google Scholar] [CrossRef]
  205. Renganathan, A.; Felley-Bosco, E. Long Noncoding RNAs in Cancer and Therapeutic Potential. In Long Non Coding RNA Biology; Rao, M.R.S., Ed.; Springer: Singapore, 2017; pp. 199–222. [Google Scholar]
  206. Coan, M.; Haefliger, S.; Ounzain, S.; Johnson, R. Targeting and engineering long non-coding RNAs for cancer therapy. Nat. Rev. Genet. 2024, 25, 578–595. [Google Scholar] [CrossRef] [PubMed]
  207. Hong, D.S.; Kang, Y.K.; Borad, M.; Sachdev, J.; Ejadi, S.; Lim, H.Y.; Brenner, A.J.; Park, K.; Lee, J.L.; Kim, T.Y.; et al. Phase 1 study of MRX34, a liposomal miR-34a mimic, in patients with advanced solid tumours. Br. J. Cancer 2020, 122, 1630–1637. [Google Scholar] [CrossRef]
  208. Chioccioli, M.; Roy, S.; Newell, R.; Pestano, L.; Dickinson, B.; Rigby, K.; Herazo-Maya, J.; Jenkins, G.; Ian, S.; Saini, G.; et al. A lung targeted miR-29 mimic as a therapy for pulmonary fibrosis. EBioMedicine 2022, 85, 104304. [Google Scholar] [CrossRef]
  209. Gallant-Behm, C.L.; Piper, J.; Lynch, J.M.; Seto, A.G.; Hong, S.J.; Mustoe, T.A.; Maari, C.; Pestano, L.A.; Dalby, C.M.; Jackson, A.L.; et al. A MicroRNA-29 Mimic (Remlarsen) Represses Extracellular Matrix Expression and Fibroplasia in the Skin. J. Investig. Dermatol. 2019, 139, 1073–1081. [Google Scholar] [CrossRef]
  210. Seto, A.G.; Beatty, X.; Lynch, J.M.; Hermreck, M.; Tetzlaff, M.; Duvic, M.; Jackson, A.L. Cobomarsen, an oligonucleotide inhibitor of miR-155, co-ordinately regulates multiple survival pathways to reduce cellular proliferation and survival in cutaneous T-cell lymphoma. Br. J. Haematol. 2018, 183, 428–444. [Google Scholar] [CrossRef]
  211. Gutschner, T.; Hämmerle, M.; Eissmann, M.; Hsu, J.; Kim, Y.; Hung, G.; Revenko, A.; Arun, G.; Stentrup, M.; Gross, M.; et al. The noncoding RNA MALAT1 is a critical regulator of the metastasis phenotype of lung cancer cells. Cancer Res. 2013, 73, 1180–1189. [Google Scholar] [CrossRef]
  212. Dhuri, K.; Bechtold, C.; Quijano, E.; Pham, H.; Gupta, A.; Vikram, A.; Bahal, R. Antisense Oligonucleotides: An Emerging Area in Drug Discovery and Development. J. Clin. Med. 2020, 9, 2004. [Google Scholar] [CrossRef]
  213. Khaleel, A.Q.; Jasim, S.A.; Menon, S.V.; Kaur, M.; Sivaprasad, G.V.; Rab, S.O.; Hjazi, A.; Kumar, A.; Husseen, B.; Mustafa, Y.F. siRNA-based knockdown of lncRNAs: A new modality to target tumor progression. Pathol. Res. Pract. 2025, 266, 155746. [Google Scholar] [CrossRef]
  214. Dana, H.; Chalbatani, G.M.; Mahmoodzadeh, H.; Karimloo, R.; Rezaiean, O.; Moradzadeh, A.; Mehmandoost, N.; Moazzen, F.; Mazraeh, A.; Marmari, V.; et al. Molecular Mechanisms and Biological Functions of siRNA. Int. J. Biomed. Sci. IJBS 2017, 13, 48–57. [Google Scholar] [CrossRef] [PubMed]
  215. Chen, J.; Dai, X.; Yu, H.; Pe, F.; Chen, L. SiRNA-mediated lncRNA ANRIL knockdown enhances the sensitivity of gastric cancer cells to doxorubicin. Iran. Red Crescent Med. J. (IRCMJ) 2020, 22, e120. [Google Scholar] [CrossRef]
  216. Dasgupta, I.; Chatterjee, A. Recent Advances in miRNA Delivery Systems. Methods Protoc. 2021, 4, 10. [Google Scholar] [CrossRef] [PubMed]
  217. Allen, T.M.; Cullis, P.R. Liposomal drug delivery systems: From concept to clinical applications. Adv. Drug Deliv. Rev. 2013, 65, 36–48. [Google Scholar] [CrossRef]
  218. Gokita, K.; Inoue, J.; Ishihara, H.; Kojima, K.; Inazawa, J. Therapeutic Potential of LNP-Mediated Delivery of miR-634 for Cancer Therapy. Mol. Ther. Nucleic Acids 2020, 19, 330–338. [Google Scholar] [CrossRef]
  219. Seo, Y.E.; Suh, H.W.; Bahal, R.; Josowitz, A.; Zhang, J.; Song, E.; Cui, J.; Noorbakhsh, S.; Jackson, C.; Bu, T.; et al. Nanoparticle-mediated intratumoral inhibition of miR-21 for improved survival in glioblastoma. Biomaterials 2019, 201, 87–98. [Google Scholar] [CrossRef]
  220. Yin, L.; Keeler, G.D.; Zhang, Y.; Hoffman, B.E.; Ling, C.; Qing, K.; Srivastava, A. AAV3-miRNA vectors for growth suppression of human hepatocellular carcinoma cells in vitro and human liver tumors in a murine xenograft model in vivo. Gene Ther. 2021, 28, 422–434. [Google Scholar] [CrossRef]
  221. Kobayashi, M.; Sawada, K.; Miyamoto, M.; Shimizu, A.; Yamamoto, M.; Kinose, Y.; Nakamura, K.; Kawano, M.; Kodama, M.; Hashimoto, K.; et al. Exploring the potential of engineered exosomes as delivery systems for tumor-suppressor microRNA replacement therapy in ovarian cancer. Biochem. Biophys. Res. Commun. 2020, 527, 153–161. [Google Scholar] [CrossRef]
  222. Jeong, M.; Lee, Y.; Park, J.; Jung, H.; Lee, H. Lipid nanoparticles (LNPs) for in vivo RNA delivery and their breakthrough technology for future applications. Adv. Drug Deliv. Rev. 2023, 200, 114990. [Google Scholar] [CrossRef]
  223. Urits, I.; Swanson, D.; Swett, M.C.; Patel, A.; Berardino, K.; Amgalan, A.; Berger, A.A.; Kassem, H.; Kaye, A.D.; Viswanath, O. A Review of Patisiran (ONPATTRO®) for the Treatment of Polyneuropathy in People with Hereditary Transthyretin Amyloidosis. Neurol. Ther. 2020, 9, 301–315. [Google Scholar] [CrossRef]
  224. Suzuki, Y.; Ishihara, H. Difference in the lipid nanoparticle technology employed in three approved siRNA (Patisiran) and mRNA (COVID-19 vaccine) drugs. Drug Metab. Pharmacokinet. 2021, 41, 100424. [Google Scholar] [CrossRef]
  225. Manturthi, S.; El-Sahli, S.; Bo, Y.; Durocher, E.; Kirkby, M.; Popatia, A.; Mediratta, K.; Daniel, R.; Lee, S.-H.; Iqbal, U.; et al. Nanoparticles Codelivering mRNA and SiRNA for Simultaneous Restoration and Silencing of Gene/Protein Expression In Vitro and In Vivo. ACS Nanosci. Au 2024, 4, 416–425. [Google Scholar] [CrossRef]
  226. Morán, L.; Woitok, M.M.; Bartneck, M.; Cubero, F.J. Hepatocyte-Directed Delivery of Lipid-Encapsulated Small Interfering RNA. Methods Mol. Biol. 2022, 2544, 95–106. [Google Scholar] [CrossRef] [PubMed]
  227. Paunovska, K.; Da Silva Sanchez, A.J.; Lokugamage, M.P.; Loughrey, D.; Echeverri, E.S.; Cristian, A.; Hatit, M.Z.C.; Santangelo, P.J.; Zhao, K.; Dahlman, J.E. The Extent to Which Lipid Nanoparticles Require Apolipoprotein E and Low-Density Lipoprotein Receptor for Delivery Changes with Ionizable Lipid Structure. Nano Lett. 2022, 22, 10025–10033. [Google Scholar] [CrossRef] [PubMed]
  228. Wong, B.; Birtch, R.; Rezaei, R.; Jamieson, T.; Crupi, M.J.F.; Diallo, J.S.; Ilkow, C.S. Optimal delivery of RNA interference by viral vectors for cancer therapy. Mol. Ther. J. Am. Soc. Gene Ther. 2023, 31, 3127–3145. [Google Scholar] [CrossRef] [PubMed]
  229. Kalluri, R.; LeBleu, V.S. The biology, function, and biomedical applications of exosomes. Science 2020, 367, eaau6977. [Google Scholar] [CrossRef]
  230. Wu, H.; Hui, Y.; Qian, X.; Wang, X.; Xu, J.; Wang, F.; Pan, S.; Chen, K.; Liu, Z.; Gao, W.; et al. Exosomes derived from mesenchymal stem cells ameliorate impaired glucose metabolism in myocardial Ischemia/reperfusion injury through miR-132-3p/PTEN/AKT pathway. Cell Cycle 2024, 23, 893–912. [Google Scholar] [CrossRef]
  231. Zimmermann, T.S.; Karsten, V.; Chan, A.; Chiesa, J.; Boyce, M.; Bettencourt, B.R.; Hutabarat, R.; Nochur, S.; Vaishnaw, A.; Gollob, J. Clinical Proof of Concept for a Novel Hepatocyte-Targeting GalNAc-siRNA Conjugate. Mol. Ther. J. Am. Soc. Gene Ther. 2017, 25, 71–78. [Google Scholar] [CrossRef]
  232. Zhang, L.; Liang, Y.; Liang, G.; Tian, Z.; Zhang, Y.; Liu, Z.; Ji, X. The therapeutic prospects of N-acetylgalactosamine-siRNA conjugates. Front. Pharmacol. 2022, 13, 1090237. [Google Scholar] [CrossRef]
  233. Guo, S.; Zhang, M.; Huang, Y. Three ‘E’ challenges for siRNA drug development. Trends Mol. Med. 2024, 30, 13–24. [Google Scholar] [CrossRef] [PubMed]
  234. Robbins, M.; Judge, A.; MacLachlan, I. siRNA and innate immunity. Oligonucleotides 2009, 19, 89–102. [Google Scholar] [CrossRef] [PubMed]
  235. Bramsen, J.B.; Laursen, M.B.; Nielsen, A.F.; Hansen, T.B.; Bus, C.; Langkjaer, N.; Babu, B.R.; Højland, T.; Abramov, M.; Van Aerschot, A.; et al. A large-scale chemical modification screen identifies design rules to generate siRNAs with high activity, high stability and low toxicity. Nucleic Acids Res. 2009, 37, 2867–2881. [Google Scholar] [CrossRef] [PubMed]
  236. Fakhr, E.; Zare, F.; Teimoori-Toolabi, L. Precise and efficient siRNA design: A key point in competent gene silencing. Cancer Gene Ther. 2016, 23, 73–82. [Google Scholar] [CrossRef]
  237. Dowdy, S.F. Endosomal escape of RNA therapeutics: How do we solve this rate-limiting problem? RNA 2023, 29, 396–401. [Google Scholar] [CrossRef]
  238. Judge, A.D.; Sood, V.; Shaw, J.R.; Fang, D.; McClintock, K.; MacLachlan, I. Sequence-dependent stimulation of the mammalian innate immune response by synthetic siRNA. Nat. Biotechnol. 2005, 23, 457–462. [Google Scholar] [CrossRef]
  239. Ledford, H.; Callaway, E. Pioneers of revolutionary CRISPR gene editing win chemistry Nobel. Nature 2020, 586, 346–347. [Google Scholar] [CrossRef]
  240. Liu, G.; Lin, Q.; Jin, S.; Gao, C. The CRISPR-Cas toolbox and gene editing technologies. Mol. Cell 2022, 82, 333–347. [Google Scholar] [CrossRef]
  241. Hazan, J.; Bester, A.C. CRISPR-Based Approaches for the High-Throughput Characterization of Long Non-Coding RNAs. Non-Coding RNA 2021, 7, 79. [Google Scholar] [CrossRef]
  242. Goodall, G.J.; Wickramasinghe, V.O. RNA in cancer. Nat. Rev. Cancer 2021, 21, 22–36. [Google Scholar] [CrossRef]
  243. Boti, M.A.; Athanasopoulou, K.; Adamopoulos, P.G.; Sideris, D.C.; Scorilas, A. Recent Advances in Genome-Engineering Strategies. Genes 2023, 14, 129. [Google Scholar] [CrossRef]
  244. Allemailem, K.S.; Rahmani, A.H.; Almansour, N.M.; Aldakheel, F.M.; Albalawi, G.M.; Albalawi, G.M.; Khan, A.A. Current updates on the structural and functional aspects of the CRISPR/Cas13 system for RNA targeting and editing: A next-generation tool for cancer management (Review). Int. J. Oncol. 2025, 66, 42. [Google Scholar] [CrossRef]
  245. Liang, W.-W.; Müller, S.; Hart, S.K.; Wessels, H.-H.; Méndez-Mancilla, A.; Sookdeo, A.; Choi, O.; Caragine, C.M.; Corman, A.; Lu, L.; et al. Transcriptome-scale RNA-targeting CRISPR screens reveal essential lncRNAs in human cells. Cell 2024, 187, 7637–7654.e7629. [Google Scholar] [CrossRef]
  246. Lucere, K.M.; O’Malley, M.M.R.; Diermeier, S.D. Functional Screening Techniques to Identify Long Non-Coding RNAs as Therapeutic Targets in Cancer. Cancers 2020, 12, 3695. [Google Scholar] [CrossRef]
  247. Pickar-Oliver, A.; Gersbach, C.A. The next generation of CRISPR-Cas technologies and applications. Nat. Rev. Mol. Cell Biol. 2019, 20, 490–507. [Google Scholar] [CrossRef]
  248. Therapeutics, H. HuidaGene Therapeutics Announced First Patient Dosed in the HERO Clinical Trial of HG204 for MECP2 Duplication Syndrome. Available online: https://www.huidagene.com/new/news/71.html (accessed on 14 September 2025).
  249. Paul, A.; Muralidharan, A.; Biswas, A.; Kamath, B.V.; Joseph, A.; Alex, A.T. siRNA therapeutics and its challenges: Recent advances in effective delivery for cancer therapy. OpenNano 2022, 7, 100063. [Google Scholar] [CrossRef]
  250. Thomas, C.J.; Mirza, R.G.; Gill, M.K. Age-Related Macular Degeneration. Med. Clin. N. Am. 2021, 105, 473–491. [Google Scholar] [CrossRef] [PubMed]
  251. Therapeutics, H. HuidaGene Therapeutics Receives the First-Ever FDA Clearance of CRISPR/Cas13 RNA-Editing HG202 for Macular Degeneration. 2024. Available online: https://www.huidagene.com/new/news/70 (accessed on 14 September 2025).
Figure 1. Biogenesis and functional roles of major ncRNAs. This figure illustrates the biogenesis pathways and mechanisms of action of microRNAs (miRNAs) (A), circular RNAs (circRNAs) (B), and long non-coding RNAs (lncRNAs) (C). It depicts key proteins and molecular complexes involved at each step, including Drosha, Dicer, Exportin-5, Argonaute, and RNA-induced silencing complex (RISC). The pathways demonstrate how primary transcripts are processed into mature ncRNAs, their cellular localization, and their functional roles in gene expression regulation, including transcriptional regulation, gene silencing, miRNA sponging, and scaffold formation. Overall, the figure highlights the essential contributions of ncRNAs in maintaining cellular homeostasis through transcriptional, post-transcriptional, and translational regulation.
Figure 1. Biogenesis and functional roles of major ncRNAs. This figure illustrates the biogenesis pathways and mechanisms of action of microRNAs (miRNAs) (A), circular RNAs (circRNAs) (B), and long non-coding RNAs (lncRNAs) (C). It depicts key proteins and molecular complexes involved at each step, including Drosha, Dicer, Exportin-5, Argonaute, and RNA-induced silencing complex (RISC). The pathways demonstrate how primary transcripts are processed into mature ncRNAs, their cellular localization, and their functional roles in gene expression regulation, including transcriptional regulation, gene silencing, miRNA sponging, and scaffold formation. Overall, the figure highlights the essential contributions of ncRNAs in maintaining cellular homeostasis through transcriptional, post-transcriptional, and translational regulation.
Labmed 02 00017 g001
Figure 2. Nanoparticles targeting oncogenic lncRNA HOTAIR for cancer therapy. This figure depicts the design and therapeutic application of engineered nanoparticles developed to target the oncogenic lncRNA HOTAIR in cancer. The nanoparticle is functionalized with ligands for αβ integrin receptors, enabling selective binding and uptake by cancer cells via receptor-mediated endocytosis. Upon internalization, the nanoparticles promote endosomal escape, releasing siRNA molecules (siHOTAIR) into the cytoplasm. These siRNAs recruit the RNA-induced silencing complex (RISC) to specifically degrade HOTAIR transcripts, resulting in inhibition of oncogenic pathways including histone trimethylation, epithelial–mesenchymal transition (EMT), and kinase activation. The therapeutic effect is evidenced by reduced tumor growth in mouse xenograft models. This delivery system exemplifies a targeted, RNA-based therapeutic approach with promising potential in clinical applications.
Figure 2. Nanoparticles targeting oncogenic lncRNA HOTAIR for cancer therapy. This figure depicts the design and therapeutic application of engineered nanoparticles developed to target the oncogenic lncRNA HOTAIR in cancer. The nanoparticle is functionalized with ligands for αβ integrin receptors, enabling selective binding and uptake by cancer cells via receptor-mediated endocytosis. Upon internalization, the nanoparticles promote endosomal escape, releasing siRNA molecules (siHOTAIR) into the cytoplasm. These siRNAs recruit the RNA-induced silencing complex (RISC) to specifically degrade HOTAIR transcripts, resulting in inhibition of oncogenic pathways including histone trimethylation, epithelial–mesenchymal transition (EMT), and kinase activation. The therapeutic effect is evidenced by reduced tumor growth in mouse xenograft models. This delivery system exemplifies a targeted, RNA-based therapeutic approach with promising potential in clinical applications.
Labmed 02 00017 g002
Figure 3. Diverse CRISPR-Cas13 systems for RNA editing and their applications. This figure illustrates the emergent CRISPR-Cas13-based RNA-targeting technologies utilizing distinct Cas13 variants: (A) RNA Silencing: The native Cas13, guided by a specific guide RNA (gRNA), cleaves target RNA transcripts, enabling transient knockdown without genomic alteration. (B) RNA Editing: The catalytically inactive dCas13 fused to an adenosine deaminase acting on RNA (ADAR) domain mediates site-specific base editing (A-to-I conversion) in target RNAs, allowing post-transcriptional correction of mutations. (C) RNA Tracking: dCas13 fused with a fluorescent protein (e.g., GFP) permits real-time detection of endogenous RNA molecules within live cells, facilitating studies of RNA localization and dynamics. (D) Nucleic Acid Detection: CRISPR-dCas13 system can be exploited for the sensitive and specific detection of viral lncRNAs, providing a powerful tool for rapid molecular diagnostics. Together, these systems demonstrate the versatility of CRISPR-Cas13 technologies for RNA manipulation, imaging, and detection, with promising implications for research and therapeutic development.
Figure 3. Diverse CRISPR-Cas13 systems for RNA editing and their applications. This figure illustrates the emergent CRISPR-Cas13-based RNA-targeting technologies utilizing distinct Cas13 variants: (A) RNA Silencing: The native Cas13, guided by a specific guide RNA (gRNA), cleaves target RNA transcripts, enabling transient knockdown without genomic alteration. (B) RNA Editing: The catalytically inactive dCas13 fused to an adenosine deaminase acting on RNA (ADAR) domain mediates site-specific base editing (A-to-I conversion) in target RNAs, allowing post-transcriptional correction of mutations. (C) RNA Tracking: dCas13 fused with a fluorescent protein (e.g., GFP) permits real-time detection of endogenous RNA molecules within live cells, facilitating studies of RNA localization and dynamics. (D) Nucleic Acid Detection: CRISPR-dCas13 system can be exploited for the sensitive and specific detection of viral lncRNAs, providing a powerful tool for rapid molecular diagnostics. Together, these systems demonstrate the versatility of CRISPR-Cas13 technologies for RNA manipulation, imaging, and detection, with promising implications for research and therapeutic development.
Labmed 02 00017 g003
Table 1. Classification of ncRNAs and their functions.
Table 1. Classification of ncRNAs and their functions.
ncRNA ClassMolecular MechanismsKey Physiological RolesExamples
miRNAsPost-transcriptional repression via mRNA degradation or translational inhibitionCell proliferation, apoptosis,
differentiation, immune regulation, stress response
miR-21, miR-155, let-7 family
lncRNAsChromatin remodeling, transcriptional repression/activation, molecular scaffolding, ceRNA activity, miRNA spongingStem cell maintenance, X-inactivation, DNA repair, epigenetic regulationHOTAIR, MALAT1, XIST
circRNAsmiRNA sponging, interaction with RNA-binding proteins, transcriptional regulationTissue development, neuronal plasticity, gene expression
fine-tuning
circHIPK3, circZNF609, CDR1as
piRNAsSilencing of transposable
elements via PIWI complex, maintenance
of genomic integrity
Germline maintenance, genome stability, spermatogenesispiR-823, piR-932
snoRNAsSite-specific modification of rRNAs (2′-O-methylation, pseudouridylation),
alternative splicing regulation
Ribosome biogenesis, mRNA splicing, cellular homeostasisSNORD66, SNORA73B
tRFsRegulation of translation initiation,
modulation of stress granules, interference with reverse transcription
Stress response, intercellular communication, viral defensetRF-5Glu, tRF-Leu-CAG
rRFsPost-transcriptional repression via
AGO proteins interaction
Proliferation control, differentiation, antiviral response28s5-rtsRNA, rRF-28S-3′C, rRF-18S-5′F
Table 2. Representative ncRNAs that are used as biomarkers in human diseases.
Table 2. Representative ncRNAs that are used as biomarkers in human diseases.
ncRNA ClassncRNAType of Biomarker/FunctionAssociated Disease(s)
miRNAmiR-21Oncogenic, diagnostic, prognostic markerVarious cancers (breast, lung, colon); targets PTEN, TPM1, PDCD4
miR-133/miR-1Cardioprotective diagnostic biomarkersCardiovascular diseases (cardiac hypertrophy, heart failure)
miR-29Diagnostic biomarkerAlzheimer’s disease; regulates BACE1 and Aβ production
miR-146a/
miR-155
Immune-modulatory biomarkersAutoimmune diseases (SLE, RA); modulate NF-κB, IFN signaling
lncRNAHOTAIRPrognostic biomarker; epigenetic regulatorMultiple cancers (breast, colorectal)
MALAT1Prognostic biomarkerLung, liver, breast cancers
ANRILDiagnostic biomarkerAtherosclerosis, coronary artery disease
MeXisDiagnostic biomarkerAtherosclerosis
BACE1-ASDiagnostic biomarkerAlzheimer’s disease
circRNAcircHIPK3Diagnostic and prognostic biomarkerBladder cancer, diabetic retinopathy, colorectal and lung cancer
circRNA-100290Diagnostic biomarkerOral squamous cell carcinoma, colorectal cancer
circSMARCA5Diagnostic biomarkerGlioblastoma, hepatocellular carcinoma, prostate cancer
hsa-circ-0001649Diagnostic biomarkerHepatocellular carcinoma, gastric, esophageal carcinoma
circ-ZNF609Diagnostic biomarkerRhabdomyosarcoma, Duchenne muscular dystrophy, colorectal cancer
piRNApiR-823Diagnostic and prognostic biomarkerMultiple myeloma, gastric, colorectal cancer
piR-651Diagnostic biomarkerLung, gastric, breast, colorectal cancer
piR-1245Diagnostic biomarkerColorectal, liver, lung cancer
piR-55490Diagnostic biomarkerLung cancer, glioma
snoRNASNORD33/66/76Diagnostic biomarkerNon-small cell lung cancer, breast cancer
SNORD115/116Diagnostic biomarkerPrader–Willi syndrome, schizophrenia
SNORA42Diagnostic biomarkerLung, colorectal, gastric cancer
tRFtRF-5GluCTCDiagnostic biomarkerPancreatic, breast, gastric cancer
tRF-3019aDiagnostic biomarkerBreast cancer, melanoma
tRF-Leu-CAGDiagnostic biomarkerLiver, colorectal and bladder cancer
Viral ncRNAEBV BART miRNAsDiagnostic biomarkerEpstein–Barr virus-related cancers
KSHV PAN RNADiagnostic biomarkerKaposi’s sarcoma
Table 3. Comparative overview of ncRNA types as diagnostic or/and prognostic biomarkers.
Table 3. Comparative overview of ncRNA types as diagnostic or/and prognostic biomarkers.
ncRNA TypeDiagnostic StrengthPrognostic StrengthStabilityDisease
Specificity
Biofluid PresenceClinical Relevance
(Examples)
miRNAsHighHighHighModerateYesCancer, cardiovascular and neurodegenerative diseases
lncRNAsModerateHighLow/
Moderate
HighLimitedCancer,
cardiovascular diseases
circRNAsEmergingEmergingVery HighPromisingYesCancer, neurological
disorders
piRNAsEmergingLimitedHighTissue-
specific
LimitedReproductive cancers
snoRNAsLow/ModerateEmergingModerateLimitedRareCancer, metabolic
disorders
tRFsEmergingEmergingHighUnder
investigation
YesCancer, stress-related
conditions
rRFsEmergingLimitedVery HighLowYesOften used as
normalization control
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Diamantopoulos, M.A.; Boti, M.A.; Sarri, T.; Scorilas, A. Non-Coding RNAs in Health and Disease: From Biomarkers to Therapeutic Targets. LabMed 2025, 2, 17. https://doi.org/10.3390/labmed2030017

AMA Style

Diamantopoulos MA, Boti MA, Sarri T, Scorilas A. Non-Coding RNAs in Health and Disease: From Biomarkers to Therapeutic Targets. LabMed. 2025; 2(3):17. https://doi.org/10.3390/labmed2030017

Chicago/Turabian Style

Diamantopoulos, Marios A., Michaela A. Boti, Triantafyllia Sarri, and Andreas Scorilas. 2025. "Non-Coding RNAs in Health and Disease: From Biomarkers to Therapeutic Targets" LabMed 2, no. 3: 17. https://doi.org/10.3390/labmed2030017

APA Style

Diamantopoulos, M. A., Boti, M. A., Sarri, T., & Scorilas, A. (2025). Non-Coding RNAs in Health and Disease: From Biomarkers to Therapeutic Targets. LabMed, 2(3), 17. https://doi.org/10.3390/labmed2030017

Article Metrics

Back to TopTop