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Bioinformatics Analysis of RNA for Human Health and Disease

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A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Molecular Genetics and Genetic Diseases".

Deadline for manuscript submissions: closed (30 November 2025) | Viewed by 17226

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Dr. Jihwan Ha

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Major of Big Data Convergence, Division of Data Information Science, Pukyoung National University, Busan 48513, Republic of Korea
Interests: bioinformatics; computational biology; artificial intelligence; machine learning; deep learning; data mining; recommender system
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Special Issue Information

Dear Colleagues,

The rapid advancement of high-throughput technologies has enabled the generation of vast amounts of omics data, including transcriptomics, providing unprecedented opportunities to explore the molecular mechanisms underlying human disease. This wealth of data has catalyzed significant progress in understanding RNA biology and its diverse roles in cellular processes and disease pathogenesis. RNAs, including miRNAs, lncRNAs, circRNAs, and other types, have emerged as critical players in gene regulation, cellular communication, and disease progression. Over the past decade, substantial efforts have been made to identify RNA molecules as biomarkers and therapeutic targets. However, challenges such as the limited availability of experimentally validated RNA interactions and functional annotations persist. Addressing these challenges requires a combination of robust computational approaches and innovative experimental methodologies to generate and validate insights. This Special Issue aims to highlight advancements in RNA research by welcoming contributions from diverse approaches, including computational modeling, experimental biology, and interdisciplinary studies. Topics of interest include, but are not limited to, the following:

The identification and validation of RNA biomarkers across all RNA types (e.g., miRNAs, lncRNAs, circRNAs, tRNAs, and others).
Experimental techniques for studying RNA–protein and RNA–disease interactions.
Multi-omics approaches to investigate RNA functions and regulatory networks.
Single-cell transcriptomics and its application in RNA research.
The structural and functional characterization of RNAs using experimental and computational methods.
The development of therapeutic strategies targeting RNA.
The integration of computational predictions with experimental validation for RNA-related discoveries.

We encourage submissions that span the full spectrum of RNA research, from experimental studies unveiling novel biological mechanisms to data-driven approaches generating hypotheses for further investigation. This Special Issue seeks to foster collaboration and innovation in the rapidly evolving field of RNA biology, ultimately contributing to the understanding and treatment of human disease. We look forward to your contributions which, we believe, will shape the future of this exciting area of research.

Dr. Jihwan Ha
Guest Editor

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Keywords

bioinformatics
computational biology
machine learning
multi-omics data analysis
biomarker detection
biomedical AI convergence
medical and public health informatics
RNA sequencing (RNA-Seq)
functional genomics
RNA-protein interactions
experimental validation of biomarkers
RNA structural biology
transcriptome-wide association studies (TWAS)
gene expression profiling
RNA modifications (e.g., m6A, pseudouridylation)
in vitro and in vivo RNA functional studies
single-cell RNA analysis
RNA therapeutics development

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16 pages, 6943 KB

Open AccessArticle

Integration of RNA Editing into Multiomics Machine Learning Models for Predicting Drug Responses in Breast Cancer Patients

by Yanara A. Bernal, Alejandro Blanco, Karen Oróstica, Iris Delgado and Ricardo Armisén

Biomedicines 2026, 14(3), 665; https://doi.org/10.3390/biomedicines14030665 - 14 Mar 2026

Cited by 1 | Viewed by 946

Abstract

Background: The integration of multi-omics data, such as genomics and transcriptomics, into artificial intelligence models has advanced precision medicine. However, their clinical applicability remains limited due to model complexity. We integrated DNA mutation, RNA expression, and A>I(G) RNA editing data to develop a predictive model for drug response in breast cancer. Methods: We analyzed 104 patients from the Breast Cancer Genome-Guided Therapy Study (ClinicalTrials.gov: NCT02022202). Clinical variables, gene expression, tumor and germline DNA variants, and RNA editing features were integrated into machine learning models to predict therapy response. Generalized linear models (GLM), random forest (RF), and support vector machines (SVM) were trained and evaluated across multiple random 70/30 train-test splits. Feature selection was performed exclusively within the training set using LASSO regularization. Model performance was assessed using the F1-score on independent test sets. The additive effect of RNA editing was evaluated using paired comparisons across identical train/test splits. Results: We characterized the cohort using clinical, mutational, transcriptomic, and RNA editing profiles in 69 non-responders and 35 responders. Across repeated splits, adding RNA editing frequently maintained or modestly improved predictive performance, particularly in expression-based models, with paired analyses showing a statistically significant increase in F1-score. Conclusions: RNA editing represents a complementary molecular layer that can enhance multi-omic models for therapy response prediction in breast cancer, supporting further investigation of epitranscriptomic features in precision oncology. Full article

(This article belongs to the Special Issue Bioinformatics Analysis of RNA for Human Health and Disease)

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21 pages, 1837 KB

Open AccessArticle

Bioinformatic Analysis of microRNAs Associated with Chemotherapy-Induced Cognitive Impairment: Integration of Gene Networks and Neuroinflammatory Pathways

by Lucas Volpi Cândido, Marcos Otávio Bueno, Ricardo Cervini, Natan Veiga, Claudriana Locatelli, João Paulo Assolini, Gustavo Colombo Dal-Pont and Ariana Centa

Biomedicines 2026, 14(3), 594; https://doi.org/10.3390/biomedicines14030594 - 6 Mar 2026

Viewed by 752

Abstract

Background/Objectives: Neuropsychological changes induced by cancer and its treatments, especially chemotherapy, represent a significant clinical challenge, being responsible for persistent cognitive deficits known as chemobrain. This study aimed to identify microRNAs (miRNAs) associated with these alterations, map their interaction networks, and determine the main biological pathways involved. Methods: An integrative review and in silico analysis were conducted to study the role of microRNAs. Results: Six experimental studies using animal models were selected, which showed that agents such as doxorubicin, cisplatin, and methotrexate induce changes in domains such as memory, attention, and learning. Among the analyzed miRNAs, miR-155-5p, miR-21-5p, and miR-125b-5p stood out, being associated with pathways related to neuroinflammation, oxidative stress, apoptosis, and synaptic dysfunction. Computational analyses revealed that these miRNAs act on pathways such as MAPK, PI3K-Akt, mTOR, neurotrophins, and cytokine receptors. The interaction analysis among target genes also revealed a functionally connected network, with coordinated involvement in inflammation, neuronal apoptosis, and glial differentiation processes, suggesting a role in cellular stress responses and neuroinflammatory pathologies. Conclusions: These findings suggest that miRNAs play a central role in mediating the observed neurocognitive changes and may represent promising biomarkers and therapeutic targets to mitigate the effects of chemobrain. The study also highlights the need for future research integrating molecular and behavioral analyses to achieve more precise clinical applications. Full article

(This article belongs to the Special Issue Bioinformatics Analysis of RNA for Human Health and Disease)

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18 pages, 2224 KB

Open AccessArticle

The Impact of Maternal Diabetes and Hypothyroidism on Signaling Pathway Activation and Gene Expression in Fetal Mesenchymal Stem Cells

by Dominika Przywara, Wiktor Babiuch, Alicja Petniak, Bartosz Piszcz, Arkadiusz Krzyżanowski, Adrianna Kondracka, Janusz Kocki and Paulina Gil-Kulik

Biomedicines 2026, 14(2), 436; https://doi.org/10.3390/biomedicines14020436 - 14 Feb 2026

Cited by 1 | Viewed by 798

Abstract

Background: Mesenchymal stem cells (MSCs) exhibit a high capacity for differentiation, possess anti-inflammatory and proangiogenic properties, and stimulate the growth and proliferation of neighboring cells. MSCs are a promising tool in regenerative medicine. However, the molecular mechanisms underlying the properties of these cells are not yet fully understood. Gene expression in MSCs influences their characteristics and differentiation potential. Therefore, it is essential to investigate factors affecting gene expression as well as those activating signaling pathways, which will enable more effective and individualized applications of MSCs. In this study, we aimed to identify signaling pathways involved in gene expression in umbilical cord-derived MSCs (UC-MSCs) that may be altered by maternal diabetes and hypothyroidism during pregnancy. Methods: The research material consisted of UC-MSCs. Samples obtained from nine participants were analyzed. UC-MSCs were isolated and cultured, and RNA was extracted. The isolated RNA was used for microarray-based gene expression analysis. Subsequently, pathway enrichment analysis was performed to identify the signaling pathways involved. Results: In the diabetes group, 340 genes (0.71%) were upregulated, while 268 genes (0.56%) were downregulated compared with UC-MSCs from the control group. In the diabetes group, the most compact module was composed of proteins associated with WNT/planar cell polarity (WNT/PCP) signaling. The second module included genes related to smooth muscle activity. In the hypothyroidism group, an association was identified between the extracellular matrix organization pathways (GO:0030198) and the extracellular structure organization (GO:0043062) pathways. Moreover, in this group, increased expression of MMP1, MMP10, and GREM1 was observed. Conclusions: In summary, our study demonstrated the impact of diabetes and hypothyroidism on gene expression in UC-MSCs. We also observed the activation of distinct signaling pathways depending on the presence of these conditions. However, this work represents a preliminary screening, and the results should be validated by PCR in a larger cohort. Full article

(This article belongs to the Special Issue Bioinformatics Analysis of RNA for Human Health and Disease)

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17 pages, 7996 KB

Open AccessArticle

Inflammation-Mediated Immune Imbalance in the Pathogenesis of Diabetic Cataracts

by Nan Gao, Xiteng Chen, Guijia Wu, Zhenyu Kou, Jun Yang, Yuanfeng Jiang, Ruihua Wei and Fang Tian

Biomedicines 2026, 14(2), 372; https://doi.org/10.3390/biomedicines14020372 - 5 Feb 2026

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Background: Diabetes increases the risk of cataract formation fivefold. Immune-mediated inflammation has been reported to play a role in this process; however, whether alterations in the immune landscape are involved remains unknown. Therefore, we conducted a multi-omics analysis to evaluate the impact of immune inflammation on the lens. Methods: Bulk RNA sequencing was performed on peripheral blood mononuclear cells (PBMCs) from diabetic patients and lens tissues from diabetic rats. Single-cell RNA sequencing was utilized to characterize intercellular interactions. Key gene and protein expressions were validated via laboratory assays. Results: An integrated RNA-seq analysis revealed a disruption of the blood–aqueous barrier integrity in the diabetic group, enhanced monocyte migration and adhesion, increased differentiation from classical to non-classical monocytes, and the upregulation of TNF and IFN-γ signaling pathways. The transcriptomic profiling of rat lenses revealed an increased proportion of monocytes and the activation of apoptotic pathways in lens epithelial cells. Immunohistochemistry and immunofluorescence staining demonstrated elevated caspase-3 and IL-6 levels in lens epithelial cells and increased immune cell infiltration in the diabetic group. The qRT-PCR and ELISA confirmed elevated levels of the pro-inflammatory cytokines IL-6 and IFN-γ, alongside reduced anti-inflammatory cytokine IL-10 in the peripheral blood and aqueous humor of diabetic patients. Conclusions: Diabetes alters the peripheral immune microenvironment and disrupts the blood–aqueous barrier, promoting intraocular inflammation and lens epithelial cell apoptosis, thereby accelerating cataract development. Full article

(This article belongs to the Special Issue Bioinformatics Analysis of RNA for Human Health and Disease)

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18 pages, 2183 KB

Open AccessArticle

Uncovering miRNA–Disease Associations Through Graph Based Neural Network Representations

by Alessandro Orro

Biomedicines 2026, 14(2), 289; https://doi.org/10.3390/biomedicines14020289 - 28 Jan 2026

Viewed by 982

Abstract

Background: MicroRNAs (miRNAs) are an important class of non-coding RNAs that regulate gene expression by binding to target mRNAs and influencing cellular processes such as differentiation, proliferation, and apoptosis. Dysregulation in miRNA expression has been reported to be implicated in many human diseases, including cancer, cardiovascular, and neurodegenerative disorders. Identifying disease-related miRNAs is therefore essential for understanding disease mechanisms and supporting biomarker discovery, but time and cost of experimental validation are the main limitations. Methods: We present a graph-based learning framework that models the complex relationships between miRNAs, diseases, and related biological entities within a heterogeneous network. The model employs a message-passing neural architecture to learn structured embeddings from multiple node and edge types, integrating biological priors from curated resources. This network representation enables the inference of novel miRNA–disease associations, even in sparsely annotated regions of the network. The approach was trained and validated on a dataset benchmark using ten replicated experiments to ensure robustness. Results: The method achieved an average AUC–ROC of ~98%, outperforming previously reported computational approaches on the same dataset. Moreover, predictions were consistent across validation folds and robustness analyses were conducted to evaluate stability and highlight the most important information. Conclusions: Integrating heterogeneous biological information and representing it through graph neural network representation learning offers a powerful and generalizable way to predict relevant associations, including miRNA–disease, and provide a robust computational framework to support biomedical discovery and translational research. Full article

(This article belongs to the Special Issue Bioinformatics Analysis of RNA for Human Health and Disease)

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27 pages, 10800 KB

Open AccessArticle

Integrative RNA-Seq and TCGA-BRCA Analyses Highlight the Role of LINC01133 in Triple-Negative Breast Cancer

by Leandro Teodoro Júnior, Henrique César de Jesus-Ferreira, Mari Cleide Sogayar and Milton Yutaka Nishiyama-Jr.

Biomedicines 2026, 14(2), 268; https://doi.org/10.3390/biomedicines14020268 - 24 Jan 2026

Cited by 1 | Viewed by 1170

Abstract

Background: Triple-negative breast cancers (TNBCs) are among the most aggressive breast tumors, due not only to the absence of clinically functional biomarkers used in other molecular subtypes, but also their marked heterogeneity and pronounced migratory and invasive behavior. The search for new molecules of interest for risk prediction, diagnosis and therapy stems from the class of long non-coding RNAs (lncRNAs), which often display context-dependent (“dual”) functions and tissue specificity. Among them, lncRNA LINC01133 stands out for its dysregulation across cancer, although its molecular role in TNBC remains unclear. Methods: In the present study, we used the human TNBC cell line Hs578T to generate a cell panel comprising the parental line (Hs578T_wt), the control line (Hs578T_ctr), and the LINC01133 knockout line (Hs578T_ko). Subsequently, we performed bulk RNA-Seq to identify KO-associated Differentially Expressed Genes (DEGs) using ko_vs_ctr as the primary contrast. Functional interpretation was achieved by Over-Representation Analysis (ORA) using Gene Ontology. We then conducted a comparative patient-cohort analysis using TCGA-BRCA Basal-like/TNBC cases (TCGA/BRCA n = 1098; Basal-like/TNBC n = 199), classified with the AIMS algorithm, and evaluated concordance between KO-associated signatures and patient tumor expression patterns via trend-based analyses across the LINC01133 expression levels and associated genes. Results: A total of 265 KO-dominant DEGs were identified in Hs578T_ko, reflecting transcriptional changes consistent with tumor progression, with enrichment of pathways associated with LINC01133 knockout including cell adhesion, cell–cell interactions, epithelial–mesenchymal transition (EMT), and extracellular matrix (ECM) remodeling. The main DEGs included ITIH5, GLUL, CACNB2, PDX1, ASPN, PTGER3, MFAP4, PI15, EPHB6, and CPA3 with additional candidates, such as KAZN and the lncRNA gene SSC4D, which have been implicated in migration/invasion, ECM remodeling, or signaling across multiple tumor contexts. Translational analyses in TCGA-BRCA basal-like tumors suggested a descriptive association in which lower LINC01133 levels were accompanied by shifts in the expression trends of genes linked to ECM/EMT programs and modulation of genes related to cell adhesion and protease inhibition. Conclusions: These results suggest a transcriptional model in which LINC01133 is associated with TNBC-related gene expression programs in a concentration-dependent manner, with loss of LINC01133 being associated with a transcriptomic shift toward pro-migratory/ECM remodeling signatures. While functional validation is required to establish causality, these data support LINC01133 as a molecule of interest in breast cancer research. Full article

(This article belongs to the Special Issue Bioinformatics Analysis of RNA for Human Health and Disease)

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20 pages, 12843 KB

Open AccessArticle

Network Analysis to Identify MicroRNAs Involved in Alzheimer’s Disease and to Improve Drug Prioritization

by Aldo Reyna and Simona Panni

Biomedicines 2026, 14(1), 147; https://doi.org/10.3390/biomedicines14010147 - 11 Jan 2026

Cited by 1 | Viewed by 1188

Abstract

Background: Advances in the understanding of molecular mechanisms of human diseases, along with the generation of large amounts of molecular datasets, have highlighted the variability between patients and the need to tailor therapies to individual characteristics. In particular, RNA-based therapies hold strong promise for new drug development, as they can be easily designed to target specific molecules. Gene and protein functions, however, operate within a highly interconnected network, and inhibiting a single function or repressing a single gene may lead to unexpected secondary effects. In this study, we focused on genes associated with Alzheimer’s disease, a progressive neurodegenerative disorder characterized by complex pathological processes leading to cognitive decline and dementia. Its hallmark features include the accumulation of extracellular amyloid-β plaques and intracellular neurofibrillary tangles composed of hyperphosphorylated tau. Methods: We built a protein interaction network subgraph seeded on five Alzheimer’s-associated genes, including tau and amyloid-β precursor, and integrated it with microRNAs in order to select regulated nodes, study the effects of their depletion on signaling pathways, and prioritize targets for microRNA-based therapeutic approaches. Results: We identified nine protein nodes as potential candidates (Pik3R1, Bace1, Traf6, Gsk3b, Akt1, Cdk2, Adam10, Mapk3 and Apoe) and performed in silico node depletion to simulate the effects of microRNA regulation. Conclusions: Despite intrinsic limitations of the approach, such as the incompleteness of the available information or possible false associations, the present work shows clear potential for drug design and target prioritization and underscores the need for reliable and comprehensive maps of interactions and pathways. Full article

(This article belongs to the Special Issue Bioinformatics Analysis of RNA for Human Health and Disease)

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24 pages, 6324 KB

Open AccessArticle

MicroRNAs as Key Regulators in the Progression of Metabolic Dysfunction-Associated Steatotic Liver Disease: A Bioinformatics Analysis

by Claudriana Locatelli, Karine Luz, Sergio Fallone de Andrade, Emyr Hiago Bellaver, Rosana Claudio Silva Ogoshi, Ariana Centa, João Paulo Assolini, Gustavo Colombo Dal Pont and Tania Beatriz Creczynski-Pasa

Biomedicines 2026, 14(1), 120; https://doi.org/10.3390/biomedicines14010120 - 7 Jan 2026

Cited by 4 | Viewed by 1203

Abstract

Background: Metabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as non-alcoholic fatty liver disease, is a highly prevalent hepatic condition closely linked to metabolic syndrome (MetS). Epigenetic regulators such as microRNAs (miRNAs) have emerged as critical modulators of the molecular pathways underlying MASLD pathogenesis, offering new perspectives for non-invasive diagnosis and targeted therapy. This study aimed to identify and characterize target genes and pathways regulated by two key hepatic miRNAs, namely miR-122 and miR-29a, through a comprehensive in silico bioinformatics approach, to better understand their functional roles in MASLD and MetS. Methods: Target genes of miR-122 and miR-29a were predicted using three databases (TargetScan, DIANA-microT-CDS, and miRWalk), and those identified by at least two databases were selected for downstream analyses. Functional enrichment was performed using Gene Ontology and KEGG pathway analysis. Gene networks and biological process maps were constructed using Metascape, clusterProfiler and Cytoscape. Results: miR-122 was found to negatively regulate genes involved in lipid metabolism, insulin signaling, and inflammatory pathways, including PPARGC1A, PPARA, LPL, TLR4, and HMGCR, contributing to insulin resistance and liver dysfunction. By contrast, miR-29a demonstrated potential hepatoprotective effects by targeting LEP, INSR, IL13, and IL18, enhancing insulin sensitivity and reducing fibrogenic activity. Enrichment analysis revealed strong associations with biological processes, such as STAT phosphorylation, lipid homeostasis, and inflammatory signaling, as well as associations with cellular components, including lipoproteins and plasma membranes. miR-122 and miR-29a exhibit opposing regulatory functions in MASLD pathogenesis. Whereas miR-122 is associated with disease progression, miR-29a acts protectively. These miRNAs may serve as promising biomarkers and therapeutic targets in MASLD and related metabolic conditions. Further validation through experimental and clinical studies is warranted. Full article

(This article belongs to the Special Issue Bioinformatics Analysis of RNA for Human Health and Disease)

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15 pages, 3402 KB

Open AccessArticle

Loss of miRNA-Mediated VEGFA Regulation by SNP-Induced Impairment: A Bioinformatic Analysis in Diabetic Complications

by Raquel Freitas, Stela Felipe, Christina Pacheco, Emmanuelle Faria, Jonathan Martins, Jefferson Fortes, Denner Silva, Paulo Oliveira and Vania Ceccatto

Biomedicines 2025, 13(5), 1192; https://doi.org/10.3390/biomedicines13051192 - 14 May 2025

Viewed by 1276

Abstract

Background/Objectives: MicroRNAs (miRNAs) are molecules involved in biological regulation processes, including type 2 diabetes and its complications development. Single nucleotide polymorphisms (SNPs) can alter miRNA mechanisms, resulting in loss or gain effects. VEGFA is recognized for its role in angiogenesis. However, its overexpression can lead to deleterious effects, such as disorganized and inefficient vasculature. Under hyperglycemic conditions, VEGFA expression seems to increase, which may contribute to the development of microvascular and macrovascular diabetic complications. Several miRNAs are associated with VEGFA regulation and seem to act in the prevention of dysregulated expression. This study aimed to investigate SNPs in miRNA regions related to the loss effect in VEGFA regulation, examining their frequency and potential physiological effects in the development of diabetic complications. Methods: VEGFA-targeting miRNAs were identified using the R package multimiR, with validated and predicted results. Tissue expression analysis and SNP search were data-mined with Python 3 for miRNASNP-v3 SNP raw databases. Allele frequencies were obtained from dbSNP. The miRNA–mRNA interaction comparison was obtained in the miRmap tool through Python 3. MalaCards were used to infer physiological disease association. Results: The variant rs371699284 was selected in hsa-miR-654-3p among 103 potential VEGFA-targeting miRNAs. This selected SNP demonstrated promising results in bioinformatics predictions, tissue-specific expression, and population frequency, highlighting its potential role in miRNA regulation and the resulting loss in VEGFA-silencing efficiency. Conclusions: Our findings suggest that carriers of rs1238947970 may increase susceptibility to diabetic microvascular and macrovascular complications. Furthermore, in vitro and in silico studies are necessary to better understand these processes. Full article

(This article belongs to the Special Issue Bioinformatics Analysis of RNA for Human Health and Disease)

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20 pages, 3030 KB

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DeepWalk-Based Graph Embeddings for miRNA–Disease Association Prediction Using Deep Neural Network

by Jihwan Ha

Biomedicines 2025, 13(3), 536; https://doi.org/10.3390/biomedicines13030536 - 20 Feb 2025

Cited by 31 | Viewed by 2724

Abstract

Background: In recent years, micro ribonucleic acids (miRNAs) have been recognized as key regulators in numerous biological processes, particularly in the development and progression of diseases. As a result, extensive research has focused on uncovering the critical involvement of miRNAs in disease mechanisms to better comprehend the underlying causes of human diseases. Despite these efforts, relying solely on biological experiments to identify miRNA-disease associations is both time-consuming and costly, making it an impractical approach for large-scale studies. Methods: In this paper, we propose a novel DeepWalk-based graph embedding method for predicting miRNA–disease association (DWMDA). Using DeepWalk, we extracted meaningful low-dimensional vectors from the miRNA and disease networks. Then, we applied a deep neural network to identify miRNA–disease associations using the low-dimensional vectors of miRNAs and diseases extracted via DeepWalk. Results: An ablation study was conducted to assess the proposed graph embedding modules. Furthermore, DWMDA demonstrates exceptional performance in two major cancer case studies (breast and lung), with results based on statistically robust measures, further emphasizing its reliability as a method for identifying associations between miRNAs and diseases. Conclusions: We expect that our model will not only facilitate the accurate prediction of disease-associated miRNAs but also serve as a generalizable framework for exploring interactions among various biological entities. Full article

(This article belongs to the Special Issue Bioinformatics Analysis of RNA for Human Health and Disease)

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17 pages, 2756 KB

Open AccessArticle

Graph Convolutional Network with Neural Collaborative Filtering for Predicting miRNA-Disease Association

by Jihwan Ha

Biomedicines 2025, 13(1), 136; https://doi.org/10.3390/biomedicines13010136 - 8 Jan 2025

Cited by 35 | Viewed by 2752

Abstract

Background: Over the past few decades, micro ribonucleic acids (miRNAs) have been shown to play significant roles in various biological processes, including disease incidence. Therefore, much effort has been devoted to discovering the pivotal roles of miRNAs in disease incidence to understand the underlying pathogenesis of human diseases. However, identifying miRNA–disease associations using biological experiments is inefficient in terms of cost and time. Methods: Here, we discuss a novel machine-learning model that effectively predicts disease-related miRNAs using a graph convolutional neural network with neural collaborative filtering (GCNCF). By applying the graph convolutional neural network, we could effectively capture important miRNAs and disease feature vectors present in the network while preserving the network structure. By exploiting neural collaborative filtering, miRNAs and disease feature vectors were effectively learned through matrix factorization and deep learning, and disease-related miRNAs were identified. Results: Extensive experimental results based on area under the curve (AUC) scores (0.9216 and 0.9018) demonstrated the superiority of our model over previous models. Conclusions: We anticipate that our model could not only serve as an effective tool for predicting disease-related miRNAs but could be employed as a universal computational framework for inferring relationships across biological entities. Full article

(This article belongs to the Special Issue Bioinformatics Analysis of RNA for Human Health and Disease)

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Beyond Variant Evolution: Structurally and Functionally Conserved Regions in the 5′UTR of SARS-CoV-2 as Resilient Antiviral Targets

by Andrea Masotti

Biomedicines 2026, 14(3), 622; https://doi.org/10.3390/biomedicines14030622 - 10 Mar 2026

Cited by 1 | Viewed by 633

Abstract

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a positive-sense RNA virus, and its genome includes a highly conserved 5′ untranslated region (5′UTR). This region contains the so-called ‘leader sequence’, a crucial genomic region responsible for the viral replication and the synthesis of all subgenomic RNAs (sgRNAs). It has been demonstrated that targeting highly conserved genomic regions is essential for developing broad-spectrum antiviral therapies that resist viral mutation and evasion. Hypothesis: Given the high level of nucleotide homology between SARS-CoV and SARS-CoV-2, particularly in essential regions like the 5′UTR, the identification of a perfect sequence alignment across SARS-CoV-2 variants within this conserved region would provide a robust, mutation-resistant target for novel RNA-based drugs, such as small interfering RNAs (siRNAs) or microRNAs (miRNAs). Materials and Methods: Sequence alignment was performed across the different SARS-CoV-2 strains (i.e., the different variants that have appeared so far) to identify conserved genomic areas, leading to the selection of potential target sites for antiviral molecules. Specifically, computational analyses were utilized to map available binding sites for human miRNAs within the SARS-CoV-2 5′UTR. Results: Comparative alignments revealed that the leader sequence/5′UTR region is highly stable and conserved in all the considered SARS-CoV-2 sequences, representing a common therapeutic target across different variants and strains. Discussion: The perfect alignment observed in the 5′UTR confirms that this region is a highly critical target, less prone to mutations in all the considered variants. This property makes the region ideal for therapeutic intervention using non-coding RNAs. If endogenous miRNAs were found to bind this region (e.g., miR-638, miR-3150b-3p, etc.) and promote viral replication similarly to mechanisms observed in viruses like hepatitis C virus (HCV), their activity could be inhibited using chemically modified antisense analogs, such as locked nucleic acid (LNA) oligonucleotides. Full article

(This article belongs to the Special Issue Bioinformatics Analysis of RNA for Human Health and Disease)

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