Search Results (353)

Background: Preeclampsia (PE) is a pregnancy-specific disease and hypertensive disorder with a multifactorial pathogenesis involving complex molecular regulatory networks. Recent studies highlight the critical role of non-coding RNAs, particularly miRNAs and lncRNAs, in PE development. This study investigates the molecular interaction between miR-7151-5p and the lncRNA KCNQ1OT1 and their functional contributions to PE pathogenesis. Methods: An integrative approach combining RNAhybrid-based bioinformatics, dual-luciferase reporter assays, qRT-PCR, Transwell migration and invasion assays, and RNA sequencing was employed to characterize the binding between miR-7151-5p and KCNQ1OT1 and assess their influence on trophoblast cell function and gene expression. Results: A bioinformatic analysis predicted a stable binding site between miR-7151-5p and KCNQ1OT1 (minimum free energy: –37.3 kcal/mol). The dual-luciferase reporter assay demonstrated that miR-7151-5p directly targets KCNQ1OT1, leading to suppressed transcriptional activity. In HTR8/SVneo cells, miR-7151-5p overexpression significantly downregulated both KCNQ1OT1 and Notch1 mRNA, whereas its inhibition showed no significant changes, suggesting additional regulatory mechanisms of Notch1 expression. Transwell assays indicated that miR-7151-5p overexpression suppressed trophoblast cell migration and invasion, whereas its inhibition enhanced these cellular behaviors. RNA-seq analysis further revealed that miR-7151-5p overexpression altered key signaling pathways, notably the TGF-β pathway, and significantly modulates PE-associated genes, including PLAC1, ANGPTL6, HIRA, GLA, HSF1, and BAG6. Conclusions: The regulatory effect of miR-7151-5p on KCNQ1OT1, along with its influence on trophoblast cell dynamics via Notch1 and TGF-β signaling pathways, highlights its role in PE pathogenesis and supports its potential as a biomarker in early PE screening. Full article

Hepatocellular carcinoma (HCC) critically lacks reliable biomarkers for early detection. By mining the TCGA_LIHC and two GEO cohorts, we identified the liver-specific long non-coding RNA CTC-537E7.3 as the most consistently down-regulated transcript in tumors. This finding was validated in 97 paired tissues, with CTC-537E7.3 expression lost in 95% of cases (*** p < 0.0001). It demonstrated excellent diagnostic performance in discriminating tumor from non-tumor tissue (AUC = 0.95), which was maintained in early-stage (I/II) disease. Low CTC-537E7.3 expression correlated with shorter overall and disease-free survival and was inversely associated with serum α-fetoprotein (AFP) levels, highlighting its complementary clinical value. Mechanistic investigation revealed a potential competing endogenous RNA (ceRNA) axis. The microRNA miR-190b-5p was highly expressed in tumors and predicted to bind CTC-537E7.3, while its target, PLGLB1, was significantly suppressed. Survival analysis confirmed that concurrent high expression of CTC-537E7.3 and PLGLB1 conferred superior outcomes. These findings establish CTC-537E7.3 as a liver-specific, ceRNA-mediated tumor suppressor with robust diagnostic and prognostic potential. It represents a promising adjunct to existing HCC surveillance strategies, such as ultrasound and AFP measurement, for high-risk populations. Full article

Asthma places a significant burden at individual and societal levels, but there remains no gold-standard objective test for asthma diagnosis or asthma severity risk prediction. MicroRNAs (miRNAs) are short non-coding RNA sequences that are attracting interest as biological signatures of health and disease status. We sought to construct serum miRNA panels that could serve as potential biomarkers to aid in the diagnosis of asthma and predict asthma severity. Thirty-five asthma-related miRNAs were screened in the serum of three patient groups (never-asthma, mild-asthma, and severe-asthma; n = 50/group) drawn from two well-characterised cohorts. miRCURY LNA technology was used, followed by GeneGlobe analysis. The associations of miRNA expression with clinical outcomes of interest and diagnostic value of the proposed miRNA panels were assessed. We identified an asthma diagnosis panel comprising upregulated miR-223-3p, miR-191-5p, and miR-197-3p (area under curve (AUC) = 0.813, sensitivity 76% and specificity 72%). Compared with mild-asthma individuals, we also identified an asthma severity risk panel comprising upregulated miR-223-3p plus downregulated miR-30a-5p, miR-660-5p, and miR-125b-5p (AUC = 0.759, sensitivity 78%, specificity 64%). Individual miRNAs showed associations with worse clinical asthma severity and impaired quality of life. miRNA panels with high sensitivity and specificity offer potential as biomarkers for asthma diagnosis and asthma severity. Full article

Background/Objectives: MicroRNAs (miRNAs) have emerged as molecular mediators involved in the pathogenesis of rheumatoid arthritis (RA). Given the influence of diet on gene expression and inflammation, plant-based diets represent a potential non-pharmacological strategy for modulating disease activity. This study aimed to explore and validate, through a bioinformatic-guided pilot approach, the regulation of miRNAs associated with RA in response to a 14-day plant-based dietary intervention. Methods: Candidate miRNAs were identified through differential expression analysis of the GSE124373 dataset using GEO2R and were further supported by a literature review. Target gene prediction and functional enrichment analyses were conducted to assess the biological relevance of these findings. Twenty-three RA patients followed a plant-based diet for 14 days. The clinical activity (DAS28-CRP), biochemical markers, and plasma expression of five selected miRNAs (miR-26a-5p, miR-125a-5p, miR-125b-5p, miR-146a-5p, and miR-155-5p) were evaluated before and after the intervention using RT-qPCR. Results: Significant reductions were observed in DAS28-CRP scores, C-reactive protein, glucose, and lipid levels after 14 days of intervention. Three of the five miRNAs (miR-26a-5p, miR-125a-5p, and miR-155-5p) were significantly downregulated post-intervention. Bioinformatic analyses indicated that these miRNAs regulate immune–inflammatory pathways relevant to RA pathogenesis. Conclusions: This pilot study suggests that a short-term plant-based dietary intervention may modulate circulating miRNAs and improve clinical and biochemical parameters in RA patients. These findings support further research into dietary strategies as complementary approaches for RA management. Full article

Background: Head and neck cancer (HNC) remains a global health challenge with a poor 5-year survival rate among patients with relapsed or advanced-stage disease. Immune checkpoint blockade therapies have emerged as a promising approach to improve outcomes; however, their effectiveness is limited, with response rates of only 15–20% because of immune evasion mechanisms. MicroRNA (miRNA) dysregulation plays a key role in facilitating such immune evasion. In this study, we aim to identify specific miRNAs whose altered expression contributes to immune escape in HNC. Methods: We employed an integrated bioinformatics approach, incorporating differential expression analysis, survival analysis, target prediction, KEGG immune pathway analysis, a protein–protein interaction network, and the identification of hub genes using in silico tools. Results: Our analysis revealed that a high expression of miR-18a and miR-2355 was associated with reduced survival, with the median survival decreasing from 42.9 to 27.8 months, respectively, in advanced-stage patients. Conversely, a low expression of let-7c and miR-6510 was linked to poor prognosis, with survival decreasing from 40.1 to 19.2 months and from 50.1 to 26.8 months, respectively, across disease progression. Further pathway analysis revealed that these miRNAs are significantly involved in the regulation of key immune evasion signaling pathways, including T cell receptor, PD-L1/PD-1 checkpoint, JAK-STAT, TGF-beta, NF-kappa B, and TNF signaling pathways. Hub gene analysis identified AKT1, STAT3, NFKB1, CD4, IL2RB, TLR4, and CTLA-4 as potential dysregulated miRNA targets, with enrichment in immune-related signaling pathways. Conclusions: Taken together, these findings suggest that targeting these miRNAs could modulate immune evasion mechanisms and potentially enhance the efficacy of ICB therapies in HNC. Full article

The prediction of microRNA-disease associations (MDAs) is crucial for understanding disease mechanisms and biomarker discovery. While graph neural networks have emerged as promising tools for MDA prediction, existing methods face critical limitations: (1) data leakage caused by improper use of Gaussian interaction profile (GIP) kernel similarity during feature construction, (2) self-validation loops in calculating miRNA functional similarity using known MDA data, and (3) information bottlenecks in conventional graph neural network (GNN) architectures that flatten heterogeneous relationships and employ over-simplified decoders. To address these challenges, we propose CoupleMDA, a metapath-guided heterogeneous graph learning framework coupling structural and semantic features. The model constructs a biological heterogeneous network using independent data sources to eliminate feature-target space coupling. Our framework implements a two-stage encoding strategy: (1) relational graph convolutional networks (RGCN) for pre-encoding and (2) metapath-guided semantic aggregation for secondary encoding. During decoding, common metapaths between node pairs structurally guide feature pooling, mitigating information bottlenecks. The comprehensive evaluation shows that CoupleMDA achieves a 2–5% performance improvement over the current state-of-the-art baseline methods in the heterogeneous graph link prediction task. Ablation studies confirm the necessity of each proposed component, while case analyses reveal the framework’s capability to recover cancer-related miRNA-disease associations through biologically interpretable metapaths. Full article

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

Background: Single-nucleotide polymorphisms (SNPs) are associated with multiple disorders and various cancer types. In the context of cancer, alterations within non-coding regions, specifically 3′ untranslated regions (3′ UTR), have proven substantially important. Methods: In this study, we utilized various bioinformatics tools to examine the effect of SNPs in the 3′ UTR. We retrieved the 3′ UTR SNPs of the Signal Transducer and Activator of Transcription 1 (STAT1) gene from the National Centre for Biotechnology Information (NCBI) website. Next, we employed the Polymorphism in miRNAs and their corresponding target sites (PolymiRTS) database to predict the 3′ UTR SNPs that create new microRNA (miRNA) binding sites and their respective miRNAs. The effect of the 3′ UTR SNPs on the messenger RNA structure was studied using RNAfold server. We used Cscape tool to predict the oncogenic 3′ UTR SNPs. Then, we submitted the miRNAs to the miRNet database to visualize the miRNA-miRNAs’ target genes interaction, for which gene enrichment analysis was performed using ShinyGO. Protein–protein interactions were conducted using the STRING database. We conducted miRNA enrichment analysis utilizing miRPathDB, subsequently performing miRNA differential expression analysis through oncoMIR, and the StarBase database. The survival analysis of the upregulated miRNAs in cancer was investigated using the Kaplan–Meier Plotter. Result: Twelve SNPs were predicted to create new miRNA binding sites. Two of them, rs188557905 and rs190542524, were predicted to destabilize the mRNA structures. We predicted rs190542524, rs11305, rs186033487, and rs188557905 to be oncogenic 3′ UTR SNPs, with high-confidence predictions and scores > 0.5. Using miRNAs’ target genes enrichment analysis, this study indicated that the miRNA target genes were more likely to be involved in cancer-related pathways. Our comprehensive analysis of miRNAs, their functional enrichment, their expression in various types of cancer, and the correlation between miRNA expression and survival outcome yielded these results. Our research shows that the oncogenic 3′ UTR SNP rs190542524 creates a new binding site for the oncogenic miRNA hsa-miR-136-5p. This miRNA is significantly upregulated in BLCA, LUSC, and STAD and is linked to poor survival. Additionally, rs114360225 creates a new binding site for hsa-miR-362-3p, influencing LIHC. Conclusions: These analyses suggest that these 3′ UTR SNPs may have a functional impact on the STAT1 gene’s regulation through their predicted effect on miRNA binding sites. Future experimental validation could establish their potential role in the diagnosis and treatment of various diseases, including cancer. Full article

Renal cell carcinoma (RCC) presents a significant diagnostic challenge, particularly in small renal masses. The search for non-invasive screening methods and biomarkers has directed research toward liquid biopsy, which focuses on microRNAs (miRNAs), exosomes, and circulating tumor cells (CTCs). miRNAs are small non-coding RNA molecules that show considerable dysregulation in RCC, and they have potential for both diagnostic and prognostic applications. Research has highlighted their utility on biofluids, such as plasma, serum, and urine, in detecting RCC and characterizing its subtypes. Promising miRNA signatures have been associated with overall survival, suggesting their potential importance in the management of RCC. Exosomes, which carry a variety of molecular components, including miRNAs, are emerging as valuable biomarkers, whereas CTCs, released from primary tumors into the bloodstream, provide critical information on cancer progression. However, translation of these findings into clinical practice requires additional validation and standardization through large-scale studies and robust evidence. Although there are currently no approved diagnostic tests for RCC, the future potential of liquid biopsy in monitoring, treatment decision-making, and outcome prediction in patients with this disease is significant. This review examined and discussed recent developments in liquid biopsy for RCC, assessing both the strengths and limitations of these approaches for managing this disease. Full article

Hemorrhagic neurovascular diseases, with high mortality and poor outcomes, urge novel biomarker discovery and therapeutic targets. Micro-ribonucleic acids (miRNAs) are potent post-transcriptional regulators of gene expression. They have been studied in association with disease states and implicated in mechanistic gene interactions in various pathologies. Their presence and stability in circulating fluids also suggest a role as biomarkers. This review summarizes the current state of knowledge about miRNAs in the context of cerebral cavernous malformations (CCMs), a disease involving cerebrovascular dysmorphism and hemorrhage, with known genetic underpinnings. We also review common and distinct miRNAs of CCM compared to other diseases with brain vascular dysmorphism and hemorrhage. A systematic search, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline, queried all peer-reviewed articles published in English as of January 2025 and reported miRNAs associated with four hemorrhagic neurovascular diseases: CCM, arteriovenous malformations, moyamoya disease, and intracerebral hemorrhage. The PubMed systematic search retrieved 154 articles that met the inclusion criteria, reporting a total of 267 unique miRNAs identified in the literature on these four hemorrhagic neurovascular diseases. Of these 267 miRNAs, 164 were identified in preclinical studies, while 159 were identified in human subjects. Seventeen miRNAs were common to CCM and other hemorrhagic diseases. Common and unique disease-associated miRNAs in this systematic review motivate novel mechanistic hypotheses and have potential applications in diagnostic, predictive, prognostic, and therapeutic contexts of use. Much of current research can be considered hypothesis-generating, reflecting association rather than causation. Future areas of mechanistic investigation are proposed alongside approaches to analytic and clinical validations of contexts of use for biomarkers. Full article

Small non-coding molecules known as microRNAs (miRNAs) play a critical role in disease diagnosis, treatment, and prognosis evaluation. Traditional wet-lab methods for validating miRNA–disease associations are often time-consuming and inefficient. With the advancement of high-throughput sequencing technologies, deep learning methods have become effective tools for uncovering potential patterns in miRNA–disease associations and revealing novel biological insights. Most of the existing approaches focus primarily on individual molecular behavior, overlooking interactions at the multi-molecular level. Conventional graph neural network (GNN) models struggle to generalize to heterogeneous graphs, and as network depth increases, node representations become indistinguishable due to over-smoothing, resulting in reduced predictive performance. GONNMDA first integrates similarity features from multiple data sources and applies noise reduction to obtain a reconstructed, comprehensive similarity representation. It then constructs heterogeneous graphs and applies a root–tree hierarchical alignment, along with an ordered gating message-passing mechanism, effectively addressing the challenges of heterogeneity and over-smoothing. Finally, a multilayer perceptron is employed to produce the final association predictions. To evaluate the effectiveness of GONNMDA, we conducted extensive experiments where the model achieved an AUC of 95.49% and an AUPR of 95.32%. The results demonstrate that GONNMDA outperforms several recent state-of-the-art methods. In addition, case studies and survival analyses on three common human cancers—breast cancer, rectal cancer, and lung cancer—further validate the effectiveness and reliability of GONNMDA in predicting miRNA–disease associations. Full article

Acute Kidney Injury (AKI) is a severe clinical condition featured by a rapid decrease in kidney function in a short period of time. AKI, which is often secondary to sepsis, ischemia-reperfusion and drug toxicity, is associated to high morbidity and mortality. Moreover, it contributes to the development of chronic kidney disease (CKD), due to maladaptive or incomplete repair mechanisms, resulting in renal fibrosis. Small non-coding RNA has recently emerged as a novel biomarker for the early detection and treatment of AKI. In particular, microRNAs (miRNAs) are non-coding RNA molecules of 21–25 nucleotides regulating the expression of protein-coding genes through sequence-specific recognition. Due to their high stability in biological fluids, such as urine and plasma, they can be reliably analyzed and quantified, and for this reason they can be considered potential diagnostic and therapeutic biomarkers. Specifically, miRNAs have been demonstrated to predict AKI before the increase in creatinine levels, thus improving the management of this syndrome. In this review, we provide a comprehensive overview of the role of urinary and plasma miRNAs in the early detection and treatment of AKI. Full article

The identification of cancer prognostic biomarkers is crucial for predicting disease progression, optimizing personalized therapies, and improving patient survival. Molecular biomarkers are increasingly being identified for cancer prognosis estimation. However, existing studies and databases often focus on single-type molecular biomarkers, deficient in comprehensive multi-omics data integration, which constrains the comprehensive exploration of biomarkers and underlying mechanisms. To fill this gap, we conducted a systematic prognostic analysis using over 10,000 samples across 33 cancer types from The Cancer Genome Atlas (TCGA). Our study integrated nine types of molecular biomarker-related data: single-nucleotide polymorphism (SNP), copy number variation (CNV), alternative splicing (AS), alternative polyadenylation (APA), coding gene expression, DNA methylation, lncRNA expression, miRNA expression, and protein expression. Using log-rank tests, univariate Cox regression (uni-Cox), and multivariate Cox regression (multi-Cox), we evaluated potential biomarkers associated with four clinical outcome endpoints: overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI). As a result, we identified 4,498,523 molecular biomarkers significantly associated with cancer prognosis. Finally, we developed SurvDB, an interactive online database for data retrieval, visualization, and download, providing a comprehensive resource for biomarker discovery and precision oncology research. Full article

Spinocerebellar ataxia type 7 (SCA7) is an inherited neurodegenerative disease characterized by cerebellar ataxia and retinal degeneration, caused by an abnormal expansion of the CAG trinucleotide in the coding region of the ATXN7 gene. Currently, in silico analysis is used to explore mechanisms and biological processes through bioinformatics predictions in various neurodegenerative diseases. Therefore, the aim of this study was to identify candidate human gene targets of four miRNAs (hsa-miR-29a-3p, hsa-miR-132-3p, hsa-miR-25-3p, and hsa-miR-92a-3p) involved in pathways that could play an important role in SCA7 pathogenesis through comprehensive in silico analysis including the prediction of miRNA target genes, Gen Ontology enrichment, identification of core genes in KEGG pathways, transcription factors and validated miRNA target genes with the mouse SCA7 transcriptome data. Our results showed the participation of the following pathways: adherens junction, focal adhesion, neurotrophin signaling, endoplasmic reticulum processing, actin cytoskeleton regulation, RNA transport, and apoptosis and dopaminergic synapse. In conclusion, unlike previous studies, we highlight using a bioinformatics approach the core genes and transcription factors involved in the different biological pathways and which ones are targets for the four miRNAs, which, in addition to being associated with neurodegenerative diseases, are also de-regulated in the plasma of patients with SCA7. Full article

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