Abstract
Background: MicroRNAs (miRNAs) are key post-transcriptional regulators implicated in the pathogene sis of cardiovascular diseases (CVD). Understanding miRNA-mRNA interactions offers valuable insights into molecular mechanisms and may aid in identifying novel diagnostic and therapeutic targets.
Objective: This study aimed to computationally predict the target genes of selected CVD-associated miR NAs and explore the biological processes and pathways involved using an in silico approach.
Methods: Disease-relevant miRNAs were identified through curated databases and literature mining. Target genes were predicted using miRDB, TargetScan, and validated through miRTarBase. High-con f idence targets were subjected to functional enrichment analysis using Enrichr and DAVID to identify overrepresented Gene Ontology (GO) terms and KEGG pathways. miRNA-mRNA interaction networks were visualized using Cytoscape to identify key regulatory hubs.
Results: Several miRNAs, including hsa-miR-21, hsa-miR-126, and hsa-miR-155, were selected based on their reported involvement in cardiovascular pathophysiology. Target prediction and filtering yielded a set of high-confidence genes involved in inflammatory response, endothelial function, and apoptosis. Enrichment analysis revealed significant involvement in pathways such as PI3K-Akt signaling, MAPK signaling, and NF-?B activation. Network analysis identified central target genes such as PTEN, VEGFA, and STAT3 as potential regulatory hubs.
Conclusion: This in silico study highlights key miRNA-gene interactions and biological pathways in volved in CVD. The findings provide a foundation for experimental validation and suggest several prom ising targets for future cardiovascular diagnostics and therapeutics.
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