In Silico Analysis of Non-Coding RNA Regulation in Human Gene Expression: A Systematic Computational Approach to Understanding Regulatory Networks

Abstract

The regulatory networks of miRNAs, lncRNAs, and their target genes play a crucial role in controlling various cellular processes, including cell growth, apoptosis, and immune responses. In this study, we performed an in silico analysis to explore the interactions between miRNAs, lncRNAs, and their target genes in the context of disease mechanisms. We utilized multiple computational approaches, including miRNA-target interaction prediction, lncRNA-target network analysis, differential expression analysis using RNA-seq data, and validation of interactions using miRNA target prediction tools. Our results highlight key genes involved in apoptosis, cell cycle regulation, and tumorigenesis, providing valuable insights into the molecular mechanisms underlying disease progression. Non-coding RNAs (ncRNAs), including long non-coding RNAs (lncRNAs) and microRNAs (miRNAs), have emerged as pivotal regulators of gene expression in human cells. Despite substantial research into their roles, there remains a critical gap in understanding how these molecules interact within complex regulatory networks. In this study, we employed a comprehensive bioinformatics approach to systematically identify and analyze ncRNA-mediated gene regulation in human cells. We utilized publicly available datasets from the ENCODE and GEO repositories, combined with computational tools such as miRBase, LNCipedia, TargetScan, and Cytoscape, to predict ncRNA-gene interactions and construct regulatory networks. Our analysis reveals several novel ncRNA regulators and their associated gene targets, which were further explored through pathway enrichment analysis. This study provides new insights into the regulatory networks of ncRNAs in human gene expression, offering a foundation for future functional studies and potential therapeutic applications.