Integrated functional analysis of prostate cancer– associated genes: A multi-dataset bioinformatics approach

Abstract

Aim: This study aimed to investigate key genetic variants contributing to prostate cancer and their functional significance through integrative bioinformatic and molecular network analyses. Methodology: GWAS data for prostate cancer were explored to identify important genetic variants and underlying biological pathways. Bioinformatic approaches were applied for enrichment analysis, protein–protein interaction network construction, and clustering to assess gene interactions. Regulatory mechanisms were examined through microRNA and transcription factor interaction analyses, with metabolomic data integrated to assess the impact of genetic variability on prostate cancer metabolism. Results: Notable associations were identified for hsa-miR-2277-5p and hsa-miR-3944-3p, suggesting potential regulatory significance, although these did not retain significance following multiple testing correction. Reactome Pathway 2024 analysis identified Abacavir Transmembrane Transport as the most significantly enriched pathway (adjusted p = 0.00147; odds ratio = 429.33), alongside additional biologically relevant pathway associations. Interpretation: This study highlights key genetic factors and regulatory elements potentially contributing to prostate cancer susceptibility and progression. The identified genes, microRNAs, and pathways advance mechanistic understanding of disease vulnerability and may ultimately inform the development of biological markers and targeted therapeutic strategies for prostate cancer. Key words: Bioinformatics, GWAS, MicroRNA, Prostate cancer, Pathway enrichment