前立腺がん創薬の標的となりうる必須遺伝子の発見

Prostate cancer (PCa) is a major malignancy affecting men and is a significant contributor to global male mortality. Over the past decade, several new treatments for advanced PCa have been approved; however, opportunities remain for the development of novel therapeutic strategies. Therefore, in this study, we developed an integrated bioinformatics pipeline to identify potential therapeutic targets and repurposed drugs using RNA-seq datasets, aiming to advance treatment options for PCa. Using the LIMMA approach, 458 common differentially expressed genes (cDEGs) were analyzed from three publicly available microarray datasets, leading to the identification of 15 hub genes (HubGs) through a protein-protein interaction (PPI) network. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses revealed their critical roles in PCa, and lower expressions of five HubGs (BIRC5, CDCA5, CENPF, NUSAP1, and TK1) correlated with better survival. All of these genes could potentially serve as biomarkers for the detection and therapy of PCa. Following that, we considered these possible genes as targets for drugs, performed docking analysis with 255 meta-drug agents, and identified the top 10 candidate drugs (adapalene, ergotamine, imatinib, dutasteride, vistusertib, risperidone, zafirlukast, irinotecan hydrochloride, drospirenone, and telmisartan). Finally, we evaluated the binding stability of the top-ranked three complexes-BIRC5-adapalene, BIRC5-imatinib, and TK1-ergotamine-through a 100 nanoseconds (ns) molecular dynamics (MD) simulation conducted using NAMD. The analysis revealed consistent stability across all complexes. This study uniquely combines multidataset transcriptomic integration, HubG prioritization, and MD validation to propose novel biomarker-drug pairings for PCa. The findings offer promising leads for future experimental and clinical validation.