Integrative Genetic and Bioinformatic Analysis of Androgen Pathway Gene Polymorphisms, Testosterone, and PSA Levels in Prostate Cancer

Background: Prostate cancer (PCa) is genetically a complicated disease with mediation by variations in genes coding for androgen biosynthesis and signaling genes. In the current study, we investigate the associations of key polymorphisms in the androgen pathway with serum testosterone and prostate-specific antigen (PSA) levels, while also exploring their potential functional impact using bioinformatics analyses. Methods: A total of 314 patients with prostate adenocarcinoma and 287 age-matched healthy controls were genotyped for ten SNPs across CYP17A1, HSD3B1, HSD3B2, SRD5A2, and AR genes using sequencing methods. Associations with disease risk, serum testosterone, and PSA were evaluated. Functional predictions, linkage disequilibrium (LD), and protein–protein interaction (PPI) network analyses were performed in silico . Results: Significant associations were found for CYP17A1 (rs10883783), HSD3B1 (rs6203, rs33937873), SRD5A2 (rs12470143) with PCa risk, whereas androgen receptors polymorphisms (rs1204038, rs6152) and HSD3B2 (rs1819698, rs58154933) showed no association. The A allele of CYP17A1 rs10883783 and the T allele of HSD3B1 rs6203 were correlated with lower testosterone levels, while only the CYP17A1 rs10883783 polymorphism was significantly associated with increased PSA levels. Bioinformatic analysis showed potential deleterious effects for the HSD3B1 rs6203 variant, as well as regulatory effects for CYP17A1 rs10883783, in accordance with their corresponding enzymatic role in androgen biosynthesis. Additionally, analysis of PPI and co-expression networks revealed tight functional connectivity of the androgen pathway genes. Conclusion: These results suggest a biological function for genetic polymorphisms of HSD3B1 and CYP17A1 in PCa susceptibility and androgen regulation. These polymorphic variants can serve as effective risk biomarkers for PCa and disease activity using a combination of genotyping, hormone assay, and bioinformatics. These findings need more research, including larger multi-ethnic cohorts and functional validation to ascertain their therapeutic relevance.