Abstract
Prostate cancer (PCa) is one of the most common malignancies in males globally, and its pathogenesis is significantly related to androgen. As one of the important treatments for prostate cancer, androgen deprivation therapy (ADT) inhibits tumor proliferation by controlling androgen levels, either surgically or pharmacologically. However, patients treated with ADT inevitably develop biochemical recurrence and advance to castration-resistant prostate cancer which has been reported to be associated with androgen biosynthetic and catabolic pathways. Thus, gene expression profiles and clinical information of PCa patients were collected from TCGA, MSKCC, and GEO databases for consensus clustering based on androgen biosynthetic and catabolic pathways. Subsequently, a novel prognostic model containing 13 genes (AFF3, B4GALNT4, CD38, CHRNA2, CST2, ADGRF5, KLK14, LRRC31, MT1F, MT1G, SFTPA2, SLC7A4, TDRD1) was constructed by univariate cox regression, lasso regression, and multivariate cox regression. Patients were divided into two groups based on their risk scores: high risk (HS) and low risk (LS), and survival analysis was used to determine the difference in biochemical recurrence-free time between the two. The results were validated on the MSKCC dataset and the GEO dataset. Functional enrichment analysis revealed some pivotal pathways that may have an impact on the prognosis of patients including the CDK-RB-E2F axis, G2M checkpoint, and KRAS signaling. In addition, somatic mutation, immune infiltration, and drug sensitivity analyses were performed to further explore the characteristics of HS and LS groups. Besides, two potential therapeutic targets, BIRC5 and RHOC, were identified by us in prostate cancer. These results indicate that the prognostic model may serve as a predictive tool to guide clinical treatment and provide new insight into the basic research in prostate cancer.