Identification of Novel Molecular Subtypes of Prostate Cancer Based on Genes Related to Metabolic Reprogramming to Assess Prognosis and Immune Landscape.

Abstract

Prostate cancer (PRAD) progression varies significantly among patients, with metabolic reprogramming linked to oncogenesis and immune response. However, the prognostic and immune-related roles of metabolic reprogramming-related genes (MRGs) in PRAD remain unclear. PRAD transcriptomic, mutation, and clinical data from TCGA were analyzed. WGCNA identified PRAD-associated gene modules. NMF clustering stratified patients into two molecular subgroups. Prognostic MRGs were screened via univariate Cox and LASSO regression. A gene-based prognostic model was established and validated using ROC, PCA, and Kaplan-Meier analyses. A clinical-variable nomogram predicted survival, with external validation via GEO data set GSE70770. Immune traits of subtypes/risk groups were assessed via ESTIMATE, CIBERSORT, and ssGSEA. Drug sensitivity and gene expression (qRT-PCR) were evaluated. Two metabolic subtypes with distinct survival and immune patterns were identified. A four-gene signature (AKR1C2, PITPNM3, PLA2G5, UCK2) formed a prognostic model. Risk stratification revealed groups with divergent survival rates. High-risk patients exhibited poorer outcomes, reduced immune infiltration, and altered drug sensitivity. The MRG prognostic model stratifies PRAD patients by survival and immune landscape, aiding precision immunotherapy and drug discovery.