A Metabolic Cluster–Derived Risk Model for Prognostic Stratification, Immunometabolic Profiling, and Therapeutic Sensitivity Prediction in Prostate Cancer

Purpose

Metabolic reprogramming is a hallmark of prostate cancer; however, the clinical utility of metabolism-related genes for prognostic stratification remains limited. Patients and Methods: Prostate cancer patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were stratified into metabolism-associated clusters based on metabolism-related gene expression. Genes differentially expressed between clusters were used to construct a prognostic risk model via least absolute shrinkage and selection operator (LASSO) regression. Associations with clinicopathological features, genomic alterations, metabolic activity, immune status, and therapeutic response were systematically evaluated. Computational analyses included tumor immune dysfunction and exclusion (TIDE) and the immunophenoscore for predicting immunotherapy response, pRRophetic and CellMiner for assessing drug sensitivity, and the Connectivity Map for screening candidate compounds. Exploratory assessments were performed using immunohistochemistry (IHC) in a 20-patient cohort with follow-up data and quantitative polymerase chain reaction (qPCR) in an independent 14-patient cohort.