Integrated multi-omics profiling of immune microenvironment and drug resistance signatures for precision prognosis in prostate cancer.

Abstract

Introduction: Prostate cancer (PCa) continues to be a significant cause of mortality among men, with treatment resistance often influenced by the complexity of the tumor microenvironment (TME). This study aims to develop an immune-centric prognostic model that correlates TME dynamics, genomic instability, and the heterogeneity of drug resistance in PCa. Methods: Multi-omics data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were integrated, encompassing transcriptomic profiles of 554 TCGA-PRAD samples and 329 external validation samples. Immune cell infiltration was assessed using CIBERSORT and ESTIMATE. Weighted gene co-expression network analysis (WGCNA) was employed to identify immune-related modules. Single-cell RNA sequencing (ScRNA-seq) of 835 PCa cells uncovered subtype-specific resistance patterns. Prognostic models were constructed using least absolute shrinkage and selection operator (LASSO) regression and subsequently validated experimentally in PCa cell lines. Results: Two immune subtypes were identified: high-risk subgroups displayed TP53 mutations, increased tumor mutation burden (TMB), and enriched energy metabolism pathways. ScRNA-seq delineated three PCa cell clusters, with high-risk subtypes being sensitive to bendamustine/dacomitinib and resistant to apalutamide/neratinib. A 10-gene prognostic model (e.g., MUC5B, TREM1) categorized patients into high/low-risk groups with distinct survival outcomes (log-rank P < 0.0001). Validation in external datasets confirmed the robust predictive accuracy (AUC: 0.854-0.889). Experimental assays verified subtype-specific drug responses and dysregulation of key model genes. Discussion: This study establishes a TME-driven prognostic framework that connects immune heterogeneity, genomic instability, and therapeutic resistance in PCa. By pinpointing metabolic dependencies and subtype-specific vulnerabilities, our findings provide actionable strategies to circumvent treatment failure, such as targeting energy metabolism or tailoring therapies based on resistance signatures.