An Integrative Methylation-Metabolism Gene Signature Defines Prognosis and Immunosuppressive Microenvironment in...

The synergistic crosstalk between epigenetic dysregulation and metabolic reprogramming underlies to prostate cancer (PCa) development and treatment resistance, yet an integrated prognostic signature reflecting this nexus remains poorly defined. We developed and validated a gene signature associated with methylation and amino acid metabolism for patient stratification and exploring its connection to tumor microenvironment (TME) remodeling. RNA sequencing data and independent datasets were integrated with predefined gene sets for DNA methylation (n = 79) and amino acid metabolism (n = 471). A analytical workflow was employed: identification of hub genes and least absolute shrinkage and selection operator (LASSO)-Cox modeling; construction of a prognostic nomogram; comprehensive TME profiling; and validation through single-cell RNA sequencing (scRNA-seq) cellular dynamics analysis and immunohistochemistry (IHC) on a prostate cancer tissue microarray. A novel six-gene prognostic model (ASPM, WDR86, CCK, HOXA2, EGF, ZFHX4) was developed. This model efficiently discriminates patients into groups based on risk level though divergent overall survival (p < 0.001) and exhibited high predictive accuracy in external validation sets (3-year area under the curve (AUC) = 0.87). A nomogram incorporating the signature, pathologic T stage, and Gleason score surpassed individual clinical factors (5-year AUC = 0.73). Functional annotation indicated that high-risk tumors were characterized by downregulated androgen response and activated E2F/G2M checkpoint pathways. The signature was correlated with an immunosuppressive TME, which was supported by a negative correlation between ZFHX4 and monocyte infiltration (r = -0.37, p < 0.001) and a positive correlation between ASPM and activated CD4⁺T cells (r = 0.44, p < 0.001). Single-cell trajectory analysis exhibited that epithelial cells, fibroblasts, and natural killer T (NKT) cells was key cellular expressors of the signature. We utilized immunohistochemistry (IHC) and quantitative real-time polymerase chain reaction (qRT-PCR) to confirm the differential expression. We developed and validated an integrative methylation-amino acid metabolism gene signature that effectively predicts prognosis and reflects an immunosuppressive TME in PCa. This study provides a translational framework for precision oncology, bridging epigenetic-metabolic crosstalk to disease aggressiveness, and offers potential biomarkers for informing risk-stratified therapy and immunotherapy approaches.