<p>The incidence of both prostate cancer (PCa) and metabolic syndrome (MS) has been steadily increasing due to changes in population structure and lifestyle. These two conditions frequently co-occur, yet their shared pathogenic mechanisms remain unclear. In this study, we utilized bioinformatics and machine learning techniques to analyze public datasets and validated our findings using clinical specimens from our center to identify common signature genes between PCa and MS. We began by screening differentially expressed genes (DEGs) and module genes through Linear models for microarray analysis (Limma) and Weighted Gene Co-expression Network Analysis (WGCNA) of four microarray datasets from the GEO database (PCa: GSE8511, GSE32571, and GSE104749; MS: GSE98895). Comprehensively bioinformatics analyses, including functional enrichment, LASSO, and random forest algorithms, coupled with receiver operating characteristic (ROC) and precision recall curve (PRC) analyses were conducted. We identified 423 DEGs in the PCa dataset and 2481 differentially modular genes in the MS dataset. Among these, 52 intersection genes enriched in immunomodulatory pathways were found. Three common signature genes, namely GPD1L, ACY1, and C12orf75, were identified through LASSO and random forest analyses. Subsequent validation using clinical specimens confirmed differential expression of these genes in PCa, with survival analysis indicating that elevated expression of ACY1 is associated with adverse prognosis in PCa patients. Additionally, immunoinfiltration analysis revealed higher levels of macrophage M0 and activated dendritic cells in PCa tissues. In summary, our study identifies three shared signature genes between PCa and MS, with ACY1 demonstrating adverse prognostic significance in PCa. Our findings provide a foundation for elucidating the pathogenic mechanisms and interplay between PCa and MS, offering novel insights for identifying potential therapeutic targets in PCa.</p>