A diagnosis model for prostate adenocarcinoma based on hub genes targeted by sulforaphane

Prostate adenocarcinoma (PRAD) is the fifth leading cause of cancer-related deaths in men, with castration-resistant disease exhibiting a 5-year survival rate of only 32%. This underscores the urgent need for novel diagnostic biomarkers and therapeutic strategies. Sulforaphane (SFN), a natural isothiocyanate from cruciferous vegetables with proven anti-cancer properties, presents a promising candidate. This study aimed to identify and validate SFN-targeted diagnostic biomarkers for PRAD. Through integrated analysis of The Cancer Genome Atlas (TCGA)/the Gene Expression Omnibus (GEO) transcriptomic data and pharmacological databases, we identified 17 high-confidence SFN-related differentially expressed genes (DEGs). Protein-protein interaction network analysis and CytoHubba algorithms prioritized 9 hub genes. Functional enrichment analysis implicated these hub genes in critical cancer-related pathways. A diagnostic model was constructed based on the hub genes via a machine learning pipeline, which demonstrated high accuracy in both internal and external validation cohorts. Additionally, immune infiltration analysis using single-sample Gene Set Enrichment Analysis, revealed significant correlations between the model genes and immune cell abundance in the tumor microenvironment. In conclusion, this study proposed a robust, SFN-derived diagnostic model for PRAD and provided insights into its potential therapeutic mechanisms through the hub gene modulation and immune microenvironment interaction. These findings warrant further clinical exploration.