Abstract

Background In recent years, growing attention has been paid to the role of Benzo [a]pyrene (BaP) in the development and progression of prostate cancer (PCa). However, the specific molecular mechanisms remain unclear. This study aims to explore the potential association between BaP and PCa and to identify key molecular targets that may underlie this relationship, using an integrative bioinformatics approach. Methods This study initiated with a computational toxicology assessment of BaP’s carcinogenicity and endocrine-disrupting properties using the ProTox 3.0 platform. Subsequently, potential target genes linking BaP to PCa were identified by integrating multiple public databases. The overlapping genes underwent PPI network construction and visualization, followed by GO functional annotation and KEGG pathway enrichment analyses to elucidate the underlying biological mechanisms. Through screening 101 machine learning algorithm combinations, we identified the most relevant key genes associated with PCa progression. Molecular docking technology was then employed to evaluate the binding interactions between BaP/natural active products and these key targets. The CIBERSORT algorithm was utilized to analyze RRM2’s regulatory role in the PCa tumor microenvironment, complemented by pan-cancer analysis to investigate RRM2’s universal functions across various malignancies. Finally, in vitro cell experiments were conducted for validation. Results This study further underscores the carcinogenic properties and endocrine-disrupting effects of BaP. Integration of multi-source databases identified 443 potential BaP-PCa targets. GO and KEGG enrichment analyses revealed that these targets are primarily involved in regulating cell proliferation, inflammatory responses, oxidative stress, and multiple oncogenic signaling pathways. Machine learning algorithm screening showed that the Enet (α = 0.1) model exhibited the best predictive performance and robustness. Through molecular dock