Air pollution and prostate cancer: Unraveling the connection through network toxicology and machine learning

作者：Yuqi Li, Tao Zhou, Zhiyu Liu, Xinyao Zhu, Qilong Wu, Chunyang Meng, Qingfu Deng · 发表于：Ecotoxicology and Environmental Safety · 年份：2025 · DOI：10.1016/j.ecoenv.2025.117966 · 被引用次数：64 · 研究领域：Ferroptosis and cancer prognosis、Air Quality and Health Impacts、Bioinformatics and Genomic Networks

BACKGROUND: In recent years, air pollution has been demonstrated to be associated with the occurrence of various diseases. This study aims to explore the potential association between air pollutants and prostate cancer (PCa) and to identify key genes that may play a critical bridging role in this process. METHODS: This study utilized multiple online databases to obtain relevant target genes associated with air pollutants and PCa. Protein-protein interaction (PPI) analysis and visualization were conducted for the intersecting genes, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses to explore potential mechanisms. Subsequently, the best predictive model was selected through a combination of 108 machine learning algorithms. A prognostic model was constructed using the Random Survival Forest (RSF) model in conjunction with Lasso regression model, and its performance was validated in four external datasets. Finally, molecular docking analysis was conducted to investigate the interaction between key genes and air pollutants. RESULTS: Seven common air pollutants (benzene, SO₂, NO, CO, NO₂, toluene, and O₃) were selected for analysis, and 48 intersecting targets related to PCa were identified. GO and KEGG functional enrichment analyses revealed that these targets are primarily involved in regulating biological processes such as apoptosis, carcinogenesis, and cell proliferation. Based on machine learning algorithm selecti...