Abstract
BACKGROUND: Chronic obstructive pulmonary disease (COPD) pathogenesis is influenced by environmental factors, including Benzo(a)pyrene (BaP) exposure. This study aims to identify BaP-related toxicological targets and elucidate their roles in COPD development. METHODS: A comprehensive bioinformatics approach was employed, including the retrieval of BaP-related targets from the Comparative Toxicogenomics Database (CTD) and Super-PRED database, identification of differentially expressed genes (DEGs) from the GSE76925 dataset, and protein-protein interaction (PPI) network analysis. Functional enrichment and immune infiltration analyses were conducted using GO, KEGG, and ssGSEA algorithms. Feature genes related to BaP exposure were identified using SVM-RFE, Lasso, and RF machine learning methods. A nomogram was constructed and validated for COPD risk prediction. Molecular docking was performed to evaluate the binding affinity of BaP with proteins encoded by the feature genes. RESULTS: We identified 72 differentially expressed BaP-related toxicological targets in COPD. Functional enrichment analysis highlighted pathways related to oxidative stress and inflammation. Immune infiltration analysis revealed significant increases in B cells, DC, iDC, macrophages, T cells, T helper cells, Tcm, and TFH in COPD patients compared to controls. Correlation analysis showed strong links between oxidative stress, inflammation pathway scores, and the infiltration of immune cells, including aDC, macrophages, T cells, Th1 cells, and Th2 cells. Seven feature genes (ACE, APOE, CDK1, CTNNB1, GATA6, IRF1, SLC1A3) were identified across machine learning methods. A nomogram based on these genes showed high diagnostic accuracy and clinical utility. Molecular docking revealed the highest binding affinity of BaP with CDK1, suggestive of its pivotal role in BaP-induced COPD pathogenesis. CONCLUSIONS: The study elucidates the molecular mechanisms of BaP-induced COPD, specifically highlighting the role of oxidative stress and inflammation pathways in promoting immune cell infiltration. The identified feature genes may serve as potential biomarkers and therapeutic targets. Additionally, the constructed nomogram demonstrates high accuracy in predicting COPD risk, providing a valuable tool for clinical application in BaP-exposed individuals.