Integrated identification of CYP1A1 and CYP1B1 as potential drivers of smoking-related lung carcinogenesis.

UNLABELLED: Smoking is the major environmental risk factor for lung cancer; however, the molecular basis underlying individual susceptibility to smoking-related carcinogenesis remains incompletely understood. This study aimed to identify smoking-specific molecular signatures associated with lung cancer development and to investigate their potential biological mechanisms. Transcriptomic datasets from smokers with and without lung cancer, as well as never-smokers with lung cancer, were analyzed using differential expression analysis and weighted gene co-expression network analysis (WGCNA). Candidate genes were further evaluated using machine learning algorithms, Mendelian randomization (MR), single-cell transcriptomic analysis, molecular docking, and in vitro validation. A total of 16 smoking-specific lung cancer-related genes were identified, which were mainly enriched in cytochrome P450-mediated xenobiotic metabolism, oxidoreductase activity, and chemical carcinogenesis-related pathways. Machine learning analysis established a four-gene signature comprising CYP1A1, CYP1B1, HLA-DRA, and IDO1, among which CYP1A1 and CYP1B1 exhibited substantial contributions to lung cancer classification. MR analysis further demonstrated that genetically predicted expression levels of CYP1A1 and CYP1B1 were positively associated with lung cancer risk. Single-cell transcriptomic analysis revealed that CYP1A1 and CYP1B1 were predominantly expressed in epithelial cells. Molecular docking analysis suggested potential interactions between CYP1A1/CYP1B1 proteins and tobacco-related carcinogens. Furthermore, cigarette smoke extract treatment significantly increased CYP1A1 and CYP1B1 expression at both the mRNA and protein levels in BEAS-2B bronchial epithelial cells. Collectively, these findings indicate that CYP1A1 and CYP1B1 may contribute to smoking-related lung carcinogenesis through regulation of carcinogen metabolism and oxidative stress responses and may serve as potential biomarkers for identifying individuals at increased risk of smoking-associated lung cancer. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at https://doi.org/10.1007/s13205-026-05099-x.