Integrating network toxicology, machine learning and molecular dynamics simulation: Revealing the dynamic molecular mechanism by which 6PPD regulates lung cancer progression

The antioxidant N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylenediamine (6PPD), widely used in tire rubber production, and its oxidative transformation product 6PPD-quinone (6PPD-Q), have become emerging urban environmental pollutants distributed worldwide, which can cause respiratory exposure in humans through atmospheric particulate matter and aerosols, potentially damaging the respiratory system; moreover, studies have suggested that such exposure is associated with the occurrence and development of lung cancer. In this study, a multidimensional computational research framework integrating network toxicology, weighted gene co-expression network analysis (WGCNA), machine learning, molecular docking, and molecular dynamics simulation was constructed to systematically decipher the dynamic molecular mechanisms by which 6PPD regulates lung cancer progression. Through cross-screening of multiple databases, 35 common interaction targets between 6PPD and lung cancer were identified, and candidate hub genes were screened out. GO and KEGG analyses showed that the targets were enriched in cancer-related pathways such as chemical carcinogenesis-reactive oxygen species and VEGF, suggesting that oxidative stress may be the core regulatory direction. The Ridge machine learning prediction model achieved an accuracy of 1.000 (AUC = 0.985) in the training set, and also exhibited good generalization ability in two independent validation datasets, and further screened out CA4, CDC25C, and NQO1 as core biomarkers; all three were significantly overexpressed in lung cancer tissues and exhibited good diagnostic efficacy for lung cancer. Molecular docking and 100 ns molecular dynamics simulation showed that 6PPD possessed strong spontaneous binding ability toward NOX4 (−8.0 kcal/mol), NQO1 (−7.9 kcal/mol), KDR, CA4, and CDC25C, and the complexes formed with the core targets remained generally stable throughout the simulations, suggesting that 6PPD can persistently bind to target proteins through stable intermolecular interactions. Trajectory hierarchical clustering analysis further showed that the conformational dynamic characteristics of each complex differed; among them, the KDR conformation was the most stable, whereas NQO1 and CA4 showed higher conformational diversity. In summary, the results of this study suggest that 6PPD may participate in oxidative stress pathway activation, DNA damage, cell cycle regulation, and tumor angiogenesis through multi-target synergistic effects, and be associated with lung cancer progression; this provides a computational pathway for deciphering the carcinogenic molecular mechanisms of environmental pollutants and assessing respiratory carcinogenic risk, and the core targets may provide candidate molecular markers for early warning and intervention of lung cancer.