Integration of Network Toxicology and Machine Learning to Prioritize Candidate Molecular Features Associated with NNK-Related Lung Cancer

Abstract 4-(Methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) is a tobacco-specific nitrosamine implicated in tobacco-related lung carcinogenesis. Its biological effects in vivo depend substantially on metabolic conversion to NNAL and cytochrome P450-mediated formation of reactive intermediates. In this study, predicted NNK-associated proteins were integrated with lung cancer transcriptomic data sets, followed by network analysis and machine-learning-based feature prioritization. Five genes, SNAI2, PLAU, DNMT3A, FAP, and CCNB1, were prioritized based on their contributions to the multigene classification framework. Exploratory molecular docking generated computational ligand-placement models with modest docking scores; these results were used only as structural visualizations and do not demonstrate direct biochemical binding. In the metabolically limited A549 cell model, micromolar exposure to parent NNK produced higher CCK-8 signals, altered cell-cycle distributions, increased Transwell migration and invasion counts, and changes in candidate-gene mRNA expression. These parallel observations do not establish that the candidate genes mediate the cellular phenotypes. Moreover, because intact NNK is rapidly metabolized in vivo and is not an established biomarker of systemic exposure in human blood, urine, or saliva, the parent-compound exposure used here does not represent an established human internal-exposure scenario. This study provides a hypothesis-generating framework for prioritizing candidate molecular features for future investigation rather than direct evidence of NNK target engagement or causal gene function.