A trans-omics gene-smoking interaction study of lung cancer based on consortium data

Rationale Genetically predicted molecular traits provide a cost-effective approach for identifying biomarkers and uncovering underlying biological mechanisms. We extended this framework to investigate gene-smoking interactions in lung cancer susceptibility. Objectives To identify trans-omics gene-smoking interactions affecting lung cancer risk and to assess how biomarkers modify effect of smoking. Methods We conducted the first trans-omics gene-smoking interaction study of lung cancer by integrating consortium-scale individual genotype data (27,737 cases vs 449,910 non-cases) from the International Lung Cancer OncoArray Consortium (ILCCO-OncoArray), Transdisciplinary Research Into Cancer of the Lung (TRICL), Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO), and the UK Biobank (UKB) with alliance-based summary-level molecular quantitative trait loci (xQTL) data, involving DNA methylation, gene expression, protein, and metabolite. Based on the identified biomarkers, we developed a molecular modifying score (MMS) to delineate gene-smoking interaction patterns and stratify high-risk smokers of lung cancer. Measurements and Main Results Eight biomarkers showing significant interactions with smoking were identified through a two-phase analytic strategy, comprising CpG sites in the nicotinic acetylcholine receptor region and gene RP11-326C3.14. The MMS, constructed by integrating these biomarkers with their effect estimates derived from meta-analysis of all available datasets, effectively stratified lung cancer risk among smokers. Trans-omics integrative analysis revealed functional relationships across molecular layers, particularly implicating the NELFE gene in smoking-related carcinogenesis pathways. Conclusions The xWAS framework enables systematic discovery of trans-omics gene-environment interactions. The MMS effectively delineates the patterns of the interaction effects and facilitates risk stratification. Additionally, we launched a free online platform, LungCancer-xWAS-GxE (http://bigdata.njmu.edu.cn/LungCancer-xWAS-GxE/).