| Title: | A trans-omics gene-smoking interaction study of lung cancer based on consortium data. |
| Journal: | American Journal of Respiratory and Critical Care Medicine |
| Published: | 23 Jan 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41738161/ |
| DOI: | https://doi.org/10.1093/ajrccm/aamaf097 |
Publication 18490
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Abstract
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.</p>
OBJECTIVES: To identify trans-omics gene-smoking interactions affecting lung cancer risk and to assess how biomarkers modify effect of smoking.</p>
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.</p>
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.</p>
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/).</p>
19 Authors
- Ning Xie
- Xiaowen Xu
- Yanru Wang
- Aoxuan Wang
- Xiang Wang
- Xuan Wang
- Mengshen Zhao
- Jiacheng Zhou
- Yongyue Wei
- Manel Esteller
- Zhibin Hu
- Hongbing Shen
- Rayjean J Hung
- Christopher I Amos
- Yi Li
- David C Christiani
- Feng Chen
- Yang Zhao
- Ruyang Zhang
Enabling scientific discoveries that improve human health