肺がんにおけるゲノム・喫煙相互作用に関するトランスオミクス研究：コンソーシアムデータに基づく解析

Ning Xie¹, Xiaowen Xu¹, Yanru Wang¹

¹Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China.

American journal of respiratory and critical care medicine

|February 25, 2026

要旨

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/).

キーワード:

遺伝子・喫煙相互作用ゲノムワイド関連解析肺がんトランスオミクス xWAS

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