A nested case‐control study of untargeted plasma metabolomics and lung cancer among never‐smoking women within the prospective Shanghai Women's Health Study

• Published in: International journal of cancer 2024-08, Vol.155 (3), p.508-518
• Main Authors: Rahman, Mohammad L., Shu, Xiao‐Ou, Jones, Dean P., Hu, Wei, Ji, Bu‐tian, Blechter, Batel, Wong, Jason Y. Y., Cai, Qiuyin, Yang, Gong, Gao, Yu‐Tang, Zheng, Wei, Rothman, Nathaniel, Walker, Douglas, Lan, Qing
• Format: Article
• Language: English
• Subjects: Adenocarcinoma; Aged; Air pollution; Biomarkers, Tumor - blood; Case-Control Studies; China - epidemiology; Energy metabolism; Etiology; Female; Humans; Liquid chromatography; Lung cancer; Lung Neoplasms - blood; Lung Neoplasms - epidemiology; Lung Neoplasms - etiology; Mass spectrometry; Mass spectroscopy; Metabolic networks; Metabolic Networks and Pathways; Metabolism; Metabolites; Metabolomics; Metabolomics - methods; Middle Aged; network analysis; never‐smokers; Non-Smokers - statistics & numerical data; Oxidative metabolism; Oxidative stress; Pathogenesis; Polycyclic aromatic hydrocarbons; Population studies; Prospective Studies; Regression analysis; Risk Factors; Smoking; Smoking - adverse effects; Smoking - blood; Women's Health; Womens health
• ISSN: 0020-7136; 1097-0215; 1097-0215
• DOI: 10.1002/ijc.34929

The etiology of lung cancer in never‐smokers remains elusive, despite 15% of lung cancer cases in men and 53% in women worldwide being unrelated to smoking. Here, we aimed to enhance our understanding of lung cancer pathogenesis among never‐smokers using untargeted metabolomics. This nested case‐control study included 395 never‐smoking women who developed lung cancer and 395 matched never‐smoking cancer‐free women from the prospective Shanghai Women's Health Study with 15,353 metabolic features quantified in pre‐diagnostic plasma using liquid chromatography high‐resolution mass spectrometry. Recognizing that metabolites often correlate and seldom act independently in biological processes, we utilized a weighted correlation network analysis to agnostically construct 28 network modules of correlated metabolites. Using conditional logistic regression models, we assessed the associations for both metabolic network modules and individual metabolic features with lung cancer, accounting for multiple testing using a false discovery rate (FDR)