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

Author:

Rahman Mohammad L.1ORCID,Shu Xiao‐Ou2,Jones Dean P.3,Hu Wei1,Ji Bu‐tian1,Blechter Batel1ORCID,Wong Jason Y. Y.1ORCID,Cai Qiuyin2,Yang Gong2,Gao Yu‐Tang4,Zheng Wei2,Rothman Nathaniel1ORCID,Walker Douglas5,Lan Qing1

Affiliation:

1. Division of Cancer Epidemiology and Genetics National Cancer Institute Bethesda Maryland USA

2. Division of Epidemiology Vanderbilt University Nashville Tennessee USA

3. Clinical Biomarkers Laboratory, Department of Medicine Emory University Atlanta Georgia USA

4. Department of Epidemiology Shanghai Cancer Institute Shanghai China

5. Division of Environmental Health, School of Public Health Emory University Atlanta Georgia USA

Abstract

AbstractThe 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) < 0.20. We identified a network module of 121 features inversely associated with all lung cancer (p = .001, FDR = 0.028) and lung adenocarcinoma (p = .002, FDR = 0.056), where lyso‐glycerophospholipids played a key role driving these associations. Another module of 440 features was inversely associated with lung adenocarcinoma (p = .014, FDR = 0.196). Individual metabolites within these network modules were enriched in biological pathways linked to oxidative stress, and energy metabolism. These pathways have been implicated in previous metabolomics studies involving populations exposed to known lung cancer risk factors such as traffic‐related air pollution and polycyclic aromatic hydrocarbons. Our results suggest that untargeted plasma metabolomics could provide novel insights into the etiology and risk factors of lung cancer among never‐smokers.

Funder

National Institutes of Health

Publisher

Wiley

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