A nested case-control study of untargeted plasma metabolomics and lung cancer among never-smoking women within the prospective Shanghai Women's Health Study.
A nested case-control study of untargeted plasma metabolomics and lung cancer among never-smoking women within the prospective Shanghai Women's Health Study.
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DOI：
10.1002/ijc.34929
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发表时间：
2024-04
影响因子：
6.4
通讯作者：
Mohammad L. Rahman;Xiao-Ou Shu;Dean P Jones;Wei Hu;B. Ji;Batel Blechter;Jason Y Y Wong-Jason-Y-Y-Wong-2290867708;Q. Cai-Q
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文献类型：
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作者：
Mohammad L. Rahman;Xiao-Ou Shu;Dean P Jones;Wei Hu;B. Ji;Batel Blechter;Jason Y Y Wong-Jason-Y-Y-Wong-2290867708;Q. Cai-Q
尽管全世界15%的男性肺癌病例和53%的女性肺癌病例与吸烟无关，但从不吸烟的人患肺癌的病因仍然难以捉摸。在这里，我们的目的是利用非靶向代谢组学来提高我们对不吸烟人群中肺癌发病机制的理解。这项嵌套式病例对照研究包括395名从不吸烟的肺癌女性和395名匹配的从不吸烟的非癌症女性，这些女性来自未来的上海妇女健康研究，其中15,353名代谢特征在诊断前的血浆中用高效液相色谱-高分辨率质谱仪进行了量化。认识到代谢物在生物过程中经常相互关联，很少独立起作用，我们利用加权相关网络分析以不可知性的方式构建了28个相关代谢物的网络模块。使用条件Logistic回归模型，我们评估了代谢网络模块和个体代谢特征与肺癌的相关性，并使用错误发现率(FDR；0.20)解释了多重测试。我们确定了一个由121个特征组成的网络模块，这些特征与所有肺癌(p=.001，fdr=0.028)和肺腺癌(p=.002，fdr=0.056)负相关，其中溶甘油磷脂起到了推动这些关联的关键作用。440个特征的另一个模块与肺腺癌呈负相关(p=0.014，fdr=0.196)。这些网络模块中的个体代谢物在与氧化应激和能量代谢相关的生物途径中得到丰富。这些途径在以前的代谢组学研究中被涉及，这些研究涉及暴露于已知肺癌风险因素的人群，如交通相关的空气污染和多环芳烃。我们的结果表明，非靶向血浆代谢组学可以为从不吸烟的人中肺癌的病因和危险因素提供新的见解。
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) < 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.