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<p>Lung cancer is the world's leading cause of cancer deaths. Early detection through low-cost metabolite screening approaches could reduce lung cancer mortality. Here we report the results of a large-scale, quantitative metabolomics study aimed at identifying plasma biomarkers for lung cancer detection in a Chinese population. A cohort of 410 patients, including 137 healthy controls, 189 biopsy-confirmed individuals with stage I/II lung cancer, and 84 individuals with biopsy-confirmed stage III/IV cancer, were studied. Plasma samples were collected and analyzed using an in-house-developed, targeted liquid chromatography-mass spectrometry (LC-MS) metabolomics method that detects and quantifies 138 metabolites. Logistic regression was used to develop an optimal biomarker panel that could distinguish all-stage lung cancer patients from healthy controls. The resulting six-metabolite panel achieved an area under the curve (AUC) of 95.0%. A second biomarker panel was developed to distinguish early-stage lung cancer from healthy controls and reached an AUC of 94.3%. All models were developed on an initial training set and then fully validated on a separate holdout set. Our proposed biomarker models show significant improvements over previously published models for metabolite-based lung cancer diagnosis and detection. These metabolite biomarker panels are intended for the development of a low-cost, minimally invasive blood test for lung cancer screening in China.</p>

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Identification of High-Performing Blood Metabolite Biomarkers of Lung Cancer in a Chinese Population

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月1日

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  • Zhenpu Chen,
  • Lun Zhang,
  • Kaining Mao,
  • Jia Li,
  • Yongchun Zhou,
  • Jiamin Zheng,
  • Marcia LeVatte,
  • David S. Wishart,
  • Youguang Huang,
  • Yunchao Huang,
  • Jie Chen

摘要

Lung cancer is the world's leading cause of cancer deaths. Early detection through low-cost metabolite screening approaches could reduce lung cancer mortality. Here we report the results of a large-scale, quantitative metabolomics study aimed at identifying plasma biomarkers for lung cancer detection in a Chinese population. A cohort of 410 patients, including 137 healthy controls, 189 biopsy-confirmed individuals with stage I/II lung cancer, and 84 individuals with biopsy-confirmed stage III/IV cancer, were studied. Plasma samples were collected and analyzed using an in-house-developed, targeted liquid chromatography-mass spectrometry (LC-MS) metabolomics method that detects and quantifies 138 metabolites. Logistic regression was used to develop an optimal biomarker panel that could distinguish all-stage lung cancer patients from healthy controls. The resulting six-metabolite panel achieved an area under the curve (AUC) of 95.0%. A second biomarker panel was developed to distinguish early-stage lung cancer from healthy controls and reached an AUC of 94.3%. All models were developed on an initial training set and then fully validated on a separate holdout set. Our proposed biomarker models show significant improvements over previously published models for metabolite-based lung cancer diagnosis and detection. These metabolite biomarker panels are intended for the development of a low-cost, minimally invasive blood test for lung cancer screening in China.