Metabolomic machine learning predictor for diagnosis and prognosis of gastric cancer

Yangzi Chen, Bohong Wang, Yizi Zhao, Xinxin Shao, Mingshuo Wang, Fuhai Ma, Laishou Yang, Meng Nie, Peng Jin, Ke Yao, Haibin Song, Shenghan Lou, Hang Wang, Tianshu Yang, Yantao Tian (), Peng Han () and Zeping Hu ()
Additional contact information
Yangzi Chen: Tsinghua University
Bohong Wang: Tsinghua University
Yizi Zhao: Tsinghua University
Xinxin Shao: Peking Union Medical College
Mingshuo Wang: Tsinghua University
Fuhai Ma: Peking Union Medical College
Laishou Yang: Harbin Medical University Cancer Hospital
Meng Nie: Tsinghua University
Peng Jin: Peking Union Medical College
Ke Yao: Tsinghua University
Haibin Song: Harbin Medical University Cancer Hospital
Shenghan Lou: Harbin Medical University Cancer Hospital
Hang Wang: Harbin Medical University Cancer Hospital
Tianshu Yang: Fudan University
Yantao Tian: Peking Union Medical College
Peng Han: Harbin Medical University Cancer Hospital
Zeping Hu: Tsinghua University

Nature Communications, 2024, vol. 15, issue 1, 1-13

Abstract: Abstract Gastric cancer (GC) represents a significant burden of cancer-related mortality worldwide, underscoring an urgent need for the development of early detection strategies and precise postoperative interventions. However, the identification of non-invasive biomarkers for early diagnosis and patient risk stratification remains underexplored. Here, we conduct a targeted metabolomics analysis of 702 plasma samples from multi-center participants to elucidate the GC metabolic reprogramming. Our machine learning analysis reveals a 10-metabolite GC diagnostic model, which is validated in an external test set with a sensitivity of 0.905, outperforming conventional methods leveraging cancer protein markers (sensitivity

Date: 2024
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DOI: 10.1038/s41467-024-46043-y

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