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Metabolomic machine learning predictor for diagnosis and prognosis of gastric cancer

Author

Listed:
  • Yangzi Chen

    (Tsinghua University)

  • Bohong Wang

    (Tsinghua University
    Tsinghua University)

  • Yizi Zhao

    (Tsinghua University)

  • Xinxin Shao

    (Peking Union Medical College)

  • Mingshuo Wang

    (Tsinghua University
    Tsinghua University)

  • Fuhai Ma

    (Peking Union Medical College
    Chinese Academy of Medical Sciences)

  • Laishou Yang

    (Harbin Medical University Cancer Hospital)

  • Meng Nie

    (Tsinghua University)

  • Peng Jin

    (Peking Union Medical College
    Tianjin’s Clinical Research Center for Cancer)

  • 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
    Shanghai Qi Zhi Institute)

  • Yantao Tian

    (Peking Union Medical College)

  • Peng Han

    (Harbin Medical University Cancer Hospital
    Key Laboratory of Tumor Immunology in Heilongjiang)

  • Zeping Hu

    (Tsinghua University
    Tsinghua University)

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

Suggested Citation

  • 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 & Pen, 2024. "Metabolomic machine learning predictor for diagnosis and prognosis of gastric cancer," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-46043-y
    DOI: 10.1038/s41467-024-46043-y
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    References listed on IDEAS

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    1. Yongjie Deng & Yao Yao & Yanni Wang & Tiantian Yu & Wenhao Cai & Dingli Zhou & Feng Yin & Wanli Liu & Yuying Liu & Chuanbo Xie & Jian Guan & Yumin Hu & Peng Huang & Weizhong Li, 2024. "An end-to-end deep learning method for mass spectrometry data analysis to reveal disease-specific metabolic profiles," Nature Communications, Nature, vol. 15(1), pages 1-17, December.

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