Metabolomic machine learning predictor for diagnosis and prognosis of gastric cancer

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 ris...

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Published inNature communications Vol. 15; no. 1; p. 1657
Main Authors Chen, Yangzi, Wang, Bohong, Zhao, Yizi, Shao, Xinxin, Wang, Mingshuo, Ma, Fuhai, Yang, Laishou, Nie, Meng, Jin, Peng, Yao, Ke, Song, Haibin, Lou, Shenghan, Wang, Hang, Yang, Tianshu, Tian, Yantao, Han, Peng, Hu, Zeping
Format Journal Article
LanguageEnglish
Published England Nature Publishing Group 23.02.2024
Nature Portfolio
Subjects
Biomarkers
Cancer
Diagnosis
Endoscopy
Gastric cancer
Humans
Learning algorithms
Machine Learning
Medical prognosis
Metabolic Reprogramming
Metabolism
Metabolites
Metabolomics
Precision Medicine
Prognosis
Risk groups
Sensitivity
Sensitivity analysis
Stomach Neoplasms - diagnosis
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Summary: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 < 0.40). Additionally, our machine learning-derived prognostic model demonstrates superior performance to traditional models utilizing clinical parameters and effectively stratifies patients into different risk groups to guide precision interventions. Collectively, our findings reveal the metabolic landscape of GC and identify two distinct biomarker panels that enable early detection and prognosis prediction respectively, thus facilitating precision medicine in GC.
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ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-024-46043-y