代谢组学
癌症
生物标志物
心理干预
机器学习
精密医学
生物标志物发现
计算机科学
重编程
医学
肿瘤科
人工智能
生物信息学
内科学
生物
蛋白质组学
病理
生物化学
遗传学
精神科
基因
细胞
作者
Yangzi Chen,Bohong Wang,Yan 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,Zeping Hu
标识
DOI:10.1038/s41467-024-46043-y
摘要
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 < 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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