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<p>Plasma metabolomics offers significant potential for non-invasive biomarker discovery in gastric cancer (GC), yet conventional analytical workflows face challenges in absolute quantification and biological interpretability, hindering clinical translation. Here we present an innovative multi-phase hybrid framework integrating untargeted metabolomics with relative- and absolute-quantitative targeted metabolomics, coupled with a custom interpretability-driven algorithm for de novo biomarker identification. We perform metabolic profiling on 1,706 plasma samples from multicenter cohorts, identifying 84 key metabolites significantly enriched in caffeine metabolism and primary bile acid biosynthesis during the relative quantitation phase. By applying the custom algorithm to absolute quantitation data, we establish a 12-metabolite panel covering multiple functional metabolic modules. Machine learning-based diagnostic models using this signature achieve an area under the curve of 0.951 in validation cohort. Together, our study provides a robust and interpretable framework for translational metabolomics and establishes a GC detection biomarker panel, laying the foundation for future mechanistic research and clinical application.</p>

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Multi-phase hybrid metabolomics framework identifies clinically applicable plasma signatures for early detection of gastric cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月13日

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  • Liyi Bai,
  • Fayong Hu,
  • Weiqin Zhang,
  • Huanqin Peng,
  • Haowen Peng,
  • Huan Li,
  • Xu Zhu,
  • Yibin Xie,
  • Shutian Zhang,
  • Li Min

摘要

Plasma metabolomics offers significant potential for non-invasive biomarker discovery in gastric cancer (GC), yet conventional analytical workflows face challenges in absolute quantification and biological interpretability, hindering clinical translation. Here we present an innovative multi-phase hybrid framework integrating untargeted metabolomics with relative- and absolute-quantitative targeted metabolomics, coupled with a custom interpretability-driven algorithm for de novo biomarker identification. We perform metabolic profiling on 1,706 plasma samples from multicenter cohorts, identifying 84 key metabolites significantly enriched in caffeine metabolism and primary bile acid biosynthesis during the relative quantitation phase. By applying the custom algorithm to absolute quantitation data, we establish a 12-metabolite panel covering multiple functional metabolic modules. Machine learning-based diagnostic models using this signature achieve an area under the curve of 0.951 in validation cohort. Together, our study provides a robust and interpretable framework for translational metabolomics and establishes a GC detection biomarker panel, laying the foundation for future mechanistic research and clinical application.