Metabolic signatures for gastric cancer diagnosis and mechanistic insights: a multicenter study

Early detection of gastric cancer (GC) is critical for improving prognosis, yet conventional biomarkers lack sensitivity and specificity, necessitating non-invasive, high-performance diagnostic tools. This study integrated untargeted metabolomics and machine learning to develop a plasma metabolite panel for GC diagnosis and mechanistic insights. Plasma and tissue samples from two cohorts (n = 597) were analyzed using ultra-performance liquid chromatography-mass spectrometry (UPLC-MS). A six-metabolite panel was identified and validated, demonstrating excellent diagnostic performance (area under the curve: 0.947-0.982 in discovery; 0.920-0.951 in validation) and superior sensitivity (0.900-0.940) compared to conventional markers (0.020-0.240). Isovalerylcarnitine (C5), a key component, was consistently downregulated in both plasma and tissue samples. Mendelian randomization supported a causal relationship between isovalerylcarnitine (C5) and GC risk. Proteomic analyses revealed inverse correlations between C5 and cadherin/MMP family proteins. Functional assays confirmed that isovalerylcarnitine (C5) inhibited GC cell migration and invasion via calpain-mediated cleavage of VE-cadherin and MMP2. This study identifies a robust diagnostic metabolite panel for GC detection and highlights a novel mechanistic role of isovalerylcarnitine (C5) in GC progression, supporting its utility as both a biomarker and therapeutic target.

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Publication Details

Journal
EMBO Molecular Medicine
Published
2025-10-27
DOI
https://doi.org/10.1038/s44321-025-00325-0
Citations
2
Primary Topic
Liver Disease Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
2.71
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article

Metabolic signatures for gastric cancer diagnosis and mechanistic insights: a multicenter study

Juan Zhu, Lingbin Du, Yingying Mao, B. Liu et al.
2 citations
EMBO Molecular Medicine
Liver Disease Diagnosis and Treatment
2.71
article

Metabolic signatures for gastric cancer diagnosis and mechanistic insights: a multicenter study

Juan Zhu, Lingbin Du, Yingying Mao, B. Liu, Yida Huang, Yongjie Xu, Le Wang, Xue Li, Yuan Li, Kun Qian, Xiangdong Cheng
article en
2 citations

Abstract

Early detection of gastric cancer (GC) is critical for improving prognosis, yet conventional biomarkers lack sensitivity and specificity, necessitating non-invasive, high-performance diagnostic tools. This study integrated untargeted metabolomics and machine learning to develop a plasma metabolite panel for GC diagnosis and mechanistic insights. Plasma and tissue samples from two cohorts (n = 597) were analyzed using ultra-performance liquid chromatography-mass spectrometry (UPLC-MS). A six-metabolite panel was identified and validated, demonstrating excellent diagnostic performance (area under the curve: 0.947-0.982 in discovery; 0.920-0.951 in validation) and superior sensitivity (0.900-0.940) compared to conventional markers (0.020-0.240). Isovalerylcarnitine (C5), a key component, was consistently downregulated in both plasma and tissue samples. Mendelian randomization supported a causal relationship between isovalerylcarnitine (C5) and GC risk. Proteomic analyses revealed inverse correlations between C5 and cadherin/MMP family proteins. Functional assays confirmed that isovalerylcarnitine (C5) inhibited GC cell migration and invasion via calpain-mediated cleavage of VE-cadherin and MMP2. This study identifies a robust diagnostic metabolite panel for GC detection and highlights a novel mechanistic role of isovalerylcarnitine (C5) in GC progression, supporting its utility as both a biomarker and therapeutic target.

EMBO Molecular MedicineVol. 17(12)
Zhejiang Chinese Medical University (CN), Shanghai Jiao Tong University (CN), Chinese Academy of Medical Sciences & Peking Union Medical College (CN), Wenzhou Medical University (CN), Renji Hospital (CN), National Cancer Center (US), Zhejiang Cancer Hospital (CN), Shanghai Cancer Institute (CN), Hangzhou Cancer Hospital (CN)
Openalex Percentile: Top 8%
Liver Disease Diagnosis and Treatment
2.71
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