Gut Microbiota and Metabolome Dynamics Along Gastric Cancer Progression: An Exploratory Multi-Omics Analysis

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Background: Gastric cancer (GC) remains a major global health burden, particularly in East Asia, with complex etiologies involving Helicobacter pylori infection, diet, host genetics, and environmental exposures. GC development follows the Correa sequence (CS), a multistep cascade from gastritis to atrophy, erosion, and carcinoma. Although gut microbiota (GM) dysbiosis and metabolic reprogramming have each been implicated in GC, their integrated dynamics across CS remain incompletely defined. Methods: We recruited participants across five groups: normal controls (G1), gastritis (G2), atrophy (G3), erosion (G4), and GC (G5). Fecal and gastric tissue samples were analyzed using 16S rRNA sequencing and untargeted metabolomics under both ion modes. Microbial diversity was assessed by α- and β-diversity indices, linear discriminant analysis effect size (LEfSe), and functional prediction. Metabolic features were profiled by UHPLC-Q Exactive Orbitrap MS, and differential metabolites were identified using t-tests and partial least squares discriminant analysis (PLS-DA). Diagnostic potential was evaluated using receiver operating characteristic (ROC) curves. Results: Microbial α-diversity decreased significantly with progression, particularly in G3, while compositional shifts included depletion of Bacteroides and Faecalibacterium alongside enrichment of Actinobacteria, Peptostreptococcaceae, and Lachnoclostridium. LEfSe identified Bifidobacterium and Oscillospiraceae as potential biomarkers of advanced stages. ROC analyses demonstrated strong discriminatory power, with the class Actinobacteria achieving an area under the ROC curve (AUC) of 0.935 in distinguishing controls from GC. Fecal metabolomics revealed reductions in anti-inflammatory short-chain fatty acids (SCFAs) and increases in pro-inflammatory metabolites emerging at G3, while tissue metabolomics showed broader reprogramming in GC involving amino acid, nucleotide, lipid, and energy metabolism. Notably, erosion (G4) exhibited transitional features, whereas atrophy (G3) marked a distinct metabolic “breakpoint”. Conclusions: By integrating GM and metabolomic data, this study delineates stage-specific microbial and metabolic alterations along the CS. Atrophy represents a pivotal inflection point in the transition from homeostasis to carcinogenesis, while erosion serves as a transitional state. Combined microbiota–metabolite signatures hold promise for non-invasive early detection, disease stratification, and mechanistic insights into metabolic dependencies in GC.

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