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

Microbial alpha-diversity decreased significantly with gastric cancer progression, with the most pronounced decline occurring at the atrophy stage (G3).

• Five groups were recruited: normal controls (G1), gastritis (G2), atrophy (G3), erosion (G4), and gastric cancer (G5).
• Alpha-diversity indices were assessed and showed significant reduction particularly in G3 (atrophy).
• Both fecal and gastric tissue samples were analyzed using 16S rRNA sequencing.
• The atrophy stage was identified as a distinct metabolic and microbial 'breakpoint' along the Correa sequence.

Compositional shifts in gut microbiota across the Correa sequence included depletion of Bacteroides and Faecalibacterium alongside enrichment of Actinobacteria, Peptostreptococcaceae, and Lachnoclostridium.

• Beta-diversity analyses confirmed significant compositional differences between groups.
• LEfSe (linear discriminant analysis effect size) was used to identify stage-specific microbial biomarkers.
• Bifidobacterium and Oscillospiraceae were identified by LEfSe as potential biomarkers of advanced stages.
• Functional prediction was also performed on the 16S rRNA sequencing data to infer metabolic capacities of microbial communities.

The class Actinobacteria achieved an AUC of 0.935 in ROC analysis distinguishing normal controls from gastric cancer patients.

• ROC curve analyses were used to evaluate the diagnostic potential of microbial features.
• Actinobacteria showed 'strong discriminatory power' with an AUC of 0.935 for controls vs. GC.
• Combined microbiota-metabolite signatures were assessed for non-invasive early detection and disease stratification potential.
• Multiple microbial taxa were evaluated as candidate biomarkers across disease stages.

Fecal metabolomics revealed reductions in anti-inflammatory short-chain fatty acids (SCFAs) and increases in pro-inflammatory metabolites beginning at the atrophy stage (G3).

• Untargeted metabolomics was performed using UHPLC-Q Exactive Orbitrap MS under both positive and negative ion modes.
• Differential metabolites were identified using t-tests and partial least squares discriminant analysis (PLS-DA).
• SCFA reductions and pro-inflammatory metabolite increases emerged specifically at G3, marking it as a metabolic inflection point.
• Erosion (G4) exhibited transitional metabolic features rather than a distinct breakpoint.

Tissue metabolomics in gastric cancer (G5) showed broad metabolic reprogramming involving amino acid, nucleotide, lipid, and energy metabolism.

• Gastric tissue samples were analyzed alongside fecal samples for metabolomic profiling.
• The reprogramming observed in GC tissue was described as broader than changes seen at earlier stages.
• Both fecal and tissue metabolomics were conducted under positive and negative ion modes using UHPLC-Q Exactive Orbitrap MS.
• Stage-specific metabolic alterations were delineated across all five groups of the Correa sequence.

Atrophy (G3) was identified as a pivotal inflection point in carcinogenesis while erosion (G4) served as a transitional state between atrophy and gastric cancer.

• This conclusion was based on integrated analysis of both microbial and metabolomic data across the five Correa sequence stages.
• Atrophy marked distinct shifts in both alpha-diversity and fecal metabolite profiles, particularly SCFA reductions.
• Erosion exhibited 'transitional features' in both microbial composition and metabolomics rather than a distinct new pattern.
• The authors described atrophy as representing the transition 'from homeostasis to carcinogenesis.'

The study employed a multi-omics approach integrating 16S rRNA-based microbiome profiling with untargeted metabolomics from both fecal and gastric tissue samples across five disease stages.

• Participants were recruited across five groups: normal controls (G1), gastritis (G2), atrophy (G3), erosion (G4), and gastric cancer (G5).
• Metabolomics was conducted using UHPLC-Q Exactive Orbitrap MS in both positive and negative ion modes.
• Microbial community analysis included alpha- and beta-diversity indices, LEfSe, and functional prediction.
• The study is described as an 'exploratory' analysis, suggesting hypothesis-generating rather than confirmatory design.