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Background <p>Upper gastrointestinal cancer (UGIC) imposes a major global health burden, yet the stage-specific molecular changes along the microbial-metabolic axis remain limited understood. We aimed to delineate this molecular landscape across UGIC progression and evaluate its potential as non-invasive methods for precision screening.</p> Results <p>Derived from a multi-center population-based UGIC screening program, we enrolled 420 individuals, stratified into normal, low-grade intraepithelial neoplasia (LGIN), high-grade intraepithelial neoplasia (HGIN), and UGIC (<i>n</i> = 105 per group). Integrated salivary metagenomics and paired salivary/plasma metabolomics were performed to capture local and systemic dysregulation. We uncovered distinct stage-specific divergence during UGIC progression: profound remodeling of the salivary microbiota (104 differential species) and salivary metabolomics (80 differential metabolites) initiated early at the LGIN stage, whereas plasma metabolic dysregulation (40 differential metabolites) peaked significantly later at the HGIN stage. Integrative analysis revealed salivary microbiota related more closely with salivary metabolome than plasma metabolome. Moreover, statistical evidence suggested that dysbiotic salivary microbiota was associated with altered lysine- and tryptophan-related catabolic pathways converging on Acetyl-CoA-related metabolic nodes, supporting a potential metabolic mechanism in precancerous lesions. Finally, the discriminative model integrating metagenomic and metabolomic markers demonstrated promising diagnostic performance in distinguishing these precancerous lesions (LGIN: area under the curve [AUC] = 0.83; HGIN: AUC = 0.77) and UGIC (AUC = 0.76) from normal.</p> Conclusion <p>This study characterizes a stage-specific microbial-metabolic axis that facilitates the comprehensive understanding of UGIC pathogenesis. These multi-biofluid signatures offer a promising non-invasive triage strategy for detecting precancerous lesions and optimizing endoscopic resource allocation.</p> <p><MediaObject ID="MOESM3"><VideoObject FileRef="MediaObjects/40168_2026_2475_MOESM3_ESM.mp4" VideoID="D_dqk9yMPkNTdMpmypLE7W"><Caption Language="En" xml:lang="en"><CaptionContent><p>Video Abstract</p></CaptionContent></Caption></VideoObject></MediaObject></p>

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Integrated landscape of salivary metagenome and multi-biofluid metabolome characterizes a microbial-metabolic axis in upper gastrointestinal cancer progression

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月28日

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  • Shuai Zhang,
  • Nan Zhang,
  • Xiaofeng Zhang,
  • Yanxiu Liu,
  • Yukun Feng,
  • Junling Xiang,
  • Jun Zhang,
  • Hengmin Ma,
  • Youhua Lu,
  • Tao Zhang

摘要

Background

Upper gastrointestinal cancer (UGIC) imposes a major global health burden, yet the stage-specific molecular changes along the microbial-metabolic axis remain limited understood. We aimed to delineate this molecular landscape across UGIC progression and evaluate its potential as non-invasive methods for precision screening.

Results

Derived from a multi-center population-based UGIC screening program, we enrolled 420 individuals, stratified into normal, low-grade intraepithelial neoplasia (LGIN), high-grade intraepithelial neoplasia (HGIN), and UGIC (n = 105 per group). Integrated salivary metagenomics and paired salivary/plasma metabolomics were performed to capture local and systemic dysregulation. We uncovered distinct stage-specific divergence during UGIC progression: profound remodeling of the salivary microbiota (104 differential species) and salivary metabolomics (80 differential metabolites) initiated early at the LGIN stage, whereas plasma metabolic dysregulation (40 differential metabolites) peaked significantly later at the HGIN stage. Integrative analysis revealed salivary microbiota related more closely with salivary metabolome than plasma metabolome. Moreover, statistical evidence suggested that dysbiotic salivary microbiota was associated with altered lysine- and tryptophan-related catabolic pathways converging on Acetyl-CoA-related metabolic nodes, supporting a potential metabolic mechanism in precancerous lesions. Finally, the discriminative model integrating metagenomic and metabolomic markers demonstrated promising diagnostic performance in distinguishing these precancerous lesions (LGIN: area under the curve [AUC] = 0.83; HGIN: AUC = 0.77) and UGIC (AUC = 0.76) from normal.

Conclusion

This study characterizes a stage-specific microbial-metabolic axis that facilitates the comprehensive understanding of UGIC pathogenesis. These multi-biofluid signatures offer a promising non-invasive triage strategy for detecting precancerous lesions and optimizing endoscopic resource allocation.

Video Abstract