ArticleMetabolomics : Official journal of the Metabolomic Society2023
Plasma metabolome identifies potential biomarkers of gastric precancerous lesions and gastric cancer risk.
Article in Metabolomics : Official journal of the Metabolomic Society, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.
- Metabolomic profiling of upper GI malignancies in blood and tissue: a systematic review and meta-analysis.Journal of cancer research and clinical oncology · 2024Pooled it
- Endoscopic and histopathological phenotypes of early gastric neoplasia: toward an integrative host-response framework.Frontiers in oncology · 2026Review
- Exploratory Serum Metabolomics Identifies Metabolic Subgroups Across the Gastric Dysplasia-Early Cancer Spectrum.Journal of Cancer · 2026Article
- [Analysis of Plasma Metabolic Profile Characteristics in Gouty Arthritis With Dampness Syndrome].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2025Article
- Causal association of blood metabolites, immune cells, and lung cancer: A mediation Mendelian randomization study.Medicine · 2025Article
- Article
- Analysis of metabolic characteristics of metabolic syndrome in elderly patients with gastric cancer by non-targeted metabolomics.World journal of gastrointestinal oncology · 2024Article
- Article
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesCurrently, metabolic biomarkers with great practicability of gastric cancer (GC) and gastric precancerous lesions (GPL) are scarce. Thus, we are devoted to determining the plasma metabolic profiles of patients with GPL or GC and validate candidate biomarkers for disease diagnosis.
methodsIn this hospital-based case-control study, 68 plasma samples from 27 non-atrophic gastritis (NAG, control), 31 GPL, and 10 GC patients were collected for targeted metabolomics analysis. Univariate and multivariate analyses were used for selecting the differential metabolites. A receiver operating characteristic curve combined with binary logistic regression analysis was performed to test the diagnostic performance of the differential metabolites. Dietary data were obtained using a semiquantitative food frequency questionnaire.
resultsDistinct metabolomic profiles were noted for NAG, GPL, and GC. Compared to the NAG patients, the levels of 5 metabolites in the GPL group and 4 metabolites in the GC group were found to significantly elevate. Compared with the model involving 9 traditional risk factors (AUC: 0.89, 95%CI: 0.78-1.00), Trimethylamine N-oxide, the most significant metabolite (P = 2.00 × 10
conclusionIn our study, 9 defined metabolites might serve as meaningful biomarkers for identifying the high-risk population of GPL and GC, possibly enhancing the prevention and control of GPL and GC.
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