ArticleFrontiers in oncology2026
Urinary metabolomics-based machine learning for diagnosis of early gastric neoplasia: a retrospective diagnostic case-control study.
Article in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objectives: Early gastric cancer (EGC) is often asymptomatic. Non-invasive and scalable screening tools remain limited. We aimed to develop and validate a urinary metabolomics-based machine-learning model for detecting early gastric neoplasia (EGN), including high-grade intraepithelial neoplasia (HGIN) and EGC, and to benchmark it against routine serum markers. Methods: This was a retrospective diagnostic case-control study using archived urine specimens collected during routine clinical care, with internal split-sample validation and an independent temporal validation cohort. Morning urine samples from two cohorts were profiled by liquid chromatography-mass spectrometry (LC-MS). Cohort 1 was split into a training set and a testing set. LASSO regression was used to identify candidate features, and a random-forest classifier was developed. An external targeted-quantification cohort was used for validation and single-biomarker assessment. Results: In this retrospective diagnostic case-control study, urinary metabolomics combined with machine learning was used to establish a three-metabolite diagnostic model (3-DM). In the internal testing set, the 3-DM showed discriminative potential with an AUROC of 0.81 (95% CI: 0.600-0.963). In the independent validation cohort, only 1-methylnicotinamide (1-MNA) remained significantly different between NC and EGN, whereas 3-pyridylacetic acid (3-PAA) and phenylacetylglutamine (PAGln) did not show consistent inter-cohort performance. 1-MNA showed a gradual decrease with disease progression, suggesting that it may represent the most reproducible candidate biomarker in this study. Conclusions: These preliminary findings suggest that urinary metabolomic profiling may have potential for non-invasive detection of EGN. Among the candidate metabolites, 1-MNA showed the most reproducible cross-cohort signal, but its diagnostic performance and clinical applicability require further validation in larger prospective cohorts.
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