Fig. 1: Schematic overview of the study.

Overview of the study design. The illustration was created with a full license on BioRender.com. A total of 702 individuals were included in the study, and their plasma samples underwent targeted metabolomics analysis. The metabolic profiles of gastric cancer (GC) patients and non-GC controls (NGC) in Cohort 1 (n = 426) were compared to depict the metabolic reprogramming in GC. Using the metabolomics data from Cohort 1 and machine learning techniques, a diagnostic model for GC (10-DM model) was created and validated. This model was further verified in the test set 2 (Cohort 2, n = 95). Metabolomics data from Cohort 3 (n = 181) patients and their clinical features were analyzed using a machine learning algorithm to develop a prognostic model (28-PM model). The performance of these two models was benchmarked against clinically used biomarkers/clinical features. Different colored triangles in the figure represent various participant groups used for model construction, validation, and comparison processes. Source data are provided as a Source Data file.