Urinary metabolomics and proteomics for early detection of gastric cancer: insights from a two-center multicenter study

Aims: This study aimed to develop a non-invasive method combining urinary metabolomics and proteomics to identify biomarkers for gastric cancer and to elucidate the molecular mechanisms associated with its progression. Methods: Urine samples from 30 advanced gastric cancer (AGC), 30 early gastric cancer (EGC), and 30 healthy controls (CG) were collected at two centers. Differential metabolites were identified using the UHPLC-MS instrument (VIP>1, FDR<0.05, |log2FC|≥1), and proteins were quantified using the TMT-based proteomics approach (VIP>1, FDR<0.05, |log2FC| ≥ 1.2). Key metabolites were selected via Random Forest and Boruta algorithms, and proteomic findings were validated using TCGA data. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to explore enriched pathways. Results: A total of 350 differential metabolites were identified in AGC vs. CG, and 285 in EGC vs. CG, primarily involving amino acid, bile acid, and energy metabolism. Core metabolites such as butyrate, indolelactic acid, D-ribose-5-phosphate, and serine were included in diagnostic models. Proteomic analysis identified 376 differentially abundant proteins in AGC and 191 in EGC, enriched in immune response, cell adhesion, and protein hydrolysis. Key proteins in AGC included TNFRSF12A, ITGB3, HSPA8, and FTL, with significant upregulation or downregulation observed for each. For example, TNFRSF12A was upregulated (p < 0.05), while HSPA8 was downregulated (p < 0.05), and these proteins were linked to pathways such as cell adhesion molecules (CAMs), ECM-receptor interaction, and platelet activation. In EGC, proteins like ITGB3 and FTL were significantly upregulated (p < 0.05), with involvement in pathways such as HIF-1 signaling, glycolysis/gluconeogenesis, and antigen processing/presentation. Integrated analysis revealed 43 significantly enriched KEGG pathways in AGC and 30 in EGC, with notable pathways in amino acid metabolism, the TCA cycle, PI3K-Akt signaling, and immune response pathways. These findings highlight the involvement of cell adhesion, immune response, and metabolic signaling in the pathobiology of gastric cancer. Conclusion: The combination of urinary metabolomics and proteomics enables non-invasive detection of gastric cancer, revealing key biomarkers and pathways with potential clinical diagnostic significance. Further investigation is needed to confirm the diagnostic value of these findings in clinical practice.