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

This study explored a non-invasive strategy to detect gastric cancer by
integrating urinary metabolomics and proteomics, aiming to uncover
biomarkers and elucidate molecular mechanisms underlying disease
progression. Urine samples were collected from 30 advanced gastric cancer
(AGC) patients, 30 early gastric cancer (EGC) patients, and 30 healthy
controls across two centers. Using UHPLC-MS, 350 differential metabolites
were identified in AGC versus controls and 285 in EGC versus controls,
mainly related to amino acid, bile acid, and energy metabolism. Key
metabolites, including butyrate, indolelactic acid, D-ribose-5-phosphate,
and serine, were selected through Random Forest and Boruta algorithms for
diagnostic modeling. Proteomic profiling with TMT labeling revealed 376
differentially abundant proteins in AGC and 191 in EGC, enriched in immune
response, cell adhesion, and protein hydrolysis pathways. Proteins such as
TNFRSF12A, ITGB3, HSPA8, and FTL showed significant regulation, with
TNFRSF12A upregulated and HSPA8 downregulated in AGC, while ITGB3 and FTL
were upregulated in EGC. These proteins were linked to pathways including
cell adhesion molecules, ECM–receptor interaction, platelet activation,
HIF-1 signaling, glycolysis/gluconeogenesis, and antigen
processing/presentation. Integrated KEGG analysis highlighted 43 enriched
pathways in AGC and 30 in EGC, spanning amino acid metabolism, the TCA
cycle, PI3K-Akt signaling, and immune response mechanisms. Overall, the
combination of urinary metabolomics and proteomics demonstrated potential
for non-invasive detection of gastric cancer, identifying biomarkers and
pathways of diagnostic and clinical relevance, with further validation
needed for translation into clinical practice.