Abstract
Identifying novel biomarkers is crucial for early detection of colorectal cancer (CRC). Saliva, as a noninvasive sample, holds promise for CRC detection. Here, we used Olink proteomics and untargeted metabolomics to analyze saliva samples from CRC patients and healthy controls with the aim of identifying candidate biomarkers in CRC saliva. Univariate and multivariate analyses revealed 16 differentially expressed proteins (DEPs) and 40 differentially accumulated metabolites (DAMs). Pathway enrichment showed DEPs were mainly involved in cancer transcriptional dysregulation, Toll-like receptor signaling, and chemokine signaling. Metabolomics analysis highlighted significant changes in amino acid metabolites, particularly in pathways such as arginine biosynthesis, histidine metabolism, and cysteine and methionine metabolism. Random forest analysis and ELISA validation identified four potential biomarkers: succinate, l-methionine, GZMB, and MMP12. A combined protein-metabolite diagnostic model was developed using logistic regression, achieving an area under the curve of 0.933 (95% CI: 0.871-0.996) for the discovery cohort and 0.969 (95% CI: 0.918-1.000) for the validation cohort, effectively distinguishing CRC patients from healthy individuals. In conclusion, our study identified and validated a panel of noninvasive saliva-based biomarkers that could improve CRC screening and provide new insights into clinical CRC diagnosis.