Development of a Large-Scale Serum Metabolome Relative Quantitative Method and Application for Metabolic Traits of Colon Cancer

Abstract

Colon cancer (CC) is one of the malignant tumors with high incidence and mortality rates worldwide, necessitating innovative diagnostic tools to improve early detection and management. In this study, we developed a large-scale metabolome relative quantitative method workflow using ultrahigh-performance liquid chromatography coupled with Q-TRAP mass spectrometry to identify novel biomarkers in the serum of CC patients. This method enables the detection of 776 metabolic features, spanning 16 chemical classes and 63 metabolic pathways. All detected features were evaluated by multiple identification criteria and assigned confidence scores to ensure annotation reliability. Through validation and application of this method to clinical samples, we compared the serum metabolic profiles of CC patients with those of healthy controls and screened 72 significantly altered metabolites. Pathway enrichment analysis revealed perturbations in tryptophan metabolism, arginine biosynthesis, and the TCA cycle. Notably, three differential metabolites (indole, tryptophan, and xanthurenic acid) were identified that could potentially serve as diagnostic biomarkers with area under the curve values exceeding 0.9. Our large-scale metabolome relative quantitative method demonstrates applicability in identifying potential metabolite biomarkers for CC and provides a promising tool for both basic and clinical metabolomics research.