Serum metabolomics to identify molecular subtypes and predict XELOX efficacy in colorectal cancer.

Colorectal cancer (CRC) is one of the most common cancers; however, accurately predicting prognosis based on existing molecular subtypes remains challenging. The XELOX regimen, which combines oxaliplatin and capecitabine, is the cornerstone of chemotherapy for CRC treatment. However, there is a notable lack of reliable predictive models for determining the sensitivity of this treatment. This study aimed to establish a novel classification system for CRC and develop a predictive model for XELOX chemotherapeutic sensitivity using serum metabolomics. We recruited 89 patients with CRC and 89 age- and sex-matched healthy controls for untargeted metabolomic studies to identify tumor-specific serum metabolites. The patients were grouped into distinct metabolic subtypes using unsupervised clustering. A serum metabolite combination predictive of the efficacy of XELOX was established using Cox regression analysis in 34 patients with stage III CRC. Using unsupervised clustering based on the serum metabolites, three distinct clusters were identified. Notably, Cluster 3, which was characterized by uniform lipid and amino acid levels, demonstrated the best prognosis. Our analysis revealed that D-glucose 6-phosphate, presqualene diphosphate, and leukotriene B4 levels were negatively correlated with XELOX sensitivity, whereas 15-HETE and N-acetyl-l-methionine levels were positively correlated. Based on these findings, we constructed a predictive model validated in an independent cohort of 34 patients with stage III CRC. In summary, this study identified a novel classification of CRC based on serum metabolites and developed a potential prognostic model for XELOX chemotherapeutic efficacy, which may have direct effects on the treatment and prognosis of CRC.