Metabolomics- and Proteomics-Based Disease Diagnostic Classifier Model for the Prediction and Diagnosis of Colorectal Carcinoma

Background: Colorectal carcinoma (CRC) is a leading cause of cancer-related deaths globally. Diagnostic biomarkers are essential for risk stratification and early detection, potentially enhancing patient survival. Our study aimed to explore the potential biomarkers of CRC at the protein and metabolic levels. Methods: Blood serum from CRC patients and healthy controls was analyzed using metabolomic and proteomic techniques. A conjoint analysis was conducted, and samples were split into training and validation sets (7:3 ratio) to develop and evaluate a disease diagnosis classifier model. Immunohistochemistry (IHC) analyses were conducted to validate the results. Results: We identified 631 differential metabolites and 61 differentially expressed proteins (DEPs) in CRC, involved in pathways such as arginine and proline metabolism, central carbon metabolism in cancer, and signaling pathways including TGF-β, mTOR, PI3K-Akt, and others. Key proteins (CILP2, SLC3A2, EXTL2, hydroxypyruvate isomerase (HYI), ENPEP, LRG1, CTSS, thyrotropin-releasing hormone-degrading ectoenzyme (TRHDE), SELE, and HSPA1A) showed significant expression differences between CRC patients and controls. IHC results showed that compared with the paracancerous tissues, the expression of CILP2, EXTL2, and HYI was significantly downregulated in the CRC tissues (P < 0.05). The classifier model, comprising l-arginine, Harden–Young ester, l-aspartic acid, oxoglutaric acid, l-proline, octopine, l-valine, and progesterone, achieved AUC values of 0.998 and 0.914 in training and validation data sets, respectively. Conclusions: The identified metabolites and DEPs are promising CRC biomarkers. The developed classifier model based on eight metabolites demonstrates high accuracy for CRC assessment and diagnosis.