Distinct Urinary Metabolic Biomarkers of Human Colorectal Cancer

• Published in: Disease markers Vol. 2022; pp. 1 - 18
• Main Authors: Zhu, Chang, Huang, Fengjie, Li, Yang, Zhu, Chaowei, Zhou, Kejun, Xie, Haihui, Xia, Ligang, Xie, Guoxiang
• Format: Journal Article
• Language: English
• Published: United States Hindawi 2022; John Wiley & Sons, Inc
• Subjects: Adenosine monophosphate; Alanine; Amino acids; AMP; Biomarkers; Biomarkers - metabolism; Biomarkers, Tumor; Cancer; Carbohydrate metabolism; Carbohydrates; Chenodeoxycholic acid; Chromatography; Colorectal cancer; Colorectal carcinoma; Colorectal Neoplasms - diagnosis; Colorectal Neoplasms - metabolism; Diagnosis; Discriminant analysis; Fatty acids; Glycine; Glycolic acid; Glycolysis; Histidine; Humans; Investigations; Lipid metabolism; Lipids; Liquid chromatography; Mass spectrometry; Mass Spectrometry - methods; Mass spectroscopy; Medical prognosis; Metabolism; Metabolites; Metabolomics; Metabolomics - methods; Metastasis; p-Hydroxybenzoic acid; Quadrupoles; Scientific imaging; Software; Statistical analysis; Support vector machines; Threonine; Tricarboxylic acid cycle; Urine; Vanillic acid; St Louis Missouri; United States > US; China
• ISSN: 0278-0240; 1875-8630; 1875-8630
• DOI: 10.1155/2022/1758113

Colorectal cancer (CRC) is one of the most commonly diagnosed cancers with high mortality rate due to its poor diagnosis in the early stage. Here, we report a urinary metabolomic study on a cohort of CRC patients (n =67) and healthy controls (n =21) using ultraperformance liquid chromatography triple quadrupole mass spectrometry. Pathway analysis showed that a series of pathways that belong to amino acid metabolism, carbohydrate metabolism, and lipid metabolism were dysregulated, for instance the glycine, serine and threonine metabolism, alanine, aspartate and glutamate metabolism, glyoxylate and dicarboxylate metabolism, glycolysis, and TCA cycle. A total of 48 differential metabolites were identified in CRC compared to controls. A panel of 12 biomarkers composed of chenodeoxycholic acid, vanillic acid, adenosine monophosphate, glycolic acid, histidine, azelaic acid, hydroxypropionic acid, glycine, 3,4-dihydroxymandelic acid, 4-hydroxybenzoic acid, oxoglutaric acid, and homocitrulline were identified by Random Forest (RF), Support Vector Machine (SVM), and Boruta analysis classification model and validated by Gradient Boosting (GB), Logistic Regression (LR), and Random Forest diagnostic model, which were able to discriminate CRC subjects from healthy controls. These urinary metabolic biomarkers provided a novel and promising molecular approach for the early diagnosis of CRC.