Metabolomic Profiling Identified Serum Metabolite Biomarkers and Related Metabolic Pathways of Colorectal Cancer

• Published in: Disease markers Vol. 2021; pp. 1 - 9
• Main Authors: Zhang, Chengjian, Zhou, Shengnan, Chang, Huijing, Zhuang, Feng, Shi, Yang, Chang, Le, Ai, Wanchao, Du, Juan, Liu, Wei, Liu, Humin, Zhou, Xukun, Wang, Zhong, Hong, Tao
• Format: Journal Article
• Language: English
• Published: United States Hindawi 2021; John Wiley & Sons, Inc
• Subjects: Accuracy; Adult; Age; Aged; Aged, 80 and over; Alanine; Area Under Curve; Biomarkers; Biomarkers, Tumor - blood; Body mass index; Cancer; Case-Control Studies; Chromatography; Colonoscopy; Colorectal cancer; Colorectal carcinoma; Colorectal Neoplasms - blood; Colorectal Neoplasms - diagnosis; Discriminant analysis; Disease; Early Detection of Cancer - methods; Female; Gas flow; Humans; Liquid chromatography; Male; Mass spectrometry; Mass spectroscopy; Metabolic Networks and Pathways; Metabolic pathways; Metabolism; Metabolites; Metabolomics; Middle Aged; NMR; Nuclear magnetic resonance; Prospective Studies; ROC Curve; Serum levels; Sodium dodecylbenzenesulfonate; Software; Succinic acid; United States > US
• ISSN: 0278-0240; 1875-8630; 1875-8630
• DOI: 10.1155/2021/6858809

Background. The screening and early detection of colorectal cancer (CRC) still remain a challenge due to the lack of reliable and effective serum biomarkers. Thus, this study is aimed at identifying serum biomarkers of CRC that could be used to distinguish CRC from healthy controls. Methods. A prospective 1 : 2 individual matching case-control study was performed which included 50 healthy control subjects and 98 CRC patients. Untargeted metabolomic profiling was conducted with liquid chromatography tandem mass spectrometry (LC-MS/MS) to identify CRC-related metabolites and metabolic pathways. Results. In total, 178 metabolites were detected, and an orthogonal partial least-squares-discriminant analysis (OPLS-DA) model was useful to distinguish CRC patients from healthy controls. Nine metabolites showed significantly differential serum levels in CRC patients under the conditions of variable importance in projection VIP>1, p<0.05 using Student’s t-test, and fold change FC≥1.2 or ≤0.5. The above nine metabolites were 3-hydroxybutyric acid, hexadecanedioic acid, succinic acid semialdehyde, 4-dodecylbenzenesulfonic acid, prostaglandin B2, 2-pyrocatechuic acid, xanthoxylin, 12-hydroxydodecanoic acid, and formylanthranilic acid. Four potential biomarkers were identified to diagnose CRC through ROC curves: hexadecanedioic acid, 4-dodecylbenzenesulfonic acid, 2-pyrocatechuic acid, and formylanthranilic acid. All AUC values of these four serum biomarkers were above 0.70. In addition, the exploratory analysis of metabolic pathways revealed the activated states for the vitamin B metabolic pathway and the alanine, aspartate, and glutamate metabolic pathways associated with CRC. Conclusion. The 4 identified potential metabolic biomarkers could discriminate CRC patients from healthy controls, and the 2 metabolic pathways may be activated in the CRC tissues.