J Cancer 2020; 11(23):6925-6938. doi:10.7150/jca.47631 This issue

Research Paper

Profiling of polar urine metabolite extracts from Chinese colorectal cancer patients to screen for potential diagnostic and adverse-effect biomarkers

Yi Deng1#, Houshan Yao2#, Wei Chen1, Hua Wei1, Xinxing Li2, Feng Zhang1, Shouhong Gao1, Huan Man1,3, Jing Chen1,3, Xia Tao1, Mingming Li1✉, Wansheng Chen1,4✉

1. Department of Pharmacy, Changzheng Hospital, Secondary Military Medical University, Shanghai, China, 200003.
2. Department of Surgery, Changzheng Hospital, Secondary Military Medical University, Shanghai, China, 200003.
3. College of Chemical and Biological Engineering, Yichun University, Jiangxi Province, China, 336000.
4. Research and Development Center of Chinese Medicine Resources and Biotechnology, Shanghai University of Traditional Chinese Medicine, Shanghai, China, 201203.
#These authors contributed equally to this work.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions.
Citation:
Deng Y, Yao H, Chen W, Wei H, Li X, Zhang F, Gao S, Man H, Chen J, Tao X, Li M, Chen W. Profiling of polar urine metabolite extracts from Chinese colorectal cancer patients to screen for potential diagnostic and adverse-effect biomarkers. J Cancer 2020; 11(23):6925-6938. doi:10.7150/jca.47631. Available from https://www.jcancer.org/v11p6925.htm

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Abstract

Background: Metabolomics has demonstrated its potential in the early diagnosis, drug safety evaluation and personalized toxicology research of various cancers.

Objectives: We aim to screen for potential diagnostic and capecitabine-related adverse effect (CRAE) biomarkers from urinary endogenous metabolites in Chinese colorectal cancer (CRC) patients.

Methods: The metabolic profiles of 139 CRC patients and 50 non-neoplastic controls were analyzed using ultra-high-performance liquid chromatography combined with quadrupole time-of-flight mass spectrometry.

Results: There were 41 metabolites identified between the CRC patients and the non-neoplastic controls, and 19 metabolites were identified between CRC patients with and without CRAE. Based on these identified metabolites, bioinformatic analysis and prediction model construction were completed. Most of these differential metabolites have important roles in cell proliferation and differentiation and the immune system. Based on binary logistic regression, a CRC prediction model, composed of 3-methylhistidine, N-heptanoylglycine, N1,N12-diacetylspermine and hippurate, was established, with an area under curve (AUC) of 0.980 (95% CI: 0.953-1.000; sensitivity: 94.3%; specificity: 92.0%) in the training set, and an AUC of 0.968 (95% CI: 0.933-1.000; sensitivity: 89.9%; specificity: 92.0%) in the testing set. In addition, methionine and 4-pyridoxic acid can be combined to predict hand foot syndrome, with an AUC of 0.884; ubiquinone-1 and 4-pyridoxic acid can be combined to predict anemia, with an AUC of 0.889; and 5-acetamidovalerate and 3,4-methylenesebacic acid can be combined to predict neutropenia, with an AUC of 0.882.

Conclusion: The profiling of urine polar metabolites has great potential in the early detection of CRC and the prediction of CRAE.

Keywords: colorectal cancer, UHPLC-Q-TOF-MS, untargeted metabolomics, capecitabine, adverse effect