NMR-based metabolomic techniques identify potential urinary biomarkers for early colorectal cancer detection
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Zhening Wang1,*, Yan Lin1,*, Jiahao Liang1, Yao Huang1, Changchun Ma2, Xingmu Liu3 and Jurong Yang4
1Radiology Department, Second Affiliated Hospital, Shantou University Medical College, Shantou 515041, Guangdong Province, China
2Radiation Oncology, Affiliated Tumor Hospital, Shantou University Medical College, Shantou 515041, Guangdong Province, China
3Surgery Department, Second Affiliated Hospital, Shantou University Medical College, Shantou 515041, Guangdong Province, China
4Shantou University Central Laboratory and NMR Unit, Shantou 515041, Guangdong Province, China
*These authors have contributed equally to this work
Yan Lin, email: [email protected]
Keywords: colorectal cancer; metabolomics;1H NMR spectroscopy; urine; biomarker
Received: April 04, 2017 Accepted: August 29, 2017 Published: November 11, 2017
Better early detection methods are needed to improve the outcomes of patients with colorectal cancer (CRC). Proton nuclear magnetic resonance spectroscopy (1H-NMR), a potential non-invasive early tumor detection method, was used to profile urine metabolites from 55 CRC patients and 40 healthy controls (HCs). Pattern recognition through orthogonal partial least squares-discriminant analysis (OPLS-DA) was applied to 1H-NMR processed data. Model specificity was confirmed by comparison with esophageal cancers (EC, n=18). Unique metabolomic profiles distinguished all CRC stages from HC urine samples. A total of 16 potential biomarker metabolites were identified in stage I/II CRC, indicating amino acid metabolism, glycolysis, tricarboxylic acid (TCA) cycle, urea cycle, choline metabolism, and gut microflora metabolism pathway disruptions. Metabolite profiles from early stage CRC and EC patients were also clearly distinguishable, suggesting that upper and lower gastrointestinal cancers have different metabolomic profiles. Our study assessed important metabolomic variations in CRC patient urine samples, provided information complementary to that collected from other biofluid-based metabolomics analyses, and elucidated potential underlying metabolic mechanisms driving CRC. Our results support the utility of NMR-based urinary metabolomics fingerprinting in early diagnosis of CRC.
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