NMR-based metabolomic techniques identify potential urinary biomarkers for early colorectal cancer detection

// Zhening Wang 1, * , Yan Lin 1, * , Jiahao Liang 1 , Yao Huang 1 , Changchun Ma 2 , Xingmu Liu 3 and Jurong Yang 4 1 Radiology Department, Second Affiliated Hospital, Shantou University Medical College, Shantou 515041, Guangdong Province, China 2 Radiation Oncology, Affiliated Tumor Hospital, Shantou University Medical College, Shantou 515041, Guangdong Province, China 3 Surgery Department, Second Affiliated Hospital, Shantou University Medical College, Shantou 515041, Guangdong Province, China 4 Shantou University Central Laboratory and NMR Unit, Shantou 515041, Guangdong Province, China * These authors have contributed equally to this work Correspondence to: Yan Lin, email: 994809889@qq.com Keywords: colorectal cancer; metabolomics; 1 H NMR spectroscopy; urine; biomarker Received: April 04, 2017     Accepted: August 29, 2017     Published: November 11, 2017 ABSTRACT Better early detection methods are needed to improve the outcomes of patients with colorectal cancer (CRC). Proton nuclear magnetic resonance spectroscopy ( 1 H-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 1 H-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.