Global metabolomics reveals potential urinary biomarkers of esophageal squamous cell carcinoma for diagnosis and staging

• Published in: Scientific reports Vol. 6; no. 1; p. 35010
• Main Authors: Xu, Jing, Chen, Yanhua, Zhang, Ruiping, He, Jiuming, Song, Yongmei, Wang, Jingbo, Wang, Huiqing, Wang, Luhua, Zhan, Qimin, Abliz, Zeper
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 11.10.2016; Nature Publishing Group
• Subjects: 639/638/11/296; 692/53/2421; Aged; Amino Acids - urine; Biomarkers; Biomarkers, Tumor - urine; Carcinoma, Squamous Cell - diagnosis; Carcinoma, Squamous Cell - metabolism; Carcinoma, Squamous Cell - urine; Chromatography, High Pressure Liquid; Data processing; Esophageal cancer; Esophageal Neoplasms - diagnosis; Esophageal Neoplasms - metabolism; Esophageal Neoplasms - urine; Esophageal Squamous Cell Carcinoma; Esophagus; Fatty Acids - urine; Humanities and Social Sciences; Humans; Liquid chromatography; Male; Mass spectroscopy; Metabolic pathways; Metabolomics; Metabolomics - methods; Middle Aged; multidisciplinary; Multivariate Analysis; Neoplasm Staging; Oxidation; Purines; Pyrimidines; Science; Squamous cell carcinoma; Tandem Mass Spectrometry; Urine
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/srep35010

We performed a metabolomics study using liquid chromatography-mass spectrometry (LC-MS) combined with multivariate data analysis (MVDA) to discriminate global urine profiles in urine samples from esophageal squamous cell carcinoma (ESCC) patients and healthy controls (NC). Our work evaluated the feasibility of employing urine metabolomics for the diagnosis and staging of ESCC. The satisfactory classification between the healthy controls and ESCC patients was obtained using the MVDA model, and obvious classification of early-stage and advanced-stage patients was also observed. The results suggest that the combination of LC-MS analysis and MVDA may have potential applications for ESCC diagnosis and staging. We then conducted LC-MS/MS experiments to identify the potential biomarkers with large contributions to the discrimination. A total of 83 potential diagnostic biomarkers for ESCC were screened out, and 19 potential biomarkers were identified; the variations between the differences in staging using these potential biomarkers were further analyzed. These biomarkers may not be unique to ESCCs, but instead result from any malignant disease. To further elucidate the pathophysiology of ESCC, we studied related metabolic pathways and found that ESCC is associated with perturbations of fatty acid β-oxidation and the metabolism of amino acids, purines, and pyrimidines.