Abstract
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.
Highlights
Several metabolomics studies of Esophageal cancer (EC) have been performed using various analytical platforms[23,24,25,26]
We evaluated the possibility of using urine metabolomics for the classification of esophageal squamous cell carcinoma (ESCC) and used an independent test set to examine the predictive ability of the analytical platform
This results demonstrated no drift in retention time and chromatographic shape during the whole run-sequence. indicating the LC-MS results were statistically acceptable for analysis[28,29]
Summary
Several metabolomics studies of EC have been performed using various analytical platforms[23,24,25,26]. We performed global and targeted metabolomics study of ESCC plasma to discover potential diagnostic and therapeutic biomarkers[27]. We performed LC-MS combined with multivariate data analysis (MVDA) to investigate the global urinary profiles of ESCC patients and normal controls. We evaluated the possibility of using urine metabolomics for the classification of ESCC and used an independent test set to examine the predictive ability of the analytical platform. Potential biomarkers were discovered, identified, and evaluated by receiver operating characteristic analysis (ROC). The overall goals of this study were to (1) develop a LC-MS-based urine metabolomics method for ESCC diagnosis and staging, (2) discover potential biomarkers, and (3) illustrate the pathological changes associated with ESCC.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
