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Abstract #3769

Investigating the visual predicting model of metabolic biomarkers in biofluids for esophageal cancer detection using NMR-based metabolomics

Yan Lin1, Ting Ouyang1, Huanian Zhang1, Rongzhi Cai1, Peie Zheng1, Zhijie Fu1, Han Zhou1, and Renhua Wu1
1Radiology Department, Second Affiliated Hospital of Shantou University Medical College, Shantou City, China


Constructing an optimal metabolic model by combining biomarkers in biofluids may improve its non-invasive screening efficiency for esophageal cancer (EC). In this study, urine and serum specimens representing the healthy and EC individuals were examined using high-resolution 600 MHz 1H NMR technique. Furthermore, the paralleled patient-matched metabolites of EC tumor tissues and their adjacent non-cancerous tissues were investigated, which was used as references to determine biofluids metabolic biomarkers. The visual nomogram prediction model through a combination of creatine and glycine in both serum and urine was constructed using multiple regression analysis, which improves the diagnostic efficiency for EC.

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