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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