![]() ![]() ![]() Our optimised diagnostic model with a panel of 12 lipid biomarkers together with age and gender reaches a sensitivity of 90.7%, 91.3% and 90.7% and an area under receiver-operating characteristic curve of 0.958, 0.966 and 0.818 in detecting ESCC for the training cohort, validation cohort and independent validation cohort, respectively. Integrative analyses of tissue transcriptome and serum lipidome were conducted to reveal the underlying mechanism of lipid dysregulation. A diagnostic model using a panel of selected features was developed and evaluated. We combined serum lipidomics and machine-learning algorithms to select important metabolite features for the detection of oesophageal squamous cell carcinoma (ESCC), the major subtype of EC in developing countries. MethodsĪ total of 525 serum samples were subjected to lipidomic analysis. ![]() A non-invasive and high-sensitivity diagnostic approach is necessary to improve the prognosis of EC patients. Oesophageal cancer (EC) ranks high in both morbidity and mortality. ![]()
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