Lung cancer is one of the most common causes of cancer death, for which no validated tumor biomarker is sufficiently accurate to\nbe useful for diagnosis. Additionally, the metabolic alterations associated with the disease are unclear. In this study, we investigated\nthe construction, interaction, and pathways of potential lung cancer biomarkers using metabolomics pathway analysis based on the\nKyoto Encyclopedia of Genes and Genomes database and the Human Metabolome Database to identify the top altered pathways\nfor analysis and visualization. We constructed a diagnostic model using potential serum biomarkers from patients with lung\ncancer. We assessed their specificity and sensitivity according to the area under the curve of the receiver operator characteristic\n(ROC) curves, which could be used to distinguish patients with lung cancer from normal subjects. The pathway analysis indicated\nthat sphingolipid metabolism was the top altered pathway in lung cancer. ROC curve analysis indicated that glycerophospho-N-arachidonoyl\nethanolamine (GpAEA) and sphingosine were potential sensitive and specific biomarkers for lung cancer diagnosis\nand prognosis. Compared with the traditional lung cancer diagnostic biomarkers carcinoembryonic antigen and cytokeratin 19\nfragment, GpAEA and sphingosine were as good or more appropriate for detecting lung cancer. We report our identification of\npotential metabolic diagnostic and prognostic biomarkers of lung cancer and clarify the metabolic alterations in lung cancer.
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