Metabolic phenotyping by 1H-NMR spectroscopy to detect lung cancer via a simple blood sample

Abstract

Introduction: Lung cancer is the leading cause of cancer death worldwide. There is an urgent need of effective methods to detect lung cancer. Accumulating evidence shows that the metabolism of cancer cells differs from that of normal cells. Disturbances in biochemical pathways which occur during the development of cancer provoke changes in the metabolic phenotype. Objective: To determine the metabolic phenotype of lung cancer by 1H-NMR spectroscopy. Methods: Fasting venous blood samples of 78 patients with confirmed lung cancer (before any treatment) and 78 controls are collected and analyzed by 1H-NMR spectroscopy. The integration values of 110 spectral regions, representing the metabolite concentrations, are analyzed by a Mann-Whitney test to identify those which differ significantly between lung cancer patients and controls. Next, orthogonal partial least squares discriminant analyses (OPLS-DA) are performed to investigate whether the metabolic composition of blood plasma discriminates between lung cancer patients and controls. Results: The integration values of 28 out of 110 spectral regions are significantly different for lung cancer. These regions include signals of several amino acids, citrate, lactate and lipids. These 28 significantly different integration values are used to build a statistical classifier by means of OPLS-DA multivariate statistics. Via this classifier model, lung cancer can be detected with a sensitivity of 86% and a specificity of 95%. Conclusion: Metabolic phenotyping of blood plasma by 1H-NMR spectroscopy detects lung cancer with a high degree of sensitivity and specificity. At present, lung cancer patients and controls are recruited to validate these promising results in a larger population study.