Non-targeted and targeted metabolomics approaches to diagnosing lung cancer and predicting patient prognosis

// Xiaoli Zhang 1 , Xinyue Zhu 2 , Caihong Wang 3 , Haixia Zhang 2 , Zhiming Cai 1 1 The Affiliated Luohu Hospital of Shenzhen University, Shenzhen 518001, China 2 College of Chemistry and Chemical Engineering, Lanzhou University, Lanzhou 730000, China 3 Shijiazhuang Huaguang Traditional Chinese Medicine Tumor Hospital, Shijiazhuang 050000, China Correspondence to: Zhiming Cai, email: caizhiming2000@163.com Xiaoli Zhang, email: abby19820909@163.com Keywords: lung cancer, non-targeted metabolomics, targeted metabolomics, proton nuclear magnetic resonance spectroscopy, rapid resolution liquid chromatography Received: August 02, 2015      Accepted: August 13, 2016      Published: August 23, 2016 ABSTRACT Lung cancer is the most common cause of cancer death in China. We characterized metabolic alterations in lung cancer using two analytical platforms: a non-targeted metabolic profiling strategy based on proton nuclear magnetic resonance ( 1 H-NMR) spectroscopy and a targeted metabolic profiling strategy based on rapid resolution liquid chromatography (RRLC). Changes in serum metabolite levels during oncogenesis were evaluated in 25 stage I lung cancer patients and matched healthy controls. We identified 25 metabolites that were differentially regulated between the lung cancer patients and matched controls. Of those, 16 were detected using the non-targeted approach and 9 were identified using the targeted approach. Both groups of metabolites could differentiate between lung cancer patients and healthy controls with 100% sensitivity and specificity. The principal metabolic alternations in lung cancer included changes in glycolysis, lipid metabolism, choline phospholipid metabolism, one-carbon metabolism, and amino acid metabolism. The targeted metabolomics approach was more sensitive, accurate, and specific than the non-targeted metabolomics approach. However, our data suggest that both metabolomics strategies could be used to detect early-stage lung cancer and predict patient prognosis.