Kwadwo Owusu-Sarfo, Vincent M. Asiago, Lingli Deng, Haiwei Gu, Siwei Wei, Narasimhamurthy Shanaiah, G. A. Nagana Gowda, Bowei Xi, Elena G. Chiorean and Daniel Raftery Pages 204 - 212 ( 9 )
Metabolite profiles of serum from pancreatic cancer (PC) patients (n=51) and non-disease controls (n=47) were measured using 1H nuclear magnetic resonance (NMR) spectroscopy with a focus on the metabolic changes associated with PC pathology and the development and external validation of the statistical models developed using the metabolite data. Univariate statistical analysis indicated 42 metabolite features showing significant differences between PC and controls (p<0.05). Based on multivariate regression analysis of the data from 38 PC patients and 32 controls, twelve distinguishing metabolites (alanine, choline, citrate, creatinine, glucose, glutamine, glutamic acid, 3- hydroxybutyrate, lactate, lipids, methionine and valine) were determined based on their ranked importance in a partial least square discriminant analysis (PLS-DA) model. A cross-validated regression PLS-DA model built using these metabolites differentiated the cancer and control groups with high accuracy and an area under the receiver operating characteristic curve (AUROC) of 0.95. Notably, external validation of this model using the NMR data from a second, distinct set of samples (13 PC patients and 15 controls) collected approximately 1 year later showed an AUROC of 0.86, which represents very good performance compared to the current approaches for identifying pancreatic cancer patients. Metabolic changes in pancreatic cancer patients compared to healthy controls as shown in this study demonstrate the potential for the development of regression models based on blood metabolites to identify patients with pancreatic cancer.
1H NMR, early detection, MCCV analysis, metabolite profiling, metabolomics, pancreatic cancer.
Northwest Metabolomics Center, Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA 98109, USA.