Metabolic Phenotyping of Blood Plasma by Proton Nuclear Magnetic Resonance to Discriminate between Colorectal Cancer, Breast Cancer and Lung CancerRobby Louis1, Evelyne Louis1, Kirsten Stinkens1,2, Liesbet Mesotten1,3, Eric de Jonge4, Michiel Thomeer1,2, Philip Caenepeel1,5 and Peter Adriaensens6*
- *Corresponding Author:
- Adriaensens P
Applied and Analytical Chemistry
Institute for Materials Research
Hasselt University, 3590 Diepenbeek, Belgium
E-mail: [email protected]
Received date: September 09, 2016; Accepted date: September 27, 2016; Published date: September 30, 2016
Citation: Louis R, Louis E, Stinkens K, Mesotten L, de Jonge E, et al. (2016) Metabolic Phenotyping of Blood Plasma by Proton Nuclear Magnetic Resonance to Discriminate between Colorectal Cancer, Breast Cancer and Lung Cancer. Metabolomics (Los Angel) 6:187. doi: 10.4172/2153-0769.1000187
Copyright: © 2016 Louis R, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Background: Although many studies have demonstrated that plasma metabolic phenotyping allows discriminating between cancer patients and controls, it remains unclear whether different cancer types elicit distinguishable metabolic signatures. Therefore, the present study was designed to examine whether metabolic phenotyping of blood plasma by proton nuclear magnetic resonance spectroscopy allows discriminating between 37 colorectal cancer, 37 breast cancer and 37 lung cancer patients. Material and methods: Plasma proton nuclear magnetic resonance spectra were rationally divided into 110 integration regions defined on the basis of spiking experiments with known metabolites. The normalized integration values of these 110 regions, which represent the metabolic phenotype, were used as statistical variables to construct a classification model which enables to discriminate between the three aforementioned cancer types. Results: The resulting model allows classifying 78% of the colorectal cancer patients, 95% of the breast cancer patients and 84% of the lung cancer patients correctly. Conclusion: This preliminary feasibility study provides strong indications that the plasma metabolic phenotype has potential to become a complementary diagnostic tool to differentiate between cancer types in addition to known general cancer biomarkers.