Combining NMR and LC/MS Using Backward Variable Elimination: Metabolomics Analysis of Colorectal Cancer, Polyps, and Healthy Controls

Both
nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry
(MS) play important roles in metabolomics. The complementary features
of NMR and MS make their combination very attractive; however, currently
the vast majority of metabolomics studies use either NMR or MS separately,
and variable selection that combines NMR and MS for biomarker identification
and statistical modeling is still not well developed. In this study
focused on methodology, we developed a backward variable elimination
partial least-squares discriminant analysis algorithm embedded with
Monte Carlo cross validation (MCCV-BVE-PLSDA), to combine NMR and
targeted liquid chromatography (LC)/MS data. Using the metabolomics
analysis of serum for the detection of colorectal cancer (CRC) and
polyps as an example, we demonstrate that variable selection is vitally
important in combining NMR and MS data. The combined approach was
better than using NMR or LC/MS data alone in providing significantly
improved predictive accuracy in all the pairwise comparisons among
CRC, polyps, and healthy controls. Using this approach, we selected
a subset of metabolites responsible for the improved separation for
each pairwise comparison, and we achieved a comprehensive profile
of altered metabolite levels, including those in glycolysis, the TCA
cycle, amino acid metabolism, and other pathways that were related
to CRC and polyps. MCCV-BVE-PLSDA is straightforward, easy to implement,
and highly useful for studying the contribution of each individual
variable to multivariate statistical models. On the basis of these
results, we recommend using an appropriate variable selection step,
such as MCCV-BVE-PLSDA, when analyzing data from multiple analytical
platforms to obtain improved statistical performance and a more accurate
biological interpretation, especially for biomarker discovery. Importantly,
the approach described here is relatively universal and can be easily
expanded for combination with other analytical technologies.