Abstract
Metabolomics has great potential in the development of new biomarkers in cancer. In this study, metabolomics and gene expression data from breast cancer tumor samples were analyzed, using (1) probabilistic graphical models to define associations using quantitative data without other a priori information; and (2) Flux Balance Analysis and flux activities to characterize differences in metabolic pathways. A metabolite network was built through the use of probabilistic graphical models. Interestingly, the metabolites were organized into metabolic pathways in this network, thus it was possible to establish differences between breast cancer subtypes at the metabolic pathway level. Additionally, the lipid metabolism node had prognostic value. A second network associating gene expression with metabolites was built. Associations were established between the biological functions of genes and the metabolites included in each node. A third network combined flux activities from Flux Balance Analysis and metabolomics data, showing coherence between the metabolic pathways of the flux activities and the metabolites in each branch. In this study, probabilistic graphical models were valuable for the functional analysis of metabolomics data from a functional point of view, allowing new hypotheses in metabolomics and associating metabolomics data with the patient’s clinical outcome.
Author summary Metabolomics is a promising technique to describe new biomarkers in cancer. In this study we proposed computational methods to manage this type of data and associate it with gene expression data. We also employed a metabolic computational model to compare predictions from this model with metabolomics measurements. Finally, we built predictors of relapse based on the integration of those high-dimensional data in breast cancer patients.