Integration of transcriptomic data and metabolic networks in cancer samples reveals highly significant prognostic power

Effective stratification of cancer patients on the basis of their molecular make-up is a key open challenge. Given the altered and heterogenous nature of cancer metabolism, we here propose to use the overall expression of central carbon metabolism as biomarker to characterize groups of patients with important characteristics, such as response to ad-hoc therapeutic strategies and survival expectancy.

Researchers from the University of Milan-Bicocca introduce the data integration framework named Metabolic Reaction Enrichment Analysis (MaREA), which strives to characterize the metabolic deregulations that distinguish cancer phenotypes, by projecting RNA-seq data onto metabolic networks, without requiring metabolic measurements. MaREA computes a score for each network reaction, based on the expression of the set of genes encoding for the associated enzyme(s). The scores are first used as features for cluster analysis and then to rank and visualize in an organized fashion the metabolic deregulations that distinguish cancer sub-types. The researchers applied they’re method to recent lung and breast cancer RNA-seq datasets from The Cancer Genome Atlas and they were able to identify subgroups of patients with significant differences in survival expectancy. They show how the prognostic power of MaREA improves when an extracted and further curated core model focusing on central carbon metabolism is used rather than the genome-wide reference network. The visualization of the metabolic differences between the groups with best and worst prognosis allowed to identify and analyze key metabolic properties related to cancer aggressiveness. Some of these properties are shared across different cancer (sub)types, e.g., the up-regulation of nucleic acid and amino acid synthesis, whereas some other appear to be tumor-specific, such as the up- or down-regulation of the phosphoenolpyruvate carboxykinase reaction, which display different patterns in distinct tumor (sub)types. These results might be soon employed to deliver highly automated diagnostic and prognostic strategies for cancer patients.


Graudenzi A, Maspero D, Filippo MD, Gnugnolo M, Isella C, Mauri G, Medico E, Antoniotti M, Damani C. (2018) Integration of transcriptomic data and metabolic networks in cancer samples reveals highly significant prognostic power. J Biomed Inform [Epub ahead of print]. [abstract]

Leave a Reply

Your email address will not be published. Required fields are marked *


Time limit is exhausted. Please reload CAPTCHA.