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

Publication date: Available online 20 September 2018Source: Journal of Biomedical InformaticsAuthor(s): Alex Graudenzi, Davide Maspero, Marzia Di Filippo, Marco Gnugnolo, Claudio Isella, Giancarlo Mauri, Enzo Medico, Marco Antoniotti, Chiara DamaniAbstractEffective 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.To this end, we here 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.We applied our method to recent lung and breast cancer RNA-seq datasets from The Cancer Genome Atlas and we were able to identify subgroups of patients with significant differences in survival expectancy. We show how the prognost...
Source: Journal of Biomedical Informatics - Category: Information Technology Source Type: research