MaREA: Metabolic feature extraction, enrichment and visualization of RNAseq data

Abstract

The characterization of the metabolic deregulations that distinguish cancer phenotypes, and which might be effectively targeted by ad-hoc strategies, is a key open challenge. To this end, we here introduce MaREA (Metabolic Reaction Enrichment Analysis), a computational pipeline that processes cross-sectional RNAseq data to identify the metabolic reactions that are significantly up-/ down-regulated in different sample subgroups. MaREA relies on the definition of a Reaction Activity Score, computed as a function of the expression level of genes encoding for reaction enzymes, which can also be used as an effective metrics to cluster samples into distinct metabolic subgroups. MaREA finally allows to visualize the results in a graphical form directly on metabolic maps. We apply MaREA to distinct cancer datasets and we show that it can produce useful information and new experimental hypotheses on metabolic deregulation of cancer cells, also allowing to stratify patients in metabolic clusters with significantly different survival expectancy.