In search for symmetries in the metabolism of cancer

Abstract

Even though aerobic glycolysis, or the Warburg effect, is arguably the most common trait of metabolic reprogramming in cancer, it is unobserved in certain tumor types. Systems biology advocates a global view on metabolism to dissect which traits are consistently reprogrammed in cancer, and hence likely to constitute an obligate step for the evolution of cancer cells. We refer to such traits as symmetric. Here, we review early systems biology studies that attempted to reveal symmetric traits in the metabolic reprogramming of cancer, discuss the symmetry of reprogramming of nucleotide metabolism, and outline the current limitations that, if unlocked, could elucidate whether symmetries in cancer metabolism may be claimed.

Systematic Analysis Reveals that Cancer Mutations Converge on Deregulated Metabolism of Arachidonate and Xenobiotics

“…We and others previously found that distinct cancer types featured few common gene-expression changes in metabolism from their respective non-cancerous tissues, which were primarily ascribed to altered nucleotide biosynthesis (Gatto et al, 2014;Hu et al, 2013;Nilsson et al, 2014). However, these studies could not distinguish whether the observed changes in gene expression are attributable to a common adaptation process during cancer progression or are rather the consequence of a specific mutation event (Gatto and Nielsen, 2016). To determine this, we selected among the genes found to be associated with nine mutated genes those that overlap with the 3,765 genes that participate in the human metabolic network (Mardinoglu et al, 2014).…”

Role of Protein Kinase CK2 in Aberrant Lipid Metabolism in Cancer

“…It includes not only alterations in lipid metabolism but also elevation in glycolysis, glutaminolytic flux, amino acid metabolism, mitochondria biogenesis and pentose phosphate pathway. Although it is a phenomenon resulting from oncogenic mutations, the metabolic switch observed in the majority of cancer types seems to be context-dependent, namely, largely attributable to the microenvironment, rather than specific to the particular type of malignancy [ 85 , 86 ]. An interest notion that emerged in recent years is that most cancer samples retain a substantial similarity with the tissue of origin regarding the expression of metabolic genes.…”

Applications of Genome-Scale Metabolic Models in Biotechnology and Systems Medicine

“…For instance, cancer specific GEMs together with EA and SLA analysis were recently used for identification of oncogenes/metabolites and biomarkers for diagnosing specific cancer (Agren et al, 2014 ; Jerby-Arnon et al, 2014 ; Gatto et al, 2015 ; Gatto and Nielsen, 2015 ). Since this procedure mainly uses the true-negative part of EA and SLA, the analysis could be highly reliable.…”