The metabolomic landscape plays a critical role in glioma oncogenesis

Cancer Sci. 2022 May;113(5):1555-1563. doi: 10.1111/cas.15325. Epub 2022 Mar 23.

Abstract

Cancer cells depend on metabolic reprogramming for survival, undergoing profound shifts in nutrient sensing, nutrient uptake and flux through anabolic pathways, in order to drive nucleotide, lipid, and protein synthesis and provide key intermediates needed for those pathways. Although metabolic enzymes themselves can be mutated, including to generate oncometabolites, this is a relatively rare event in cancer. Usually, gene amplification, overexpression, and/or downstream signal transduction upregulate rate-limiting metabolic enzymes and limit feedback loops, to drive persistent tumor growth. Recent molecular-genetic advances have revealed discrete links between oncogenotypes and the resultant metabolic phenotypes. However, more comprehensive approaches are needed to unravel the dynamic spatio-temporal regulatory map of enzymes and metabolites that enable cancer cells to adapt to their microenvironment to maximize tumor growth. Proteomic and metabolomic analyses are powerful tools for analyzing a repertoire of metabolic enzymes as well as intermediary metabolites, and in conjunction with other omics approaches could provide critical information in this regard. Here, we provide an overview of cancer metabolism, especially from an omics perspective and with a particular focus on the genomically well characterized malignant brain tumor, glioblastoma. We further discuss how metabolomics could be leveraged to improve the management of patients, by linking cancer cell genotype, epigenotype, and phenotype through metabolic reprogramming.

Keywords: epigenetics; glioblastoma; mTOR complex; metabolome; omics.

Publication types

  • Review

MeSH terms

  • Brain Neoplasms* / genetics
  • Brain Neoplasms* / metabolism
  • Carcinogenesis / genetics
  • Cell Transformation, Neoplastic / genetics
  • Glioma* / genetics
  • Glioma* / metabolism
  • Humans
  • Metabolomics
  • Proteomics
  • Tumor Microenvironment