| Literature DB >> 21317451 |
Amparo Wolf1, Sameer Agnihotri, Abhijit Guha.
Abstract
A key aberrant biological difference between tumor cells and normal differentiated cells is altered metabolism, whereby cancer cells acquire a number of stable genetic and epigenetic alterations to retain proliferation, survive under unfavorable microenvironments and invade into surrounding tissues. A classic biochemical adaptation is the metabolic shift to aerobic glycolysis rather than mitochondrial oxidative phosphorylation, regardless of oxygen availability, a phenomenon termed the "Warburg Effect". Aerobic glycolysis, characterized by high glucose uptake, low oxygen consumption and elevated production of lactate, is associated with a survival advantage as well as the generation of substrates such as fatty acids, amino acids and nucleotides necessary in rapidly proliferating cells. This review discusses the role of key metabolic enzymes and their association with aerobic glycolysis in Glioblastoma Multiforme (GBM), an aggressive, highly glycolytic and deadly brain tumor. Targeting key metabolic enzymes involved in modulating the "Warburg Effect" may provide a novel therapeutic approach either singularly or in combination with existing therapies in GBMs.Entities:
Keywords: cancer; drug discovery; oncotarget; stem cells; wnt
Mesh:
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Year: 2010 PMID: 21317451 PMCID: PMC3035636 DOI: 10.18632/oncotarget.190
Source DB: PubMed Journal: Oncotarget ISSN: 1949-2553
Figure 1Schematic of Metabolic Remodeling in GBM
Enzymes of glycolysis, the pentose phosphate pathway, fatty acid and glutamine metabolism and their regulation by known oncogenes and tumor suppressor genes in proliferating cells. Growth factor/PI3K/AKT signaling stimulates glucose uptake and flux through the early part of glycolysis. Tyrosine kinase signaling negatively regulates flux through at PKM2, making glycolytic intermediates available for macromolecular synthesis. Myc has been found to promote glutamine metabolism and inhibit oxidative metabolism by activating PDK. p53 decreases metabolic flux through glycolysis in response to cell stress.
Differentially expressed metabolic genes in GBMs at the RNA level
GBM expression array data was obtained from TCGA (http://tcgadata.nci.nih.gov/tcga/dataAccessMatrix.htm?mode=ApplyFilter&showMatrix=false). In Brief, array files from TCGA batch 8 (HG-U133A Affymetrix Array platform) which contains 25 GBM samples (group1) and 10 Normal brain samples (group2) were imported into Affymetrix Gene Expression Console. Normal brain and GBMs were compared against each other for differential gene expression using significance analysis of microarrays (SAM) (False Discovery Rate of 10% and a minimum fold change of 2). Genes of various metabolic processes from the significantly identified gene list were extracted using Ingenuity Pathway Analysis. This analysis does not discern differentially expressed genes at the protein level, protein modifications or mutations. See Supplemental Table for TCGA sample ID's used in this review.
| Up-regulated | Down-regulated | ||
|---|---|---|---|
| Primary Metabolic Process | Gene | Primary Metabolic Process | Gene |
| Glucose Metabolism | FABP5 | Glucose Metabolism | ALDH5A1 |
| Fatty Acid Metabolism | ACOT9 | Fatty Acid Metabolism | AACS |
| Glutamine Metabolism | DDAH2 | Glutamine Metabolism | ALDH5A1 |
| Nucleotide Metabolism | ADA | Nucleotide Metabolism | CSGALNACT1 |