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Identification of hypoxia-induced genes in a malignant glioma cell line (U-251) by cDNA microarray analysis.

Ragel BT, Couldwell WT, Gillespie DL, Jensen RL

Department of Neurosurgery, University of Utah, Salt Lake City, UT 84132, USA.

Overcoming the metabolic restrictions of hypoxia may allow the progression of lower-grade tumors to glioblastoma multiforme. Our findings of up-regulation of HIF-1alpha and its downstream targets VEGF, GLUT-1, and CAIX in higher-grade gliomas support this hypothesis. We compared the gene expression profiles of the U-251 malignant glioma cell line under normoxic and hypoxic conditions to discover future research targets. U-251 cells were grown to 75% confluence and exposed to either normoxic or hypoxic conditions for 24 h. RNA was extracted, amplified, and hybridized to a cDNA microarray chip containing ~8,800 universal cellular genes. A threefold increase in mRNA expression was used as a threshold value for differential expression. Identified genes were divided into cell cycle control, stress response, and "newly connected" genes. Hybridization identified 11 hypoxia-induced genes: 1 involved with cell cycle control (CCNG2), 6 in stress response (IGFBP3, SLC2A3, GSTT2, FOS, DDIT3, AKR1C3), and 2 newly connected genes (Depp, AKAP4). One stress-related gene (AKR1C3) encodes for an enzyme that synthesizes progesterone. Of newly connected genes, the gene decidual protein induced by progesterone (Depp) showed the highest expression (4.2-fold increase). Possible future targeting for "hypoxic" glioma cells includes the targets for the AP-1 transcription factor complex (FOS), as well as blockade of the enzyme AKR1C3 with nonsteroidal anti-inflammatory drugs. Possible functions of the highly expressed gene Depp include tumor vascularization. Future studies will focus on the hypothesis that Depp is up-regulated in an autocrine fashion by the AKR1C3 enzyme in U-251 glioma cells under hypoxic conditions.

Published 11 June 2007 in Neurosurg Rev, 30(3): 181-7; discussion 187.
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Microarrays Books

Analysis of Microarray Gene Expression Data (Trends in Logic)

Analysis of Microarray Gene Expression Data (Trends in Logic)