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Quantitative Immunohistochemistry of the Cellular Microenvironment in Patient Glioblastoma Resections
Published on: July 31, 2017
Discovering gene-environment interactions in glioblastoma through a comprehensive data integration bioinformatics
Brian Kunkle1, Changwon Yoo, Deodutta Roy
1Department of Environmental and Occupational Health, Florida International University, Miami, FL 33199, United States.
Neurotoxicology
|December 25, 2012
Summary
This study introduces a bioinformatics method to identify gene-environment interactions (GEI) in glioblastoma (GBM). The approach uncovered 173 genes potentially involved in GBM development, including 65 novel candidates, advancing our understanding of this aggressive brain tumor.
Area of Science:
- Oncology
- Genetics
- Environmental Health
Background:
- Glioblastoma multiforme (GBM) is an aggressive brain tumor with limited treatment progress due to unknown molecular drivers.
- Gene-environment interactions (GEI) are increasingly recognized in GBM development, but studies are hampered by small sample sizes and high costs.
- Advances in microarray technology provide extensive genetic and epigenetic data for gliomas, necessitating data pooling.
Purpose of the Study:
- To develop a comprehensive bioinformatics method integrating genetic variations and environmental data for glioblastoma (GEG).
- To identify genes interacting with environmental chemicals and linked to GBM development.
- To uncover novel genes and pathways involved in GBM pathogenesis influenced by environmental exposures.
Main Methods:
- Integrated genetic variations (copy number and small-scale) with environmental data using a novel GEG bioinformatics approach.
- Utilized databases like Environmental Genome Project, Comparative Toxicology, and Seattle SNPs to identify environmentally responsive genes.
- Compared identified genes with GBM-related genetic alterations and analyzed using gene networking tools (RSpider, Cytoscape, DAVID).
Main Results:
- Identified 173 genes with potential involvement in GEI crucial for GBM development.
- Discovered 65 environmentally responsive genes not previously linked to GBM, showing potential for chemical response and disease action.
- Highlighted key biological functions including NGF signaling, DNA repair, cell adhesion, apoptosis, and metabolism implicated in glioma.
Conclusions:
- The GEG bioinformatics approach effectively revealed potential gene-environment interactions in GBM.
- Generated novel hypotheses for GBM development by identifying understudied genes and pathways.
- Emphasized the importance of integrating genetic and environmental data for understanding complex diseases like GBM.
