ABSTRACT
Age is a powerful predictor of survival in glioblastoma multiforme (GBM) yet the biological basis for the difference in clinical outcome is mostly unknown. Discovering genes and pathways that would explain age-specific survival difference could generate opportunities for novel therapeutics for GBM. Here we have integrated gene expression, exon expression, microRNA expression, copy number alteration, SNP, whole exome sequence, and DNA methylation data sets of a cohort of GBM patients in The Cancer Genome Atlas (TCGA) project to discover age-specific signatures at the transcriptional, genetic, and epigenetic levels and validated our findings on the REMBRANDT data set. We found major age-specific signatures at all levels including age-specific hypermethylation in polycomb group protein target genes and the upregulation of angiogenesis-related genes in older GBMs. These age-specific differences in GBM, which are independent of molecular subtypes, may in part explain the preferential effects of anti-angiogenic agents in older GBM and pave the way to a better understanding of the unique biology and clinical behavior of older versus younger GBMs.
Index Terms
- Age-Specific Signatures of Glioblastoma at the Genomic, Genetic, and Epigenetic levels
Recommendations
Investigating Gene and MicroRNA Expression in Glioblastoma
IJCBS '09: Proceedings of the 2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent ComputingGlioblastoma is the most common primary brain tumor in adults. Here we present an integrated analysis of microRNA expression and gene expression in 237 tumor tissues and 10 normal tissues. We indentified 1,236 genes, and 131 pathways significantly ...
Drug repurposing for glioblastoma based on molecular subtypes
Display Omitted Glioblastoma (GBM) is the most aggressive brain tumor with poor prognosis.Effective new targeted therapies are needed for GBM.We developed a drug repositioning approach for GBM and its molecular subtypes.Our approach combines human ...
Computational selection of distinct class- and subclass-specific gene expression signatures
In this investigation we used statistical methods to select genes with expression profiles that partition classes and subclasses of biological samples. Gene expression data corresponding to liver samples from rats treated for 24 h with an enzyme inducer ...
Comments