Abstract
Glioblastoma Multiforme (GBM) is a tumor with high mortality and no known cure. The dramatic molecular and clinical heterogeneity seen in this tumor has led to attempts to define genetically similar subgroups of GBM with the hope of developing tumor specific therapies targeted to the unique biology within each of these subgroups. Recently, a subset of relatively favorable prognosis GBMs has been identified. These glioma CpG island methylator phenotype, or G-CIMP tumors, have distinct genomic copy number aberrations, DNA methylation patterns, and (mRNA) expression profiles compared to other GBMs. While the standard method for identifying G-CIMP tumors is based on genome-wide DNA methylation data, such data is often not available compared to the more widely available gene expression data. In this study, we have developed and evaluated a method to predict the G-CIMP status of GBM samples based solely on gene expression data.
Highlights
Glioblastoma Multiforme (GBM) is a deadly brain tumor with few effective therapies
We demonstrate how a computational algorithm we have devised allows one to identify the G-CIMP phenotype of any given GBM at nearly 100% accuracy using only mRNA expression data
We have established a method to classify GBM samples into G-CIMP subtypes based on gene expression data
Summary
Glioblastoma Multiforme (GBM) is a deadly brain tumor with few effective therapies. Identification of the underlying pathogenic mechanisms involved in the initiation and progression of this tumor is critical for developing more effective treatments. Recent developments in genomic technology (microarray, generation sequencing etc.) have enabled a large number of GBMs to be genetically characterized at unprecedented levels of detail. Such studies have revealed the heterogeneity between GBMs, they have allowed the identification of subgroups of tumors that are more closely related than others. A number of recent studies using supervised and unsupervised analyses have been published stratifying GBMs into similar subgroups based on mRNA expression profiles [1,2,3,4]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
