Epigenetic alterations, including methylation, have been shown to be an important mechanism of gene silencing in cancer. Ependymoma has been well characterized at the DNA copy number and mRNA expression levels. However little is known about DNA methylation changes. To gain a more global view of the methylation profile of ependymoma we conducted an array-based analysis. Our data demonstrated tumors to segregate according to their location in the CNS, which was associated with a difference in the global level of methylation. Supratentorial and spinal tumors displayed significantly more hypermethylated genes than posterior fossa tumors, similar to the 'CpG island methylator phenotype' (CIMP) identified in glioma and colon carcinoma. This hypermethylated profile was associated with an increase in expression of genes encoding for proteins involved in methylating DNA, suggesting an underlying mechanism. An integrated analysis of methylation and mRNA expression array data allowed us to identify methylation-induced expression changes. Most notably genes involved in the control of cell growth and death and the immune system were identified, including members of the JNK pathway and PPARG. In conclusion, we have generated a global view of the methylation profile of ependymoma. The data suggests epigenetic silencing of tumor suppressor genes is an important mechanism in the pathogenesis of supratentorial and spinal, but not posterior fossa ependymomas. Hypermethylation correlated with a decrease in expression of a number of tumor suppressor genes and pathways that could be playing an important role in tumor pathogenesis.
Pubmed ID: 22109108 RIS Download
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Software repository for R packages related to analysis and comprehension of high throughput genomic data. Uses separate set of commands for installation of packages. Software project based on R programming language that provides tools for analysis and comprehension of high throughput genomic data.
View all literature mentionsA web-based software application that enables users to analyze, integrate, and understand data derived from gene expression, microRNA, and SNP microarrays, metabolomics, proteomics, and RNA-Seq experiments, and small-scale experiments that generate gene and chemical lists. Users can search for targeted information on genes, proteins, chemicals, and drugs, and build interactive models of experimental systems. IPA allows exploration of molecular, chemical, gene, protein and miRNA interactions, creation of custom molecular pathways, and the ability to view and modify metabolic, signaling, and toxicological canonical pathways. In addition to the networks and pathways that can be created, IPA can provide multiple layering of additional information, such as drugs, disease genes, expression data, cellular functions and processes, or a researchers own genes or chemicals of interest.
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