Integrated Network Analysis of Genetic and Epigenetic Factors in Glioblastoma Multiform

Glioblastoma is the most common and aggressive type of primary brain tumor in humans. It is located preferentially in the cerebral hemispheres. Glioblastoma arises from complex interactions between a variety of genetic, epigenetic alterations and environmental perturbations. However, the precise mechanism of glioblastoma is unknown and its survival rate is very low. The Cancer Genome Atlas (TCGA) generates large-scale multi-dimensional genetic and epigenetic data to catalogue and identify cancer causing alterations (Kuhn, et al., 2008). Glioblastoma (GBM) is the first cancer studied by TCGA. In TCGA glioblastoma pilot project, a total of 601 genes were sequenced for detection of somatic mutations in 179 tumor and matched normal tissues pairs; expressions of 12,042 genes were measured in 243 tumor tissue samples and 10 normal tissue samples and 1 cell line; expressions of 534 miRNAs were profiled in 240 tumor tissue samples and 10 normal tissue samples and a total of 2,994 genes were examined for methylation in 239 tumor tissue samples and 1 cell line. This dataset will be used as an example for developing system biology and network approach as a general framework for integrated analysis of genetic and epigenetic alternations in cancer studies. Biological functions and mechanisms are encoded in network properties. An important strategy for unraveling the mechanisms of initiation and progression of cancer is to conduct analysis of complex genetic and epigenetic networks and study their behaviors under genetic and epigenetic perturbations. Robustness of a biological network, ability to retain much of its functionality in the face of perturbation (Dartnell, et al., 2005), has emerged as a fundamental concept in the study of network topological properties (Demetrius and Manke, 2005 ). The locations of the DNA variants, mRNA, miRNA, and methylation in the genetic and epigenetic networks are likely to affect the phenotypes. We use network structural analysis as a tool to identify a set of key cancer causing genome alternations and core modules of biological networks that play an essential role in the development of cancer. Purpose of this report is to use system biology approaches to develop novel analytic strategies for systematically integrating genetic and epigenetic data. To achieve this, we