No abstract

The knowledge on the biological molecular mechanisms underlying cancer is important for the precise diagnosis and treatment of cancer patients. Detecting dysregulated pathways in cancer can provide insights into the mechanism of cancer and help to detect novel drug targets. Based on the wide existing mutual exclusivity among mutated genes and the interrelationship between gene mutations and expression changes, this study presents a network-based method to detect the dysregulated pathways from gene mutations and expression data of the glioblastoma cancer. First, the authors construct a gene network based on mutual exclusivity between each pair of genes and the interaction between gene mutations and expression changes. Then they detect all complete subgraphs using CFinder clustering algorithm in the constructed gene network. Next, the two gene sets whose overlapping scores are above a specific threshold are merged. Finally, they obtain two dysregulated pathways in which there are glioblastoma-related multiple genes which are closely related to the two subtypes of glioblastoma. The results show that one dysregulated pathway revolving around epidermal growth factor receptor is likely to be associated with the primary subtype of glioblastoma, and the other dysregulated pathway revolving around TP53 is likely to be associated with the secondary subtype of glioblastoma.