Using evolutional properties of gene networks in understanding survival prognosis of glioblastoma.
Previously, we investigated survival prognosis of glioblastoma by applying a gene regulatory approach to a human glioblastoma dataset. Here, we further extend our understanding of survival prognosis of glioblastoma by refining the network inference technique we apply to the glioblastoma dataset with the intent of uncovering further topological properties of the networks. For this study, we modify the approach by specifically looking at both positive and negative correlations separately, as opposed to absolute correlations. There is great interest in applying mathematical modeling approaches to cancer cell line datasets to generate network models of gene regulatory interactions. Analysis of these networks using graph theory metrics can identify genes of interest. The principal approach for modeling microarray datasets has been to group all the cell lines together into one overall network, and then, analyze this network as a whole. As per the previous study, we categorize a human glioblastoma cell line dataset into five categories based on survival data, and analyze each category separately using both negative and positive correlation networks constructed using a modified version of the WGCNA algorithm. Using this approach, we identified a number of genes as being important across different survival stages of the glioblastoma cell lines.
Alex
A
Upton
Theodoros N
TN
Arvanitis
D000465:Algorithms; D001932:Brain Neoplasms / Q000175:diagnosis / Q000235:genetics / Q000378:metabolism; D045744:Cell Line, Tumor; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000379:methods*; D053263:Gene Regulatory Networks / Q000235:genetics*; D023281:Genomics / Q000379:methods*; D005909:Glioblastoma / Q000175:diagnosis / Q000235:genetics / Q000378:metabolism; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis; D011379:Prognosis; D016019:Survival Analysis