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This article is part of the supplement: Proceedings from the Great Lakes Bioinformatics Conference 2011

Open Access Proceedings

Predicting glioblastoma prognosis networks using weighted gene co-expression network analysis on TCGA data

Yang Xiang1, Cun-Quan Zhang2 and Kun Huang13*

Author Affiliations

1 Department of Biomedical Informatics, The Ohio State University, Columbus, USA

2 Department of Mathematics, West Virginia University, Morgantown, USA

3 The Comprehensive Cancer Center Biomedical Informatics Shared Resource, The Ohio State University, Columbus, USA

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BMC Bioinformatics 2012, 13(Suppl 2):S12  doi:10.1186/1471-2105-13-S2-S12

Published: 13 March 2012

Abstract

Background

Using gene co-expression analysis, researchers were able to predict clusters of genes with consistent functions that are relevant to cancer development and prognosis. We applied a weighted gene co-expression network (WGCN) analysis algorithm on glioblastoma multiforme (GBM) data obtained from the TCGA project and predicted a set of gene co-expression networks which are related to GBM prognosis.

Methods

We modified the Quasi-Clique Merger algorithm (QCM algorithm) into edge-covering Quasi-Clique Merger algorithm (eQCM) for mining weighted sub-network in WGCN. Each sub-network is considered a set of features to separate patients into two groups using K-means algorithm. Survival times of the two groups are compared using log-rank test and Kaplan-Meier curves. Simulations using random sets of genes are carried out to determine the thresholds for log-rank test p-values for network selection. Sub-networks with p-values less than their corresponding thresholds were further merged into clusters based on overlap ratios (>50%). The functions for each cluster are analyzed using gene ontology enrichment analysis.

Results

Using the eQCM algorithm, we identified 8,124 sub-networks in the WGCN, out of which 170 sub-networks show p-values less than their corresponding thresholds. They were then merged into 16 clusters.

Conclusions

We identified 16 gene clusters associated with GBM prognosis using the eQCM algorithm. Our results not only confirmed previous findings including the importance of cell cycle and immune response in GBM, but also suggested important epigenetic events in GBM development and prognosis.