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

Yang Xiang,Kun Huang,Cun-Quan Zhang

doi:10.1186/1471-2105-13-s2-s12

Yang Xiang, Kun Huang + Show 1 more

Open Access

https://doi.org/10.1186/1471-2105-13-s2-s12

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Abstract

BackgroundUsing 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.MethodsWe 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.ResultsUsing 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.ConclusionsWe 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.

Highlights

Using gene co-expression analysis, researchers were able to predict clusters of genes with consistent functions that are relevant to cancer development and prognosis
Many network mining algorithms take only binary edges by imposing a threshold on the PCC values and transforming the network into a sparse unweighted gene co-expression network (UWGCN)
We identified 16 gene networks associated with glioblastoma multiforme (GBM) prognosis

Summary

Introduction

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. In order to overcome this issue and identify functionally related genes associated with disease development and prognosis, several approaches have been adopted. One of such approaches is to use gene co-expression analysis. In [6], an algorithm called CODENSE was developed to identify frequent UWGCNs from multiple datasets and this method has been applied to cancer biomarker discovery.

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