Predicting glioblastoma prognosis networks using weighted gene co-expression network analysis on TCGA data.
Predicting glioblastoma prognosis networks using weighted gene co-expression network analysis on TCGA data.
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DOI：
10.1186/1471-2105-13-s2-s12
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发表时间：
2012-03-13
影响因子：
3
通讯作者：
Huang K
中科院分区：
文献类型：
--
作者：
Xiang Y;Zhang CQ;Huang K
通过基因共表达分析，研究人员能够预测与癌症发展和预后相关的具有一致功能的基因簇。我们对TCGA项目获得的多形性胶质母细胞瘤（GBM）数据应用加权基因共表达网络（WGCN）分析算法，并预测了一组与GBM预后相关的基因共表达网络。我们将准派合并算法（QCM 算法）修改为边缘覆盖准派合并算法（eQCM），用于挖掘 WGCN 中的加权子网络。每个子网络被视为一组特征，用于使用 K 均值算法将患者分为两组。使用对数秩检验和Kaplan-Meier曲线比较两组的生存时间。使用随机基因组进行模拟以确定网络选择的对数秩检验 p 值的阈值。 p值小于相应阈值的子网络根据重叠率（> 50％）进一步合并成簇。使用基因本体富集分析来分析每个簇的功能。使用 eQCM 算法，我们在 WGCN 中识别出 8,124 个子网络，其中 170 个子网络的 p 值小于相应的阈值。然后它们被合并成16个集群。我们使用 eQCM 算法识别了 16 个与 GBM 预后相关的基因簇。我们的结果不仅证实了之前的发现，包括细胞周期和免疫反应在 GBM 中的重要性，而且还提示了 GBM 发育和预后中重要的表观遗传事件。
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. 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. 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. 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.