Upton Alex, Arvanitis Theodoros N
IEEE J Biomed Health Inform. 2014 May;18(3):810-6. doi: 10.1109/JBHI.2013.2282569. Epub 2013 Sep 18.
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.
此前，我们通过对人类胶质母细胞瘤数据集应用基因调控方法来研究胶质母细胞瘤的生存预后。在此，我们通过改进应用于胶质母细胞瘤数据集的网络推理技术，进一步拓展对胶质母细胞瘤生存预后的理解，旨在揭示网络的更多拓扑特性。在本研究中，我们对方法进行了修改，具体是分别考察正相关和负相关，而非绝对相关性。将数学建模方法应用于癌细胞系数据集以生成基因调控相互作用的网络模型备受关注。使用图论指标对这些网络进行分析可以识别出感兴趣的基因。对微阵列数据集进行建模的主要方法是将所有细胞系归为一个整体网络，然后对这个网络进行整体分析。根据之前的研究，我们基于生存数据将人类胶质母细胞瘤细胞系数据集分为五类，并使用基于WGCNA算法修改版构建的负相关和正相关网络分别对每一类进行分析。通过这种方法，我们确定了一些在胶质母细胞瘤细胞系不同生存阶段都很重要的基因。
