Jiang Xue, Zhang Han, Quan Xiongwen
College of Computer and Control Engineering, Nankai University, Tianjin 300350, China.
Biomed Res Int. 2016;2016:3962761. doi: 10.1155/2016/3962761. Epub 2016 Nov 30.
Screening disease-related genes by analyzing gene expression data has become a popular theme. Traditional disease-related gene selection methods always focus on identifying differentially expressed gene between case samples and a control group. These traditional methods may not fully consider the changes of interactions between genes at different cell states and the dynamic processes of gene expression levels during the disease progression. However, in order to understand the mechanism of disease, it is important to explore the dynamic changes of interactions between genes in biological networks at different cell states. In this study, we designed a novel framework to identify disease-related genes and developed a differentially coexpressed disease-related gene identification method based on gene coexpression network (DCGN) to screen differentially coexpressed genes. We firstly constructed phase-specific gene coexpression network using time-series gene expression data and defined the conception of differential coexpression of genes in coexpression network. Then, we designed two metrics to measure the value of gene differential coexpression according to the change of local topological structures between different phase-specific networks. Finally, we conducted meta-analysis of gene differential coexpression based on the rank-product method. Experimental results demonstrated the feasibility and effectiveness of DCGN and the superior performance of DCGN over other popular disease-related gene selection methods through real-world gene expression data sets.
通过分析基因表达数据来筛选疾病相关基因已成为一个热门主题。传统的疾病相关基因选择方法总是专注于识别病例样本与对照组之间的差异表达基因。这些传统方法可能没有充分考虑不同细胞状态下基因间相互作用的变化以及疾病进展过程中基因表达水平的动态过程。然而，为了理解疾病机制，探索不同细胞状态下生物网络中基因间相互作用的动态变化很重要。在本研究中，我们设计了一个新颖的框架来识别疾病相关基因，并开发了一种基于基因共表达网络（DCGN）的差异共表达疾病相关基因识别方法来筛选差异共表达基因。我们首先使用时间序列基因表达数据构建阶段特异性基因共表达网络，并定义了共表达网络中基因差异共表达的概念。然后，我们根据不同阶段特异性网络之间局部拓扑结构的变化设计了两个指标来衡量基因差异共表达的值。最后，我们基于秩积法对基因差异共表达进行元分析。实验结果通过真实世界的基因表达数据集证明了DCGN的可行性和有效性以及DCGN相对于其他流行的疾病相关基因选择方法的优越性能。
