Enhanced construction of gene regulatory networks using hub gene information.
Enhanced construction of gene regulatory networks using hub gene information.
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DOI：
10.1186/s12859-017-1576-1
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发表时间：
2017-03-23
影响因子：
3
通讯作者：
Xiao G
中科院分区：
文献类型：
--
作者：
Yu D;Lim J;Wang X;Liang F;Xiao G
基因调控网络揭示了基因如何协同工作以发挥其生物学功能。从基因表达数据重建基因网络极大地促进了我们对潜在生物学机制的理解，并为生物标志物和药物的发现提供了新的机会。在基因网络中，与其他基因有许多相互作用的基因称为枢纽基因，通常在基因调控和生物过程中发挥重要作用。在这项研究中，我们开发了一种使用基于部分相关的方法重建基因网络的方法，该方法结合了有关中心基因的先验信息。通过仿真研究和两个实际数据示例，我们比较了现有方法和所提出的方法在估计网络结构方面的性能。在模拟研究中，我们表明，与现有方法相比，所提出的策略减少了估计网络结构的错误。当应用于大肠杆菌时，我们提出的ESPACE方法构建的调控网络比SPACE方法更符合当前的生物学知识。此外，所提出的方法在肺癌中的应用已经确定了中心基因，其 mRNA 表达可预测癌症进展和患者对治疗的反应。我们已经证明，将枢纽基因信息纳入估计网络结构可以提高现有方法的性能。
Gene regulatory networks reveal how genes work together to carry out their biological functions. Reconstructions of gene networks from gene expression data greatly facilitate our understanding of underlying biological mechanisms and provide new opportunities for biomarker and drug discoveries. In gene networks, a gene that has many interactions with other genes is called a hub gene, which usually plays an essential role in gene regulation and biological processes. In this study, we developed a method for reconstructing gene networks using a partial correlation-based approach that incorporates prior information about hub genes. Through simulation studies and two real-data examples, we compare the performance in estimating the network structures between the existing methods and the proposed method. In simulation studies, we show that the proposed strategy reduces errors in estimating network structures compared to the existing methods. When applied to Escherichia coli, the regulation network constructed by our proposed ESPACE method is more consistent with current biological knowledge than the SPACE method. Furthermore, application of the proposed method in lung cancer has identified hub genes whose mRNA expression predicts cancer progress and patient response to treatment. We have demonstrated that incorporating hub gene information in estimating network structures can improve the performance of the existing methods.