Inferring the perturbed microRNA regulatory networks from gene expression data using a network propagation based method.
Inferring the perturbed microRNA regulatory networks from gene expression data using a network propagation based method.
复制标题
使用基于网络传播的方法从基因表达数据推断受干扰的 microRNA 调控网络
DOI：
10.1186/1471-2105-15-255
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发表时间：
2014-07-29
影响因子：
3
通讯作者：
Li Y
中科院分区：
文献类型：
--
作者：
Wang T;Gu J;Li Y
背景MicroRNAs（miRNAs）是一类内源性小分子调控RNA。鉴定失调或干扰的miRNA及其关键靶基因对于理解与所研究的细胞过程相关的调控网络是重要的。通过整合全基因组基因表达数据和基于序列的miRNA靶点预测，已经开发了几种计算方法来推断扰动的miRNA调控网络。然而，大多数方法仅利用miRNA直接靶基因的表达信息，很少考虑miRNA扰动对全局基因调控网络的次级影响。该方法在基因调控网络中使用带重启的随机游走来模拟miRNA扰动的网络效应。然后，采用前向搜索策略，评估了miRNA扰动的网络效应与基因差异表达水平之间相关性的显著性。结果表明，我们的方法在重新发现癌细胞系中实验干扰的miRNA方面优于几种比较方法。将其应用于结直肠癌临床患者的基因表达数据集，并推断了结直肠癌中受到干扰的miRNA调控网络，包括已知的致癌或抑癌miRNA，如miR-17、miR-26和miR-145。结论基于网络传播的方法充分利用了miRNA干扰对靶基因的网络效应。利用基因表达数据推断与所研究的生物过程相关的受干扰的miRNA及其关键靶基因是一种有用的方法。
BackgroundMicroRNAs (miRNAs) are a class of endogenous small regulatory RNAs. Identifications of the dys-regulated or perturbed miRNAs and their key target genes are important for understanding the regulatory networks associated with the studied cellular processes. Several computational methods have been developed to infer the perturbed miRNA regulatory networks by integrating genome-wide gene expression data and sequence-based miRNA-target predictions. However, most of them only use the expression information of the miRNA direct targets, rarely considering the secondary effects of miRNA perturbation on the global gene regulatory networks.ResultsWe proposed a network propagation based method to infer the perturbed miRNAs and their key target genes by integrating gene expressions and global gene regulatory network information. The method used random walk with restart in gene regulatory networks to model the network effects of the miRNA perturbation. Then, it evaluated the significance of the correlation between the network effects of the miRNA perturbation and the gene differential expression levels with a forward searching strategy. Results show that our method outperformed several compared methods in rediscovering the experimentally perturbed miRNAs in cancer cell lines. Then, we applied it on a gene expression dataset of colorectal cancer clinical patient samples and inferred the perturbed miRNA regulatory networks of colorectal cancer, including several known oncogenic or tumor-suppressive miRNAs, such as miR-17, miR-26 and miR-145.ConclusionsOur network propagation based method takes advantage of the network effect of the miRNA perturbation on its target genes. It is a useful approach to infer the perturbed miRNAs and their key target genes associated with the studied biological processes using gene expression data.
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