Regulatory network analysis of microRNAs and genes in neuroblastoma.
Regulatory network analysis of microRNAs and genes in neuroblastoma.
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DOI：
10.7314/apjcp.2014.15.18.7645
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Li Wang;X. Che;Ning Wang;Jie Li;Minghui Zhu
中科院分区：
文献类型：
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作者：
Li Wang;X. Che;Ning Wang;Jie Li;Minghui Zhu
神经母细胞瘤（NB）是最常见的颅外实体瘤，占儿童癌症的10%。迄今为止，科学家们已经获得了相当多的关于NB中microRNA（miRNAs）及其基因的知识。然而，发现内部调节网络仍然存在问题。我们的研究重点是确定差异表达的miRNA，其靶基因和转录因子（TF）发挥深远的影响，NB的发病机制。在这里，我们构建了三个调控网络：差异表达，相关和全球。我们比较和分析了三种网络之间的差异，以区分关键路径和重要节点。某些途径表现出特定的特征。差异表达网络由已经鉴定的差异表达基因、miRNA及其宿主基因组成。通过这个网络，我们可以清楚地看到差异表达的基因、差异表达的miRNAs和TF的通路如何影响NB的进展。以NB的突变基因MYCN为例，hsa-miR-29 a和hsa-miR-34 a靶向调控另外8个差异表达的miRNAs，分别靶向VEGFA、BCL 2、BCL 2等基因，进一步获得相关基因和miRNAs构建相关网络，观察到一个miRNAs及其靶基因表现出特殊的功能。例如，Hsa-miR-34 a靶向基因MYC，MYC反过来调节hsa-miR-34 a。这就形成了一种自适应的关联。具有六种类型的相邻节点的TF如MYC和PTEN以及研究的其他类别的TF确实可以帮助证明TF通过参与NB发病机制的重要miRNA的表达来影响通路。本研究提供了全面的数据，部分揭示了NB的机制，并将有助于未来的研究，以获得更有意义的和相关的数据结果。
Neuroblastoma (NB), the most common extracranial solid tumor, accounts for 10% of childhood cancer. To date, scientists have gained quite a lot of knowledge about microRNAs (miRNAs) and their genes in NB. Discovering inner regulation networks, however, still presents problems. Our study was focused on determining differentially-expressed miRNAs, their target genes and transcription factors (TFs) which exert profound influence on the pathogenesis of NB. Here we constructed three regulatory networks: differentially-expressed, related and global. We compared and analyzed the differences between the three networks to distinguish key pathways and significant nodes. Certain pathways demonstrated specific features. The differentially-expressed network consists of already identified differentially-expressed genes, miRNAs and their host genes. With this network, we can clearly see how pathways of differentially expressed genes, differentially expressed miRNAs and TFs affect on the progression of NB. MYCN, for example, which is a mutated gene of NB, is targeted by hsa-miR-29a and hsa-miR-34a, and regulates another eight differentially-expressed miRNAs that target genes VEGFA, BCL2, REL2 and so on. Further related genes and miRNAs were obtained to construct the related network and it was observed that a miRNA and its target gene exhibit special features. Hsa-miR-34a, for example, targets gene MYC, which regulates hsa-miR-34a in turn. This forms a self-adaption association. TFs like MYC and PTEN having six types of adjacent nodes and other classes of TFs investigated really can help to demonstrate that TFs affect pathways through expressions of significant miRNAs involved in the pathogenesis of NB. The present study providing comprehensive data partially reveals the mechanism of NB and should facilitate future studies to gain more significant and related data results for NB.