Wang Li, Che Xiang-Jiu, Wang Ning, Li Jie, Zhu Ming-Hui
College of Computer Science and Technology, Key Lab of Symbol Computation and Knowledge Engineer, Ministry of Education, Jilin University, Changchun, China E-mail :
Asian Pac J Cancer Prev. 2014;15(18):7645-52. doi: 10.7314/apjcp.2014.15.18.7645.
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.
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,科学家们已经对NB中的微小RNA(miRNA)及其基因有了相当多的了解。然而,发现内部调控网络仍然存在问题。我们的研究重点是确定差异表达的miRNA、它们的靶基因和转录因子(TF),这些对NB的发病机制有深远影响。在这里,我们构建了三个调控网络:差异表达网络、相关网络和全局网络。我们比较并分析了这三个网络之间的差异,以区分关键途径和重要节点。某些途径表现出特定特征。差异表达网络由已确定的差异表达基因、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基因、差异表达miRNA和TF的途径如何影响NB的进展。例如,MYCN是NB的一个突变基因,被hsa-miR-29a和hsa-miR-34a靶向,并调节另外八个靶向基因VEGFA、BCL2、REL2等的差异表达miRNA。进一步获得相关基因和miRNA以构建相关网络,观察到一个miRNA及其靶基因表现出特殊特征。例如,hsa-miR-34a靶向基因MYC,而MYC又反过来调节hsa-miR-34a。这形成了一种自适应关联。像MYC和PTEN这样的TF有六种类型的相邻节点,对其他类别的TF进行研究真的有助于证明TF通过参与NB发病机制的重要miRNA的表达来影响途径。本研究提供的全面数据部分揭示了NB的机制,应该有助于未来的研究获得更多关于NB的重要和相关数据结果。