Asian Pacific Journal of Cancer Prevention

  • 제15권18호
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  • Pages.7645-7652
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  • 2014
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  • 1513-7368(pISSN)
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  • 2476-762X(eISSN)
아시아태평양암예방학회 (Asian Pacific Journal of Cancer Prevention)
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Regulatory Network Analysis of MicroRNAs and Genes in Neuroblastoma

  • Wang, Li (College of Computer Science and Technology, Key Lab of Symbol Computation and Knowledge Engineer, Ministry of Education, Jilin University) ;
  • Che, Xiang-Jiu (College of Computer Science and Technology, Key Lab of Symbol Computation and Knowledge Engineer, Ministry of Education, Jilin University) ;
  • Wang, Ning (College of Computer Science and Technology, Key Lab of Symbol Computation and Knowledge Engineer, Ministry of Education, Jilin University) ;
  • Li, Jie (College of Computer Science and Technology, Key Lab of Symbol Computation and Knowledge Engineer, Ministry of Education, Jilin University) ;
  • Zhu, Ming-Hui (College of Computer Science and Technology, Key Lab of Symbol Computation and Knowledge Engineer, Ministry of Education, Jilin University)
  • 발행 : 2014.10.11
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초록

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.

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