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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

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.

과제정보

연구 과제 주관 기관 : National Natural Science Foundation of China

참고문헌

  1. Chen K, Rajewsky N (2007). The evolution of gene regulation by transcription factors and microRNAs. Nature Reviews Genetics, 8, 93-103.
  2. Baskerville S, Bartel DP (2005). Microarray profiling of microRNAs reveals frequent coexpression with neighboring miRNAs and host genes. RNA, 11, 241-7. https://doi.org/10.1261/rna.7240905
  3. Cao G, Huang B, Liu Z, et al (2010). Intronic miR-301 feedback regulates its host gene, ska2, in A549 cells by targeting MEOX2 to affect ERK/CREB pathways. Biochem Biophys Res Common, 396, 978-82. https://doi.org/10.1016/j.bbrc.2010.05.037
  4. Chekmenec DS, Haid C, Kel AE (2005). P-Match: transcription factor binding site search by combining patterns and weight matrices. Nucleic Acids Res, 33, W432-7. https://doi.org/10.1093/nar/gki441
  5. Di PD, Ambrogio C, Pastorino F (2011). Selective therapeutic targeting of the anaplastic lymphoma kinase with liposomal siRNA induces apoptosis and inhibits angiogenesis in neuroblastoma. Molecular Therapy, 19, 2201-12. https://doi.org/10.1038/mt.2011.142
  6. Griffiths-Jones S, Saini HK, van Dongen S, et al (2008). miRBase: tools for microRNA genomics. Nucleic Acids Res, 36, D154-8. https://doi.org/10.1093/nar/gkn221
  7. Hsu SD, Lin FM, Wu WY, et al (2011). miRTarBase: a database curates experimentally validated microRNA-target interactions. Nucleic Acids Res, 39, D163-9. https://doi.org/10.1093/nar/gkq1107
  8. Latchman DS (1997). Transcription factors: an overview. Int J Biochem Cell Biol, 29, 1305-12. https://doi.org/10.1016/S1357-2725(97)00085-X
  9. Jiang QH, Wang YD, Hao YY, et al (2009). miR2 disease: a manually curated database for microRNA deregulation in human disease. Nucleic Acids Res, 37, D98-104. https://doi.org/10.1093/nar/gkn714
  10. Karin M (1990). Too many transcription factors: positive and negative interactions. New Biol, 2, 126-31.
  11. Langer I, Vertongen P, Perret J, et al (2000). Expression of vascular endothelial growth factor (VEGF) and VEGF receptors in human neuroblastomas. Med Pediatr Oncol, 34, 386-93. https://doi.org/10.1002/(SICI)1096-911X(200006)34:6<386::AID-MPO2>3.0.CO;2-3
  12. Liu DF, Wu JT, Wang JM, et al (2012). MicroRNA expression profile analysis reveals diagnostic biomarker for human prostate cancer. Asian Pac J Cancer Prev, 13, 3313-7. https://doi.org/10.7314/APJCP.2012.13.7.3313
  13. Naves T, Jawhari S, Jauberteau MO, et al (2013). Autophagy takes place in mutated p53 neuroblastoma cells in response to hypoxia mimetic CoCl2. Biochem Pharmacol, 85, 1153-61. https://doi.org/10.1016/j.bcp.2013.01.022
  14. Ogura T, Hiyama E, Kamei N, et al (2012 ). Clinical feature of anaplastic lymphoma kinase–mutated neuroblastom. J Pediatr Surg, 47, 1789-96. https://doi.org/10.1016/j.jpedsurg.2012.05.007
  15. Rodriguez A, Griffiths-Jones S, Ashurst JL (2004). Identification of mammalian microRNA host genes and transcription units. Genome Res, 14, 1902-10. https://doi.org/10.1101/gr.2722704
  16. Sethupathy P, Corda B, Hatzigeorgiou AG (2006). TarBase: a comprehensive database of experimentally supported animal microRNA targets. RNA, 12, 192-7.
  17. Tong L, Yang XX, Liu MF, et al (2012). Mutational analysis of key EGFR pathway genes in Chinese breast cancer patients. Asian Pac J Cancer Prev, 13, 5599-603. https://doi.org/10.7314/APJCP.2012.13.11.5599
  18. van Golen CM, Castle VP, Feldman EL (2000). IGF-I receptor activation and BCL-2 overexpression prevent early apoptotic events in human neuroblastoma. Cell Death Differ, 7, 654-65. https://doi.org/10.1038/sj.cdd.4400693
  19. Wang J, Lu M, Qiu C, et al (2010). TransmiR: a transcription factor-microRNA regulation database. Nucleic Acids Res, 38, D119-22. https://doi.org/10.1093/nar/gkp803
  20. Westermark UK, Wilhelm M, Frenzel A, et al (2011). The MYCN oncogene and differentiation in neuroblastoma. Semin Cancer Biol, 21, 256-66. https://doi.org/10.1016/j.semcancer.2011.08.001
  21. Zhang B, Xu ZW, Wang KH, et al (2013). Complex regulatory network of MicroRNAs, transcription factors, gene alterations in adrenocortical cancer. Asian Pac J Cancer Prev, 14, 2265-8. https://doi.org/10.7314/APJCP.2013.14.4.2265

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