About this Journal Submit a Manuscript Table of Contents
BioMed Research International
Volume 2013 (2013), Article ID 798912, 13 pages
http://dx.doi.org/10.1155/2013/798912
Research Article

Identifying Breast Cancer Subtype Related miRNAs from Two Constructed miRNAs Interaction Networks in Silico Method

Biomedical Engineering Institute of Capital Medical University, Beijing 100069, China

Received 13 July 2013; Revised 29 September 2013; Accepted 4 October 2013

Academic Editor: Qinghua Cui

Copyright © 2013 Lin Hua et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. R. C. Millikan, B. Newman, C.-K. Tse et al., “Epidemiology of basal-like breast cancer,” Breast Cancer Research and Treatment, vol. 109, no. 1, pp. 123–139, 2008. View at Publisher · View at Google Scholar · View at Scopus
  2. N. G. Bediaga, A. Acha-Sagredo, I. Guerra et al., “DNA methylation epigenotypes in breast cancer molecular subtypes,” Breast Cancer Research, vol. 12, no. 5, p. R77, 2010. View at Publisher · View at Google Scholar · View at Scopus
  3. L. A. Carey, C. M. Perou, C. A. Livasy et al., “Race, breast cancer subtypes, and survival in the Carolina Breast Cancer Study,” The Journal of the American Medical Association, vol. 295, no. 21, pp. 2492–2502, 2006. View at Publisher · View at Google Scholar · View at Scopus
  4. C. Fan, D. S. Oh, L. Wessels et al., “Concordance among gene-expression-based predictors for breast cancer,” The New England Journal of Medicine, vol. 355, no. 6, pp. 560–569, 2006. View at Publisher · View at Google Scholar · View at Scopus
  5. X. R. Yang, M. E. Sherman, D. L. Rimm et al., “Differences in risk factors for breast cancer molecular subtypes in a population-based study,” Cancer Epidemiology Biomarkers and Prevention, vol. 16, no. 3, pp. 439–443, 2007. View at Publisher · View at Google Scholar · View at Scopus
  6. M. V. Iorio, M. Ferracin, C.-G. Liu et al., “MicroRNA gene expression deregulation in human breast cancer,” Cancer Research, vol. 65, no. 16, pp. 7065–7070, 2005. View at Publisher · View at Google Scholar · View at Scopus
  7. S. Volinia, G. A. Calin, C.-G. Liu et al., “A microRNA expression signature of human solid tumors defines cancer gene targets,” Proceedings of the National Academy of Sciences of the United States of America, vol. 103, no. 7, pp. 2257–2261, 2006. View at Publisher · View at Google Scholar · View at Scopus
  8. G. Yu, C.-L. Xiao, X. Bo et al., “A new method for measuring functional similarity of microRNAs,” Journal of Integrated Omics, vol. 1, pp. 49–54, 2010.
  9. D. Wang, J. Wang, M. Lu, F. Song, and Q. Cui, “Inferring the human microRNA functional similarity and functional network based on microRNA-associated diseases,” Bioinformatics, vol. 26, no. 13, pp. 1644–1650, 2010. View at Publisher · View at Google Scholar · View at Scopus
  10. I. Ulitsky, L. C. Laurent, and R. Shamir, “Towards computational prediction of microRNA function and activity,” Nucleic Acids Research, vol. 38, no. 15, p. e160, 2010. View at Publisher · View at Google Scholar · View at Scopus
  11. J. Sun, M. Zhou, H. Yang, J. Deng, L. Wang, and Q. Wang, “Inferring potential microRNA-microRNA associations based on targeting propensity and connectivity in the context of protein interaction network,” PLoS ONE, vol. 8, no. 7, Article ID e69719, 2013.
  12. Network TCGA, “Comprehensive molecular portraits of human breast tumours,” Nature, vol. 490, pp. 61–70, 2012.
  13. D. Luo, J. M. Wilson, N. Harvel et al., “A systematic evaluation of miRNA:mRNA interactions involved in the migration and invasion of breast cancer cells,” Journal of Translational Medicine, vol. 11, p. 57, 2013.
  14. M. Lionetti, M. Biasiolo, L. Agnelli et al., “Identification of microRNA expression patterns and definition of a microRNA/mRNA regulatory network in distinct molecular groups of multiple myeloma,” Blood, vol. 114, no. 25, pp. e20–e26, 2009. View at Publisher · View at Google Scholar · View at Scopus
  15. S. Zhang, Q. Li, J. Liu, and X. J. Zhou, “A novel computational framework for simultaneous integration of multiple types of genomic data to identify microrna-gene regulatory modules,” Bioinformatics, vol. 27, no. 13, pp. i401–i409, 2011. View at Publisher · View at Google Scholar · View at Scopus
  16. C. O. Daub, R. Steuer, J. Selbig, and S. Kloska, “Estimating mutual information using B-spline functions—an improved similarity measure for analysing gene expression data,” BMC Bioinformatics, vol. 5, p. 118, 2004. View at Publisher · View at Google Scholar · View at Scopus
  17. E. Enerly, I. Steinfeld, K. Kleivi et al., “miRNA-mRNA integrated analysis reveals roles for mirnas in primary breast tumors,” PLoS ONE, vol. 6, no. 2, Article ID e16915, 2011. View at Publisher · View at Google Scholar · View at Scopus
  18. V. G. Tusher, R. Tibshirani, and G. Chu, “Significance analysis of microarrays applied to the ionizing radiation response,” Proceedings of the National Academy of Sciences of the United States of America, vol. 98, no. 9, pp. 5116–5121, 2001. View at Publisher · View at Google Scholar · View at Scopus
  19. L. Hua, P. Zhou, L. Li, H. Liu, and Z. Yang, “Prioritizing breast cancer subtype related miRNAs using miRNA-mRNA dysregulated relationships extracted from their dual expression profiling,” Journal of Theoretical Biology, vol. 331, pp. 1–11, 2013.
  20. G. Sales and C. Romualdi, “Parmigene—a parallel R package for mutual information estimation and gene network reconstruction,” Bioinformatics, vol. 27, no. 13, pp. 1876–1877, 2011. View at Publisher · View at Google Scholar · View at Scopus
  21. S.-K. Kim, J.-W. Nam, J.-K. Rhee, W.-J. Lee, and B.-T. Zhang, “miTarget: microRNA target gene prediction using a support vector machine,” BMC Bioinformatics, vol. 7, p. 411, 2006. View at Publisher · View at Google Scholar · View at Scopus
  22. V. Chandra, R. Girijadevi, A. S. Nair, S. S. Pillai, and R. M. Pillai, “MTar: a computational microRNA target prediction architecture for human transcriptome,” BMC Bioinformatics, vol. 11, supplement 1, p. S2, 2010. View at Publisher · View at Google Scholar · View at Scopus
  23. J. Sun, X. Gong, B. Purow, and Z. Zhao, “Uncovering microRNA and transcription factor mediated regulatory networks in glioblastoma,” PLOS Computational Biology, vol. 8, no. 7, p. 1, 2012.
  24. L. C. Freeman, “Centrality in social networks conceptual clarification,” Social Networks, vol. 1, no. 3, pp. 215–239, 1979.
  25. S. Wasserman and K. Faust, Social Network Analysis: Methods and Applications, Cambridge University Press, New York, NY, USA, 1994.
  26. C. Wang, W. Jiang, W. Li et al., “Topological properties of the drug targets regulated by microRNA in human protein-protein interaction network,” Journal of Drug Targeting, vol. 19, no. 5, pp. 354–364, 2011. View at Publisher · View at Google Scholar · View at Scopus
  27. M. Zhu, L. Gao, X. Li et al., “The analysis of the drug-targets based on the topological properties in the human protein-protein interaction network,” Journal of Drug Targeting, vol. 17, no. 7, pp. 524–532, 2009. View at Publisher · View at Google Scholar · View at Scopus
  28. G. H. John and P. Langley, “Estimating continuous distributions in Bayesian classifiers,” in Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, pp. 338–345, Morgan Kaufmann, San Mateo, Calif, USA, 1995.
  29. G. Gutin, A. Yeo, and A. Zverovich, “Traveling salesman should not be greedy: domination analysis of greedy-type heuristics for the TSP,” Discrete Applied Mathematics, vol. 117, no. 1–3, pp. 81–86, 2002. View at Publisher · View at Google Scholar · View at Scopus
  30. T. S. Furey, “Support vector machine classification and validation of cancer tissue samples using microarray expression data,” Bioinformatics, vol. 16, no. 10, pp. 906–914, 2000. View at Scopus
  31. H. Pang, A. Lin, M. Holford et al., “Pathway analysis using random forests classification and regression,” Bioinformatics, vol. 22, no. 16, pp. 2028–2036, 2006. View at Publisher · View at Google Scholar · View at Scopus
  32. J. J. Goeman, S. van de Geer, F. de Kort, and H. C. van Houwellingen, “A global test for groups fo genes: testing association with a clinical outcome,” Bioinformatics, vol. 20, no. 1, pp. 93–99, 2004. View at Publisher · View at Google Scholar · View at Scopus
  33. S. D. Selcuklu, M. T. A. Donoghue, K. Rehmet et al., “MicroRNA-9 inhibition of cell proliferation and identification of novel miR-9 targets by transcriptome profiling in breast cancer cells,” Journal of Biological Chemistry, vol. 287, pp. 29516–29528, 2012.
  34. H. Yi, B. Liang, J. Jia et al., “Differential roles of miR-199a-5p in radiation-induced autophagy in breast cancer cells,” FEBS Letters, vol. 587, no. 5, pp. 436–443, 2013.
  35. W. Jiang, X. Li, S. Rao et al., “Constructing disease-specific gene networks using pair-wise relevance metric: application to colon cancer identifies interleukin 8, desmin and enolase 1 as the central elements,” BMC Systems Biology, vol. 2, no. 72, 2008. View at Publisher · View at Google Scholar · View at Scopus
  36. W. Huang, P. Wang, Z. Liu, and L. Zhang, “Identifying disease associations via genome-wide association studies,” BMC Bioinformatics, vol. 10, supplement 1, 2009. View at Publisher · View at Google Scholar · View at Scopus
  37. J. Lim, T. Hao, C. Shaw et al., “A protein-protein interaction network for human inherited ataxias and disorders of purkinje cell degeneration,” Cell, vol. 125, no. 4, pp. 801–814, 2006. View at Publisher · View at Google Scholar · View at Scopus
  38. K. Cuk, M. Zucknick, J. Heil et al., “Circulating microRNAs in plasma as early detection markers for breast cancer,” International Journal of Cancer, vol. 132, no. 7, pp. 1602–1612, 2013. View at Publisher · View at Google Scholar
  39. M. Yang, J. Chen, F. Su et al., “Microvesicles secreted by macrophages shuttle invasion-potentiating microRNAs into breast cancer cells,” Molecular Cancer, vol. 10, p. 117, 2011. View at Publisher · View at Google Scholar · View at Scopus
  40. S. Volinia, M. Galasso, M. E. Sana et al., “Breast cancer signatures for invasiveness and prognosis defined by deep sequencing of microRNA,” Proceedings of the National Academy of Sciences of the United States of America, vol. 109, no. 8, pp. 3024–3029, 2012. View at Publisher · View at Google Scholar · View at Scopus
  41. J. Xu, C.-X. Li, Y.-S. Li et al., “MiRNA-miRNA synergistic network: construction via co-regulating functional modules and disease miRNA topological features,” Nucleic Acids Research, vol. 39, no. 3, pp. 825–836, 2011. View at Publisher · View at Google Scholar · View at Scopus
  42. Q. Jiang, Y. Wang, Y. Hao et al., “miR2Disease: a manually curated database for microRNA deregulation in human disease,” Nucleic Acids Research, vol. 37, no. 1, pp. D98–D104, 2009. View at Publisher · View at Google Scholar · View at Scopus
  43. P. Langfelder and S. Horvath, “WGCNA: an R package for weighted correlation network analysis,” BMC Bioinformatics, vol. 9, p. 559, 2008. View at Publisher · View at Google Scholar · View at Scopus
  44. C. Tibiche and E. Wang, “MicroRNA regulatory patterns on the human metabolic network,” The Open Systems Biology Journal, vol. 1, pp. 1–8, 2008.
  45. H. Peurala, D. Greco, T. Heikkinen et al., “MiR-34a expression has an effect for lower risk of metastasis and associates with expression patterns predicting clinical outcome in breast cancer,” PLoS ONE, vol. 6, no. 11, Article ID e26122, 2011. View at Publisher · View at Google Scholar · View at Scopus
  46. A. Ahmad, “Pathways to breast cancer recurrence,” ISRN Oncology, vol. 2013, Article ID 290568, 16 pages, 2013. View at Publisher · View at Google Scholar
  47. R. Garzon, G. A. Calin, and C. M. Croce, “MicroRNAs in cancer,” Annual Review of Medicine, vol. 60, pp. 167–179, 2009. View at Publisher · View at Google Scholar · View at Scopus
  48. J. Lu, G. Getz, E. A. Miska et al., “MicroRNA expression profiles classify human cancers,” Nature, vol. 435, no. 7043, pp. 834–838, 2005. View at Publisher · View at Google Scholar · View at Scopus
  49. G. A. Calin and C. M. Croce, “MicroRNA signatures in human cancers,” Nature Reviews Cancer, vol. 6, no. 11, pp. 857–866, 2006. View at Publisher · View at Google Scholar · View at Scopus
  50. T. Suzuki, M. Sugiyama, J. Sese, and T. Kanamori, “Approximating mutual information by maximum likelihood density ratio estimation,” Journal of Machine Learning Research, vol. 4, pp. 5–20, 2008.
  51. W. M. Grady, R. K. Parkin, P. S. Mitchell et al., “Epigenetic silencing of the intronic microRNA hsa-miR-342 and its host gene EVL in colorectal cancer,” Oncogene, vol. 27, no. 27, pp. 3880–3888, 2008. View at Publisher · View at Google Scholar · View at Scopus
  52. C. Mayr, M. T. Hemann, and D. P. Bartel, “Disrupting the pairing between let-7 and Hmga2 enhances oncogenic transformation,” Science, vol. 315, no. 5818, pp. 1576–1579, 2007. View at Publisher · View at Google Scholar · View at Scopus
  53. W. H. Majoros, P. Lekprasert, N. Mukherjee et al., “MicroRNA target site identification by integrating sequence and binding information,” Nature Methods, vol. 10, no. 7, pp. 630–633, 2013.