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BioMed Research International
Volume 2013 (2013), Article ID 798912, 13 pages
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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Network TCGA, “Comprehensive molecular portraits of human breast tumours,” Nature, vol. 490, pp. 61–70, 2012.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- L. C. Freeman, “Centrality in social networks conceptual clarification,” Social Networks, vol. 1, no. 3, pp. 215–239, 1979.
- S. Wasserman and K. Faust, Social Network Analysis: Methods and Applications, Cambridge University Press, New York, NY, USA, 1994.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- W. Huang, P. Wang, Z. Liu, and L. Zhang, “Identifying disease associations via genome-wide association studies,” BMC Bioinformatics, vol. 10, supplement 1, 2009.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- P. Langfelder and S. Horvath, “WGCNA: an R package for weighted correlation network analysis,” BMC Bioinformatics, vol. 9, p. 559, 2008.
- C. Tibiche and E. Wang, “MicroRNA regulatory patterns on the human metabolic network,” The Open Systems Biology Journal, vol. 1, pp. 1–8, 2008.
- 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.
- A. Ahmad, “Pathways to breast cancer recurrence,” ISRN Oncology, vol. 2013, Article ID 290568, 16 pages, 2013.
- R. Garzon, G. A. Calin, and C. M. Croce, “MicroRNAs in cancer,” Annual Review of Medicine, vol. 60, pp. 167–179, 2009.
- J. Lu, G. Getz, E. A. Miska et al., “MicroRNA expression profiles classify human cancers,” Nature, vol. 435, no. 7043, pp. 834–838, 2005.
- G. A. Calin and C. M. Croce, “MicroRNA signatures in human cancers,” Nature Reviews Cancer, vol. 6, no. 11, pp. 857–866, 2006.
- 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.
- 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.
- 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.
- 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.