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The Scientific World Journal
Volume 2014, Article ID 362141, 13 pages
http://dx.doi.org/10.1155/2014/362141
Research Article

RRHGE: A Novel Approach to Classify the Estrogen Receptor Based Breast Cancer Subtypes

School of Information Technology, Deakin University, 221 Burwood Highway, Melbourne, VIC 3125, Australia

Received 5 November 2013; Accepted 11 December 2013; Published 19 January 2014

Academic Editors: P. Chong and P. Van Dam

Copyright © 2014 Ashish Saini 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. A. P. Gasch, P. T. Spellman, C. M. Kao et al., “Genomic expression programs in the response of yeast cells to environmental changes,” Molecular Biology of the Cell, vol. 11, no. 12, pp. 4241–4257, 2000. View at Google Scholar · View at Scopus
  2. T. Ito, K. Tashiro, S. Muta et al., “Toward a protein-protein interaction map of the budding yeast: a comprehensive system to examine two-hybrid interactions in all possible combinations between the yeast proteins,” Proceedings of the National Academy of Sciences of the United States of America, vol. 97, no. 3, pp. 1143–1147, 2000. View at Publisher · View at Google Scholar · View at Scopus
  3. P. Uetz, L. Glot, G. Cagney et al., “A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae,” Nature, vol. 403, no. 6770, pp. 623–627, 2000. View at Publisher · View at Google Scholar · View at Scopus
  4. M. Ashburner, C. A. Ball, J. A. Blake et al., “Gene ontology: tool for the unification of biology,” Nature Genetics, vol. 25, no. 1, pp. 25–29, 2000. View at Publisher · View at Google Scholar · View at Scopus
  5. M. Kanehisa, S. Goto, Y. Sato, M. Furumichi, and M. Tanabe, “KEGG for integration and interpretation of large-scale molecular data sets,” Nucleic Acids Research, vol. 40, pp. D109–D114, 2012. View at Google Scholar
  6. Y. Gao and G. Church, “Improving molecular cancer class discovery through sparse non-negative matrix factorization,” Bioinformatics, vol. 21, no. 21, pp. 3970–3975, 2005. View at Publisher · View at Google Scholar · View at Scopus
  7. M. Garcia, R. Millat-carus, F. Bertucci, P. Finetti, D. Birnbaum, and G. Bidaut, “Interactome-transcriptome integration for predicting distant metastasis in breast cancer,” Bioinformatics, vol. 28, no. 5, pp. 672–678, 2012. View at Publisher · View at Google Scholar · View at Scopus
  8. C. Sotiriou, P. Wirapati, S. Loi et al., “Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis,” Journal of the National Cancer Institute, vol. 98, no. 4, pp. 262–272, 2006. View at Publisher · View at Google Scholar · View at Scopus
  9. Y. Cun and H. F. Fröhlich, “Prognostic gene signatures for patient stratification in breast cancer - accuracy, stability and interpretability of gene selection approaches using prior knowledge on protein-protein interactions,” BMC Bioinformatics, vol. 13, article 69, 2012. View at Google Scholar
  10. B. Haibe-Kains, C. Desmedt, S. Loi et al., “A three-gene model to robustly identify breast cancer molecular subtypes,” Journal of the National Cancer Institute, vol. 104, no. 4, pp. 311–325, 2012. View at Publisher · View at Google Scholar · View at Scopus
  11. M. J. van de Vijver, Y. D. He, L. J. Van 'T Veer et al., “A gene-expression signature as a predictor of survival in breast cancer,” The New England Journal of Medicine, vol. 347, no. 25, pp. 1999–2009, 2002. View at Publisher · View at Google Scholar · View at Scopus
  12. Y. Wang, J. G. M. Klijn, Y. Zhang et al., “Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer,” The Lancet, vol. 365, no. 9460, pp. 671–679, 2005. View at Publisher · View at Google Scholar · View at Scopus
  13. S. Michiels, S. Koscielny, and C. Hill, “Prediction of cancer outcome with microarrays: a multiple random validation strategy,” The Lancet, vol. 365, no. 9458, pp. 488–492, 2005. View at Publisher · View at Google Scholar · View at Scopus
  14. H.-Y. Chuang, E. Lee, Y.-T. Liu, D. Lee, and T. Ideker, “Network-based classification of breast cancer metastasis,” Molecular Systems Biology, vol. 3, article 140, 2007. View at Publisher · View at Google Scholar · View at Scopus
  15. R. Gill, S. Datta, and S. Datta, “A statistical framework for differential network analysis from microarray data,” BMC Bioinformatics, vol. 11, article 95, 2010. View at Publisher · View at Google Scholar · View at Scopus
  16. I. Xenarios, D. W. Rice, L. Salwinski, M. K. Baron, E. M. Marcotte, and D. Eisenberg, “DIP: the database of interacting proteins,” Nucleic Acids Research, vol. 28, no. 1, pp. 289–291, 2000. View at Google Scholar · View at Scopus
  17. T. S. Keshava Prasad, R. Goel, K. Kandasamy et al., “Human protein reference database—2009 update,” Nucleic Acids Research, vol. 37, no. 1, pp. D767–D772, 2009. View at Publisher · View at Google Scholar · View at Scopus
  18. C. M. Deane, Ł. Salwiński, I. Xenarios, and D. Eisenberg, “Protein interactions: two methods for assessment of the reliability of high throughput observations,” Molecular & Cellular Proteomics, vol. 1, no. 5, pp. 349–356, 2002. View at Google Scholar · View at Scopus
  19. A. Saini, J. Hou, and W. Zhou, “Hub-based reliable gene expression algorithm to classify ER+ and ER- breast cancer subtypes,” International Journal of Bioscience, Biochemistry and Bioinformatics, vol. 3, pp. 20–26, 2013. View at Publisher · View at Google Scholar
  20. C. Desmedt, B. Haibe-Kains, P. Wirapati et al., “Biological processes associated with breast cancer clinical outcome depend on the molecular subtypes,” Clinical Cancer Research, vol. 14, no. 16, pp. 5158–5165, 2008. View at Publisher · View at Google Scholar · View at Scopus
  21. S. Loi, B. Haibe-Kains, C. Desmedt et al., “Predicting prognosis using molecular profiling in estrogen receptor-positive breast cancer treated with tamoxifen,” BMC Genomics, vol. 9, article 239, 2008. View at Publisher · View at Google Scholar · View at Scopus
  22. R. Sabatier, P. Finetti, N. Cervera et al., “A gene expression signature identifies two prognostic subgroups of basal breast cancer,” Breast Cancer Research and Treatment, vol. 126, no. 2, pp. 407–420, 2011. View at Publisher · View at Google Scholar · View at Scopus
  23. M. Schmidt, D. Böhm, C. von Törne et al., “The humoral immune system has a key prognostic impact in node-negative breast cancer,” Cancer Research, vol. 68, no. 13, pp. 5405–5413, 2008. View at Google Scholar
  24. T. Barrett, D. B. Troup, S. E. Wilhite et al., “NCBI GEO: archive for high-throughput functional genomic data,” Nucleic Acids Research, vol. 37, no. 1, pp. D885–D890, 2009. View at Publisher · View at Google Scholar · View at Scopus
  25. F. Reyal, N. Stransky, I. Bernard-Pierrot et al., “Visualizing chromosomes as transcriptome correlation maps: evidence of chromosomal domains containing co-expressed genes—a study of 130 invasive ductal breast carcinomas,” Cancer Research, vol. 65, no. 4, pp. 1376–1383, 2005. View at Publisher · View at Google Scholar · View at Scopus
  26. C. Stark, B.-J. Breitkreutz, A. Chatr-Aryamontri et al., “The BioGRID interaction database: 2011 update,” Nucleic Acids Research, vol. 39, no. 1, pp. D698–D704, 2011. View at Publisher · View at Google Scholar · View at Scopus
  27. S. Kerrien, B. Aranda, L. Breuza et al., “The IntAct molecular interaction database in 2012,” Nucleic Acids Research, vol. 40, pp. D841–D846, 2012. View at Google Scholar
  28. L. Licata, L. Briganti, D. Peluso et al., “MINT, the molecular interaction database: 2012 update,” Nucleic Acids Research, vol. 40, pp. D857–D861, 2011. View at Google Scholar
  29. G. D. Bader, D. Betel, and C. W. V. Hogue, “BIND: the biomolecular interaction network database,” Nucleic Acids Research, vol. 31, no. 1, pp. 248–250, 2003. View at Publisher · View at Google Scholar · View at Scopus
  30. The Universal Protein Resource (UniProt), Nucleic Acids Research, vol. 35, pp. D193–D197, 2007.
  31. R Development Core Team, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, 2008.
  32. A. Saini and J. Hou, “Progressive clustering based method for protein function prediction,” Bulletin of Mathematical Biology, vol. 75, no. 2, pp. 331–350, 2013. View at Google Scholar
  33. R. Saito, H. Suzuki, and Y. Hayashizaki, “Interaction generality, a measurement to assess the reliability of a protein-protein interaction,” Nucleic Acids Research, vol. 30, no. 5, pp. 1163–1168, 2002. View at Google Scholar · View at Scopus
  34. P. Shannon, A. Markiel, O. Ozier et al., “Cytoscape: a software environment for integrated models of biomolecular interaction networks,” Genome Research, vol. 13, no. 11, pp. 2498–2504, 2003. View at Publisher · View at Google Scholar · View at Scopus
  35. X. He and J. Zhang, “Why do hubs tend to be essential in protein networks?” PLoS Genetics, vol. 2, no. 6, article e88, 2006. View at Publisher · View at Google Scholar · View at Scopus
  36. W. Chang, L. Ma, L. Lin et al., “Identification of novel hub genes associated with liver metastasis of gastric cancer,” International Journal of Cancer, vol. 125, no. 12, pp. 2844–2853, 2009. View at Publisher · View at Google Scholar · View at Scopus
  37. P. F. Jonsson and P. A. Bates, “Global topological features of cancer proteins in the human interactome,” Bioinformatics, vol. 22, no. 18, pp. 2291–2297, 2006. View at Publisher · View at Google Scholar · View at Scopus
  38. P. Dao, K. Wang, C. Collins, M. Ester, A. Lapuk, and S. C. Sahinalp, “Optimally discriminative subnetwork markers predict response to chemotherapy,” Bioinformatics, vol. 27, no. 13, pp. i205–i213, 2011. View at Publisher · View at Google Scholar · View at Scopus
  39. B. W. Matthews, “Comparison of the predicted and observed secondary structure of T4 phage lysozyme,” Biochimica et Biophysica Acta, vol. 405, no. 2, pp. 442–451, 1975. View at Google Scholar · View at Scopus
  40. P. Baldi, S. Brunak, Y. Chauvin, C. A. F. Andersen, and H. Nielsen, “Assessing the accuracy of prediction algorithms for classification: an overview,” Bioinformatics, vol. 16, no. 5, pp. 412–424, 2000. View at Google Scholar · View at Scopus
  41. K. Lang, H. Huang, M. Namjoshi, V. Federico, and J. Menzin, “Initial treatment and survival among elderly breast cancer patients in the United States by estrogen receptor status and cancer stage at diagnosis: an analysis of national registry data 2000–2009,” Cancer Research, vol. 72, pp. 3–7, 2012. View at Publisher · View at Google Scholar
  42. L. K. Dunnwald, M. A. Rossing, and C. I. Li, “Hormone receptor status, tumor characteristics, and prognosis: a prospective cohort of breast cancer patients,” Breast Cancer Research, vol. 9, no. 1, article R6, 2007. View at Publisher · View at Google Scholar · View at Scopus
  43. R. Liu, X. Wang, G. Y. Chen et al., “The prognostic role of a gene signature from tumorigenic breast-cancer cells,” The New England Journal of Medicine, vol. 356, no. 3, pp. 217–226, 2007. View at Publisher · View at Google Scholar · View at Scopus
  44. D. W. Huang, B. T. Sherman, and R. A. Lempicki, “Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources,” Nature Protocols, vol. 4, no. 1, pp. 44–57, 2009. View at Publisher · View at Google Scholar · View at Scopus