ABSTRACT Modeling inter-relationships of genes over a specific genetic network is one of the most challenging studies in systems biology. Among the families of models proposed one commonly used is the discrete stochastic, based on conditionally independent Markov chains. In practice, this model is estimated from time sequential sampling, usually obtained by microarray experiments. In order to improve the accuracy of the estimation method, we can use biological knowledge. In this paper, we decided to apply this idea to study the role of estrogen in breast cancer proliferation. The n-influence zone of a set S of genes in a given multi-layer genetic network is a set L of genes regulated, directly or indirectly, by genes in S, after at most n-1 layers. In this manuscript we describe a new approach for computing the n-influence zone of S through the estimation of a multi-layer genetic network from gene expression time series, measured by microarrays, and biological knowledge. Using seed genes related to cell proliferation, our method was able to add to the third layer of the network other genes related to this biological function and validated in the literature. Using a set of genes directly influenced by estrogen, we could find a new role for cell adhesion genes estrogen dependent. Our pipeline is user-friendly and does not have high system requirements. We believe this paper could contribute to improve the data mining for biologists in microarray time series.
Cite this paper
Lima, L., Ris, M., Barrera, J., Brentani, M. and Brentani, H. (2012) Computing a Predictor Set Influence Zone through a Multi-Layer Genetic Network to Explore the Role of Estrogen in Breast Cancer. Advances in Breast Cancer Research, 1, 21-29. doi: 10.4236/abcr.2012.13004.
 M. Schena, “DNA Microarrays: A Practical Approach,” Oxford University Press, Oxford, 1999.
 S. Marguerat and J. B?hler, “Rna-seq: From Technology to Biology,” Cellular and Molecular Life Sciences, Vol. 67, No. 4, 2010, pp. 569-579.
 M. Schena, D. Shalon, R. Davis and P. O. Brown, “Quantitative Monitoring of Gene Expression Patterns with a Complementary DNA Microarray,” Science, Vol. 270, No. 5235, 1995, pp. 467-470.
 S. Peddada, E. Lobenhofer, L. Li, C. Afshari, C. Weinberg and D. M. Umbach, “Gene Selection and Clustering for Time-Course and Dose-Response Microarray Experiments Using Order-Restricted Inference,” Science, Vol. 19, No. 7, 2003, pp. 834-841.
 R. F. Hashimoto, S. Kim, I. Shmulevich, W. Zhang, M. L. Bittner and E. R. Dougherty, “Growing Genetic Regulatory Networks from Seed Genes,” Bioinformatics, Vol. 20, No. 8, 2004, pp. 1241-1247.
 M. Bansal, V. Belcastro, A. Ambesi-Impiombato and D. di Bernardo, “How to Infer Gene Networks from Expression Profiles,” Molecular Systems Biology, Vol. 3, No. 78, 2007, pp. 1-10.
 H. D. Jong, “Modeling and Simulation of Genetic Regulatory Systems: A Literature Review,” Journal of Computational Biology, Vol. 9, No. 1, 2002, pp. 67-103.
 I. Shmulevich and E. R. Dougherty, “Genomic Signal Processing,” Princeton University Press, Princeton, 2007.
 C. Sima, J. Hua and S. Jung, “Inference of Gene Regulatory Networks Using Time-Series Data: A Survey,” Current Genomic, Vol. 10, No. 6, 2009, pp. 416-429.
 E. Segal, M. Shapira, A. Regev, D. Pe’er, D. Botstein, D. Koller and N. Friedman, “Module Networks: Identifying Regulatory Modules and Their Condition-Specific Regulators from Gene Expression Data,” Nature Genetics, Vol. 34, No. 2, 2003, pp. 166-176.
 S. A. Kauffman, “The Origins of Order, Self-Organiza- tion and Selection in Evolution,” Oxford University Press, Oxford, 1993, pp. 441-520.
 I. Shmulevich, E. R. Dougherty, S. Kim and W. Zhang, “Probabilistic Boolean Networks: A Rule-Based Uncertainty,” Bioinformatics, Vol. 18, No. 2, 2002, pp. 261- 274.
 J. Barrera, R. M. Cesar-Jr., D. C. Martins-Jr., E. F. Merino, R. Z. N. Vêncio, F. G. Leonardi, M. M. Yamamoto, C. A. B. Pereira and H. A. Portillo, “A New Annotation Tool for Malaria Based on Inference of Probabilistic Genetic Networks,” In CAMDA, 2004.
 P J. M. e?a, J. Bj?rkegren and J. Tegnér, “Growing Bayesian Network Models of Gene Networks from Seed Genes,” Bioinformatics, Vol. 21, Suppl. 2, 2005, pp. ii224-ii229.
 X. Xu, L. Wang and D. Ding, “Learning Module Networks from Genome-Wide Location and Expression Data,” FEBS Letters, Vol. 578, No. 3, 2004, pp. 297-304.
 B. T. Zhu and A. H. Conney, “Functional Role of Estrogen Metabolism in Target Cells: Review and Perspectives,” Carcinogenesis, Vol. 19, No. 1, 1998, pp. 1-27.
 C. F?rster, S. M?kela, A. W?rri, S. Kietz, D. Becker, K. Hultenby, M. Warner and J. Gustafsson, “Involvement of Estrogen Receptor β in Terminal Differentiation of Mammary Gland Epithelium,” Proceedings of the National Academy of Sciences, Vol. 99, No. 24, 2002, pp. 15578- 15583.
 K. R. Coser, J. Chesnes, J. Hur, S. Ray, K. J. Isselbacher and T. Shioda, “Global Analysis of Ligand Sensitivity of Estrogen Inducible and Suppressible Genes in mcf7/Bus Breast Cancer Cells by DNA Microarray,” Proceedings of the National Academy of Sciences, Vol. 100, No. 24, 2003, pp. 13994-13999.
 J. Frasor, J. M. Danes, B. Komm, K. C. N. Chang, C. R. Lyttle and B. S. Katzenellenbogen, “Profiling of Estrogen Up- and Down-Regulated Gene Expression in Human Breast Cancer Cells: Insights into Gene Networks and Pathways Underlying Estrogenic Control of Proliferation and Cell Phenotype,” Endocrinology, Vol. 144, No. 10, 2003, pp. 4562-4574.
 V. X. Jin, Y. W. Leu, S. Liyanarachchi, H. Sun, M. Fan, K. P. Nephew, T. H. Huang and R. V. Davuluri, “Identifying Estrogen Receptor α Target Genes Using Integrated Computational Genomics and Chromatin Immunoprecipitation Microarray,” Nucleic Acids Research, Vol. 32, No. 22, 2004, pp. 6627-6635.
 A. S. Levenson, I. L. Kliakhandler, K. M. Svoboda, K. M. Pease, S. A. Kaiser, J. E. Ward-III and V. C. Jordan, “Molecular Classification of Selective Oestrogen Receptor Modulators on the Basis of Gene Expression Profiles of Breast Cancer Cells Expressing Oestrogen Receptor α,” British Journal of Cancer, Vol. 87, No. 4, 2002, pp. 449-456.
 C. Lin, A. Str?m, V. B. Vega, S. L. Kong, A. L. Yeo, J. S. Thomsen, W. C. Chan, B. Doray, D. K. Bangarusamy, A. Ramasamy, L. A. Vergara, S. Tang, A. Chong, V. B. Bajic, L. D. Miller, J. Gustafsson and E. T. Liu, “Discovery of Estrogen Receptor α Target Genes and Response Elements in Breast Tumor Cells,” Genome Biology, Vol. 5, No. 9, 2004, pp. 1-18.
 A. Weisz, W. Basile, C. Scafoglio, L. Altucci, F. Bresciani, A. Facchiano, P. Sismondi, L. Cicatiello and M. Bortoli, “Molecular Identification of ERα-Positive Breast Cancer Cells by the Expression Profile of an Intrinsic Set of Estrogen Regulated Genes,” Journal Cellular Physiology, Vol. 200, No. 3, 2004, pp. 440-450.
 M. L. Whitfield, L. K. George, G. D. Grant and C. M. Perou, “Common Markers of Proliferation,” Nature Reviews, Vol. 6, No. 2, 2006, pp. 99-106.
 M. Ashburner, C. A. Ball, J. A. Blake, D. Botstein, H. Butler, J. M. Cherry, A. P.Davis, K. Dolinski, S. S. Dwight, J. T. Eppig, M. A. Harris, D. P. Hill, L. Issel-Tarver, A. Kasarskis, S. Lewis, J. C. Matese, J. E. Richardson, M. Ringwald, G. M. Rubin and G. Sherlock,. “Gene Ontology: Tool for the Unification of Biology,” Nature Genetics, Vol. 25, 2000, pp. 25-29.
 J. Barrera, R. M. Cesar-Jr, D. C. Martins-Jr, R. Z. N. Vêncio, E. F. Merino, M. M. Yamamoto, F. G. Leonardi, C. A. B. Pereira and H. A. del Portillo, “Constructing Probabilistic Genetic Networks of Plasmodium Falciparum from Dynamical Expression Signals of the Intraerythrocytic Development Cycle, Chapter 2,” Springer, Berlin, 2006, pp. 11-26.
 E. Prifti, J.-D. Zucker, K. Clement and C. Henegar, “Funnet: An Integrative Tool for Exploring Transcriptional Interactions,” Bioinformatics, Vol. 24, No. 22, 2008, pp. 2636-2638.
 S. Hirohashi and Y. Kanai, “Cell Adhesion System and Human Cancer Morphogenesis,” Cancer Science, Vol. 94, No. 7, 2003, pp. 575-581.
 H. Jeong, S. P. Mason, A.-L. Barabasi and Z. N. Oltvai, “Lethality and Centrality in Protein Networks,” Nature, Vol. 411, No. 6833, 2001, pp. 41-42.
 J. I. Fidler, “Origin and Biology of Cancer Metastasis,” Cytometry, Vol. 10, No. 6, 1989, pp. 673-680.
 D. R. Coman, “Adhesiveness and Stickiness: Two Independent Properties of the Cell Surface,” Cancer Research, Vol. 1, 1961, pp. 1436-1438.
 R. O. Hynes, “Integrins: Versatility, Modulation, and Signaling in Cell Adhesion,” Cell, Vol. 69, No. 1, 1992, pp. 11-25.
 H. Oka, H. Shiozaki, K. Kobayashi, M. Inoue, H. Tahara, T. Kobayashi, Y. Takatsuka, N. Matsuyoshi, S. Mirano, M. Takeichi and T. Mori, “Expression of E-Cadherin Cell Adhesion Molecules in Human Breast Cancer Tissues and Its Relationship to Metastasis,” Cancer Research, Vol. 53, No. 7, 1993, pp.1696-1701.
 J. Helleman, M. P. Jansen, K. Ruigrok-Ritstier, I. L. van Staveren, M. P. Look, M. E. M. van Gelder, A. M. Sieuwerts, J. G. Klijn, S. Sleijfer, F J. A. oekens and E. M. Berns, “Association of an Extracellular Matrix Gene Cluster with Breast Cancer Prognosis and Endocrine Therapy Response,” Clinical Cancer Research, Vol. 14, No. 17, 2008, pp. 5555-5564.
 M. P. H. M. Jansen, K. Ruigrok-Ritstier, L. C. J. Dorssers, I. L. van Staveren, M. P. Look, M. E. M. van Gelder, A. M. Sieuwerts, J. Helleman, S. Sleijfer, J. G. M. Klijn, J. A. Foekens and E. M. J. J. Berns, “Down Regulation of Siah2, an Ubiquitin e3 Ligase, Is Associated with Resistance to Endocrine Therapy in Breast Cancer,” Breast Cancer Research and Treatment, Vol. 116, No. 2, 2009, pp. 263-271.
 G. E. Ayala, H. Dai, M. Powell, R. Li, Y. Ding, T. M. Wheeler, D. Shine, D. Kadmon, T. Thompson, B. J. Miles, M. M. Ittmann and D. Rowley, “Cancer-Related Axonogenesis and Neurogenesis in Prostate Cancer,” Clinical Cancer Research, Vol. 14, No. 23, 2008, pp. 7593-7603.
 A. Chédotal, G. Kerjan and C. Moreau-Fauvarque, “The Brain within the Tumor: New Roles for Axon Guidance Molecules in Cancers,” Cell Death Differ, Vol. 12, No. 8, 2005, pp. 1044-1056.