Two-layer modular analysis of gene and protein networks in breast cancer

BackgroundGenomic, proteomic and high-throughput gene expression data, when integrated, can be used to map the interaction networks between genes and proteins. Different approaches have been used to analyze these networks, especially in cancer, where mutations in biologically related genes that encode mutually interacting proteins are believed to be involved. This system of integrated networks as a whole exhibits emergent biological properties that are not obvious at the individual network level. We analyze the system in terms of modules, namely a set of densely interconnected nodes that can be further divided into submodules that are expected to participate in multiple biological activities in coordinated manner.ResultsIn the present work we construct two layers of the breast cancer network: the gene layer, where the correlation network of breast cancer genes is analyzed to identify gene modules, and the protein layer, where each gene module is extended to map out the network of expressed proteins and their interactions in order to identify submodules. Each module and its associated submodules are analyzed to test the robustness of their topological distribution. The constituent biological phenomena are explored through the use of the Gene Ontology. We thus construct a “network of networks”, and demonstrate that both the gene and protein interaction networks are modular in nature. By focusing on the ontological classification, we are able to determine the entire GO profiles that are distributed at different levels of hierarchy. Within each submodule most of the proteins are biologically correlated, and participate in groups of distinct biological activities.ConclusionsThe present approach is an effective method for discovering coherent gene modules and protein submodules. We show that this also provides a means of determining biological pathways (both novel and as well those that have been reported previously) that are related, in the present instance, to breast cancer. Similar strategies are likely to be useful in the analysis of other diseases as well.

[1]  I. Ellis,et al.  Differential oestrogen receptor binding is associated with clinical outcome in breast cancer , 2011, Nature.

[2]  B Formby,et al.  Progesterone inhibits growth and induces apoptosis in breast cancer cells: inverse effects on Bcl-2 and p53. , 1998, Annals of clinical and laboratory science.

[3]  Kelly Janis,et al.  Estrogen Decreases Expression of Chemokine Receptors, and Suppresses Chemokine Bioactivity in Murine Monocytes , 2004, American journal of reproductive immunology.

[4]  Robert Clarke,et al.  Caveolin-1 Tyrosine Phosphorylation Enhances Paclitaxel-mediated Cytotoxicity* , 2007, Journal of Biological Chemistry.

[5]  A. Barabasi,et al.  The human disease network , 2007, Proceedings of the National Academy of Sciences.

[6]  Alexander Rives,et al.  Modular organization of cellular networks , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[7]  Francesca D. Ciccarelli,et al.  Network of Cancer Genes: a web resource to analyze duplicability, orthology and network properties of cancer genes , 2009, Nucleic Acids Res..

[8]  T. Takagi,et al.  Assessment of prediction accuracy of protein function from protein–protein interaction data , 2001, Yeast.

[9]  Gábor Csárdi,et al.  The igraph software package for complex network research , 2006 .

[10]  C. Daub,et al.  BMC Systems Biology , 2007 .

[11]  L. Kaiser,et al.  Stratification of randomization is not required for a pre‐specified subgroup analysis , 2013, Pharmaceutical statistics.

[12]  Dipanwita Roy Chowdhury,et al.  Human protein reference database as a discovery resource for proteomics , 2004, Nucleic Acids Res..

[13]  M. Newman,et al.  Finding community structure in networks using the eigenvectors of matrices. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[14]  Steven P Gygi,et al.  Abraxas and RAP80 Form a BRCA1 Protein Complex Required for the DNA Damage Response , 2007, Science.

[15]  Alex Arenas,et al.  Synchronization reveals topological scales in complex networks. , 2006, Physical review letters.

[16]  Rory Stark.  Differential Oestrogen Receptor Binding is Associated with Clinical Outcome in Breast Cancer , 2012, RECOMB.

[17]  Russ B. Altman,et al.  Missing value estimation methods for DNA microarrays , 2001, Bioinform..

[18]  K. Strimmer,et al.  Statistical Applications in Genetics and Molecular Biology A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics , 2011 .

[19]  Susumu Goto,et al.  KEGG: Kyoto Encyclopedia of Genes and Genomes , 2000, Nucleic Acids Res..

[20]  A. Barabasi,et al.  Functional and topological characterization of protein interaction networks , 2004, Proteomics.

[21]  M. Newman,et al.  Finding community structure in very large networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[22]  J. Usuda,et al.  Bax is essential for mitochondrion-mediated apoptosis but not for cell death caused by photodynamic therapy , 2003, British Journal of Cancer.

[23]  Deepa Subramanyam,et al.  Notch Signaling Pathway as a Therapeutic Target in Breast Cancer , 2010, Molecular Cancer Therapeutics.

[24]  Cathy H. Wu,et al.  UniProt: the Universal Protein knowledgebase , 2004, Nucleic Acids Res..

[25]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[26]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[27]  R. Rosenfeld,et al.  Transforming Growth Factor- β-induced Cell Growth Inhibition in Human Breast Cancer Cells Is Mediated through Insulin-like Growth Factor-binding Protein-3 Action (*) , 1995, The Journal of Biological Chemistry.

[28]  Z. Szallasi,et al.  An online survival analysis tool to rapidly assess the effect of 22,277 genes on breast cancer prognosis using microarray data of 1,809 patients , 2010, Breast Cancer Research and Treatment.

[29]  J. Reichardt,et al.  Statistical mechanics of community detection. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[30]  Jyotsna Sen,et al.  Histopathologic changes following neoadjuvant chemotherapy in various malignancies , 2012, International journal of applied & basic medical research.

[31]  Q Zhou,et al.  The protein-tyrosine kinase SYK interacts with TRAF-interacting protein TRIP in breast epithelial cells , 2008, Oncogene.

[32]  R. Strausberg,et al.  The Cancer Genome Anatomy Project: new resources for reading the molecular signatures of cancer , 2001, The Journal of pathology.

[33]  R. Carter.  11 – IT and society , 1991 .

[34]  Dennis M. Wilkinson,et al.  A method for finding communities of related genes , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[35]  R. Sharan,et al.  Protein networks in disease. , 2008, Genome research.

[36]  N. Campbell.  Genetic association database , 2004, Nature Reviews Genetics.

[37]  Jiong Wu,et al.  TIEG1 Inhibits Breast Cancer Invasion and Metastasis by Inhibition of Epidermal Growth Factor Receptor (EGFR) Transcription and the EGFR Signaling Pathway , 2011, Molecular and Cellular Biology.

[38]  Guey-Mei Jow,et al.  Involvement of Smac, p53, and caspase pathways in induction of apoptosis by gossypol in human retinoblastoma cells , 2012, Molecular vision.

[39]  Andrea Lancichinetti,et al.  Detecting the overlapping and hierarchical community structure in complex networks , 2008, 0802.1218.

[40]  Mason A. Porter,et al.  Communities in Networks , 2009, ArXiv.

[41]  Santo Fortunato,et al.  Community detection in graphs , 2009, ArXiv.

[42]  Alan F. Scott,et al.  Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders , 2002, Nucleic Acids Res..

[43]  G. Sledge,et al.  Constitutive activation of NF-kappaB during progression of breast cancer to hormone-independent growth , 1997, Molecular and cellular biology.

[44]  Shuqin Zhang,et al.  Hierarchical modular structure in gene coexpression networks , 2012, 2012 IEEE 6th International Conference on Systems Biology (ISB).

[45]  P. Newcomb,et al.  Effect of insulin-like growth factor binding protein-1 on integrin signalling and the induction of apoptosis in human breast cancer cells. , 1999, Journal of molecular endocrinology.

[46]  Mason A. Porter,et al.  Community Structure in Online Collegiate Social Networks , 2008 .

[47]  Réka Albert,et al.  Near linear time algorithm to detect community structures in large-scale networks. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[48]  Hunter B. Fraser,et al.  Modularity and evolutionary constraint on proteins , 2005, Nature Genetics.

[49]  Ding Cheng Yang,et al.  Down-regulation of expression of interleukin-6 and its receptor results in growth inhibition of MCF-7 breast cancer cells. , 2011, Anticancer research.

[50]  B. Schwikowski,et al.  A network of protein–protein interactions in yeast , 2000, Nature Biotechnology.

[51]  C. Osborne,et al.  Aberrant Subcellular Localization of BRCA1 in Breast Cancer , 1995, Science.

[52]  Li Yang,et al.  Down-regulation of osteopontin expression by RNA interference affects cell proliferation and chemotherapy sensitivity of breast cancer MDA-MB-231 cells. , 2011, Molecular medicine reports.

[53]  E. Filardo,et al.  Epidermal growth factor receptor (EGFR) transactivation by estrogen via the G-protein-coupled receptor, GPR30: a novel signaling pathway with potential significance for breast cancer , 2002, The Journal of Steroid Biochemistry and Molecular Biology.

[54]  H. Hirt,et al.  Protein networking: insights into global functional organization of proteomes , 2008, Proteomics.

[55]  Emily Dimmer,et al.  The Gene Ontology Annotation (GOA) Database: sharing knowledge in Uniprot with Gene Ontology , 2004, Nucleic Acids Res..

[56]  Jan Šrámek,et al.  Caspase-2 is involved in cell death induction by taxanes in breast cancer cells , 2013, Cancer Cell International.

[57]  Matthieu Latapy,et al.  Computing Communities in Large Networks Using Random Walks , 2004, J. Graph Algorithms Appl..

[58]  E. Levin,et al.  Plasma membrane estrogen receptors signal to antiapoptosis in breast cancer. , 2000, Molecular endocrinology.

[59]  Hiroyuki Ogata,et al.  KEGG: Kyoto Encyclopedia of Genes and Genomes , 1999, Nucleic Acids Res..

[60]  R. Greenberg,et al.  MERIT40 controls BRCA1-Rap80 complex integrity and recruitment to DNA double-strand breaks. , 2009, Genes & development.

[61]  T. Hubbard,et al.  A census of human cancer genes , 2004, Nature Reviews Cancer.

[62]  M. DePamphilis,et al.  HUMAN DISEASE , 1957, The Ulster Medical Journal.

[63]  Alessandro Vespignani,et al.  Evolution thinks modular , 2003, Nature Genetics.

[64]  Sanghyun Park,et al.  Integrative gene network construction for predicting a set of complementary prostate cancer genes , 2011, Bioinform..

[65]  Amy S. Lee,et al.  GRP78/BiP inhibits endoplasmic reticulum BIK and protects human breast cancer cells against estrogen starvation-induced apoptosis. , 2007, Cancer research.

[66]  Martin Rosvall,et al.  Maps of random walks on complex networks reveal community structure , 2007, Proceedings of the National Academy of Sciences.

[67]  Z. Pan,et al.  Autocrine regulation of cell proliferation by estrogen receptor-alpha in estrogen receptor-alpha-positive breast cancer cell lines , 2009, BMC Cancer.

[68]  R. Spang,et al.  Predicting the clinical status of human breast cancer by using gene expression profiles , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[69]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[70]  R. Vigneri,et al.  High-affinity insulin binding to an atypical insulin-like growth factor-I receptor in human breast cancer cells. , 1992, The Journal of clinical investigation.

[71]  Olivier Ledoit,et al.  Improved estimation of the covariance matrix of stock returns with an application to portfolio selection , 2003 .

[72]  Lan V. Zhang,et al.  Evidence for dynamically organized modularity in the yeast protein–protein interaction network , 2004, Nature.

[73]  J. Rastetter,et al.  Effects of vinorelbine on epidermal growth factor-receptor binding of human breast cancer cell linesin vitro , 2004, Investigational New Drugs.

[74]  B. Tombal,et al.  Delayed micromolar elevation in intracellular calcium precedes induction of apoptosis in thapsigargin-treated breast cancer cells. , 2000, Clinical cancer research : an official journal of the American Association for Cancer Research.

[75]  Patricia A. Ganz,et al.  Fatigue and Proinflammatory Cytokine Activity in Breast Cancer Survivors , 2002, Psychosomatic medicine.

[76]  Alok Kumar Srivastava,et al.  OntoVisT: A general purpose Ontological Visualization Tool , 2011, Bioinformation.

[77]  David L. Steffen,et al.  The Breast Cancer Gene Database: a collaborative information resource , 1999, Oncogene.

[78]  M. Takeichi,et al.  Expression of E-cadherin cell adhesion molecules in human breast cancer tissues and its relationship to metastasis. , 1993, Cancer research.

[79]  D. Shaw,et al.  A non–RGD-based integrin binding peptide (ATN-161) blocks breast cancer growth and metastasis in vivo , 2006, Molecular Cancer Therapeutics.

[80]  G. Paine-Murrieta,et al.  Transfection with human thioredoxin increases cell proliferation and a dominant-negative mutant thioredoxin reverses the transformed phenotype of human breast cancer cells. , 1996, Cancer research.

[81]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[82]  Tobias Müller,et al.  Identifying functional modules in protein–protein interaction networks: an integrated exact approach , 2008, ISMB.

[83]  W. Wonderlin,et al.  Mitogenic signal transduction in human breast cancer cells. , 1995, General pharmacology.

[84]  Anton J. Enright,et al.  Detection of functional modules from protein interaction networks , 2003, Proteins.

[85]  Katri Pylkäs,et al.  Breast Cancer–Associated Abraxas Mutation Disrupts Nuclear Localization and DNA Damage Response Functions , 2012, Science Translational Medicine.

[86]  T. Vicsek,et al.  Uncovering the overlapping community structure of complex networks in nature and society , 2005, Nature.

[87]  M. Newman,et al.  The structure of scientific collaboration networks. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[88]  Aedín C Culhane,et al.  RAP80 Targets BRCA1 to Specific Ubiquitin Structures at DNA Damage Sites , 2007, Science.

[89]  Andreas Wagner,et al.  Alternative routes and mutational robustness in complex regulatory networks , 2007, Biosyst..