Evidence for systems-level molecular mechanisms of tumorigenesis

BackgroundCancer arises from the consecutive acquisition of genetic alterations. Increasing evidence suggests that as a consequence of these alterations, molecular interactions are reprogrammed in the context of highly connected and regulated cellular networks. Coordinated reprogramming would allow the cell to acquire the capabilities for malignant growth.ResultsHere, we determine the coordinated function of cancer gene products (i.e., proteins encoded by differentially expressed genes in tumors relative to healthy tissue counterparts, hereafter referred to as "CGPs") defined as their topological properties and organization in the interactome network. We show that CGPs are central to information exchange and propagation and that they are specifically organized to promote tumorigenesis. Centrality is identified by both local (degree) and global (betweenness and closeness) measures, and systematically appears in down-regulated CGPs. Up-regulated CGPs do not consistently exhibit centrality, but both types of cancer products determine the overall integrity of the network structure. In addition to centrality, down-regulated CGPs show topological association that correlates with common biological processes and pathways involved in tumorigenesis.ConclusionGiven the current limited coverage of the human interactome, this study proposes that tumorigenesis takes place in a specific and organized way at the molecular systems-level and suggests a model that comprises the precise down-regulation of groups of topologically-associated proteins involved in particular functions, orchestrated with the up-regulation of specific proteins.

[1]  R. Luce,et al.  A method of matrix analysis of group structure , 1949, Psychometrika.

[2]  G. Sabidussi.  The centrality of a graph. , 1966, Psychometrika.

[3]  Gert Sabidussi,et al.  The centrality index of a graph , 1966 .

[4]  C. Bron,et al.  Algorithm 457: finding all cliques of an undirected graph , 1973 .

[5]  Coenraad Bron,et al.  Finding all cliques of an undirected graph , 1973 .

[6]  Leonard M. Freeman,et al.  A set of measures of centrality based upon betweenness , 1977 .

[7]  Steven B. Andrews,et al.  Structural Holes: The Social Structure of Competition , 1995, The SAGE Encyclopedia of Research Design.

[8]  D. Hanahan,et al.  The Hallmarks of Cancer , 2000, Cell.

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

[10]  M. Vidal,et al.  Identification of potential interaction networks using sequence-based searches for conserved protein-protein interactions or "interologs". , 2001, Genome research.

[11]  A. Barabasi,et al.  Lethality and centrality in protein networks , 2001, Nature.

[12]  E. Lander,et al.  Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[13]  U. Alon,et al.  Transcriptional gene expression profiles of colorectal adenoma, adenocarcinoma, and normal tissue examined by oligonucleotide arrays. , 2001, Cancer research.

[14]  E. Lander,et al.  Gene expression correlates of clinical prostate cancer behavior. , 2002, Cancer cell.

[15]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

[16]  Edison T Liu,et al.  Classification of cancers by expression profiling. , 2003, Current opinion in genetics & development.

[17]  T. Speed,et al.  Summaries of Affymetrix GeneChip probe level data. , 2003, Nucleic acids research.

[18]  A. Fraser,et al.  A first-draft human protein-interaction map , 2004, Genome Biology.

[19]  J. Wang-Rodriguez,et al.  In silico dissection of cell-type-associated patterns of gene expression in prostate cancer. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

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

[21]  H. Kitano.  Cancer as a robust system: implications for anticancer therapy , 2004, Nature Reviews Cancer.

[22]  Joaquín Dopazo,et al.  FatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes , 2004, Bioinform..

[23]  A. Barabasi,et al.  Network biology: understanding the cell's functional organization , 2004, Nature Reviews Genetics.

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

[25]  D. Koller,et al.  A module map showing conditional activity of expression modules in cancer , 2004, Nature Genetics.

[26]  Hans Clevers,et al.  Signaling pathways in intestinal development and cancer. , 2004, Annual review of cell and developmental biology.

[27]  M. Gerstein,et al.  Annotation transfer between genomes: protein-protein interologs and protein-DNA regulogs. , 2004, Genome research.

[28]  T. Barrette,et al.  ONCOMINE: a cancer microarray database and integrated data-mining platform. , 2004, Neoplasia.

[29]  Massimo Marchiori,et al.  Error and attacktolerance of complex network s , 2004 .

[30]  I. Khalil,et al.  Systems biology for cancer , 2005, Current opinion in oncology.

[31]  Joaquín Dopazo,et al.  BABELOMICS: a suite of web tools for functional annotation and analysis of groups of genes in high-throughput experiments , 2005, Nucleic Acids Res..

[32]  Shinichiro Wachi,et al.  Interactome-transcriptome analysis reveals the high centrality of genes differentially expressed in lung cancer tissues , 2005, Bioinform..

[33]  C. Conti,et al.  Molecular mechanisms of protein kinase C-induced apoptosis in prostate cancer cells. , 2005, Journal of biochemistry and molecular biology.

[34]  T. Barrette,et al.  Mining for regulatory programs in the cancer transcriptome , 2005, Nature Genetics.

[35]  A. Chinnaiyan,et al.  Integrative analysis of the cancer transcriptome , 2005, Nature Genetics.

[36]  Paul A. Bates,et al.  Cluster analysis of networks generated through homology: automatic identification of important protein communities involved in cancer metastasis , 2006, BMC Bioinformatics.

[37]  Igor Jurisica,et al.  Online Predicted Human Interaction Database , 2005, Bioinform..

[38]  M. Vidal,et al.  Effect of sampling on topology predictions of protein-protein interaction networks , 2005, Nature Biotechnology.

[39]  A. Papatsoris,et al.  Novel insights into the implication of the IGF-1 network in prostate cancer. , 2005, Trends in molecular medicine.

[40]  Burkhard Rost,et al.  Protein–Protein Interactions More Conserved within Species than across Species , 2006, PLoS Comput. Biol..

[41]  Paul A. Bates,et al.  Global topological features of cancer proteins in the human interactome , 2006, Bioinform..

[42]  Kumaran Kandasamy,et al.  An evaluation of human protein-protein interaction data in the public domain , 2006, BMC Bioinformatics.

[43]  Erich E. Wanker,et al.  Comparison of Human Protein-Protein Interaction Maps , 2007, German Conference on Bioinformatics.

[44]  Joaquín Dopazo,et al.  Next station in microarray data analysis: GEPAS , 2006, Nucleic Acids Res..

[45]  Kiyoko F. Aoki-Kinoshita,et al.  From genomics to chemical genomics: new developments in KEGG , 2005, Nucleic Acids Res..

[46]  K. N. Chandrika,et al.  Analysis of the human protein interactome and comparison with yeast, worm and fly interaction datasets , 2006, Nature Genetics.