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Regulatory network motifs and hotspots of cancer genes in a mammalian cellular signalling network

Regulatory network motifs and hotspots of cancer genes in a mammalian cellular signalling network

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Mutations or overexpression of signalling genes can result in cancer development and metastasis. In this study, we manually assembled a human cellular signalling network and developed a robust bioinformatics strategy for extracting cancer-associated single nucleotide polymorphisms (SNPs) using expressed sequence tags (ESTs). We then investigated the relationships of cancer-associated genes [cancer-associated SNP genes, known as cancer genes (CG) and cell mobility genes (CMGs)] in a signalling network context. Through a graph-theory-based analysis, we found that CGs are significantly enriched in network hub proteins and cancer-associated genes are significantly enriched or depleted in some particular network motif types. Furthermore, we identified a substantial number of hotspots, the three- and four-node network motifs in which all nodes are either CGs or CMGs. More importantly, we uncovered that CGs are enriched in the convergent target nodes of most network motifs, although CMGs are enriched in the source nodes of most motifs. These results have implications for the foundations of the regulatory mechanisms of cancer development and metastasis.

References

    1. 1)
      • S. Mangan , U. Alon . Structure and function of the feed-forward loop network motif. Proc. Natl. Acad. Sci. USA , 21 , 11980 - 11985
    2. 2)
      • R. Milo , S. Shen-Orr , S. Itzkovitz , N. Kashtan , D. Chklovskii , U. Alon . Network motifs: simple building blocks of complex networks. Science , 5594 , 824 - 827
    3. 3)
      • S.F. Altschul , T.L. Madden , A.A. Schaffer , J. Zhang , Z. Zhang , W. Miller . Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. , 17 , 3389 - 3402
    4. 4)
      • R.D. Blitzer , J.H. Connor , G.P. Brown , T. Wong , S. Shenolikar , R. Iyengar . Gating of CaMKII by cAMP-regulated protein phosphatase activity during LTP. Science , 5371 , 1940 - 1943
    5. 5)
      • E. Oikonomou , A. Pintzas . Cancer genetics of sporadic colorectal cancer: BRAF and PI3KCA mutations, their impact on signaling and novel targeted therapies. Anticancer Res. , 1077 - 1084
    6. 6)
      • S. Mangan , A. Zaslaver , U. Alon . The coherent feedforward loop serves as a sign-sensitive delay element in transcription networks. J. Mol. Biol. , 2 , 197 - 204
    7. 7)
      • E. Wang , E. Purisima . Network motifs are enriched with transcription factors whose transcripts have short half-lives. Trends Genet. , 492 - 495
    8. 8)
      • C.S. Collins , J. Hong , L. Sapinoso , Y. Zhou , Z. Liu , K. Micklash . A small interfering RNA screen for modulators of tumor cell motility identifies MAP4K4 as a promigratory kinase. Proc. Natl. Acad. Sci. USA , 10 , 3775 - 3780
    9. 9)
      • J. Downward . Cancer biology: signatures guide drug choice. Nature , 7074 , 274 - 275
    10. 10)
      • D. Hanahan , R.A. Weinberg . The hallmarks of cancer. Cell , 1 , 57 - 70
    11. 11)
      • G.A. Calin , C.M. Croce . MicroRNA-cancer connection: the beginning of a new tale. Cancer Res. , 15 , 7390 - 7394
    12. 12)
      • J.D. Han , N. Bertin , T. Hao , D.S. Goldberg , G.F. Berriz , L.V. Zhang , D. Dupuy , A.J.M. Walhout , M.E. Cusick , F.P. Roth , M. Vidal . Evidence for dynamically organized modularity in the yeast protein-protein interaction network. Nature , 6995 , 88 - 93
    13. 13)
      • G. Balazsi , A.L. Barabasi , Z.N. Oltvai . Topological units of environmental signal processing in the transcriptional regulatory network of Escherichia coli. Proc. Natl. Acad. Sci. USA , 22 , 7841 - 7846
    14. 14)
      • D.K. Broderick , Di C , T.J. Parrett , Y.R. Samuels , J.M. Cummins , R.E. McLendon . Mutations of PIK3CA in anaplastic oligodendrogliomas, high-grade astrocytomas, and medulloblastomas. Cancer Res. , 15 , 5048 - 5050
    15. 15)
      • P. Qiu , L. Wang , M. Kostich , W. Ding , J.S. Simon , J.R. Greene . Genome wide in silico SNP-tumor association analysis. BMC Cancer
    16. 16)
      • N.G. Iyer , H. Ozdag , C. Caldas . p300/CBP and cancer. Oncogene , 24 , 4225 - 4231
    17. 17)
      • N.G. Iyer , S.F. Chin , H. Ozdag , Y. Daigo , D.E. Hu , M. Cariati . p300 regulates p53-dependent apoptosis after DNA damage in colorectal cancer cells by modulation of PUMA/p21 levels. Proc. Natl. Acad. Sci. USA , 19 , 7386 - 7391
    18. 18)
      • K.E. Bachman , P. Argani , Y. Samuels . The PIK3CA gene is mutated with high frequency in human breast cancers. Cancer Biol. Ther. , 8 , 772 - 775
    19. 19)
      • C. Ferrer-Costa , J.L. Gelpi , L. Zamakola , I. Parraga , X. de lC , M. Orozco . PMUT: a web-based tool for the annotation of pathological mutations on proteins. Bioinformatics , 14 , 3176 - 3178
    20. 20)
      • Y. Samuels , V.E. Velculescu . Oncogenic mutations of PIK3CA in human cancers. Cell Cycle , 10 , 1221 - 1224
    21. 21)
      • U.S. Bhalla , P.T. Ram , R. Iyengar . MAP kinase phosphatase as a locus of flexibility in a mitogen-activated protein kinase signaling network. Science , 5583 , 1018 - 1023
    22. 22)
      • P. Rodriguez-Viciana , O. Tetsu , K. Oda , J. Okada , K. Rauen , F. McCormick . Cancer targets in the Ras pathway. Cold Spring Harb. Symp. Quant. Biol. , 461 - 467
    23. 23)
      • A. Ma'ayan , S.L. Jenkins , S. Neves . Formation of regulatory patterns during signal propagation in a mammalian cellular network. Science , 1078 - 1083
    24. 24)
      • E. Huang , S. Ishida , J. Pittman , H. Dressman , A. Bild , M. Kloos . Gene expression phenotypic models that predict the activity of oncogenic pathways. Nat. Genet. , 2 , 226 - 230
    25. 25)
      • F. Toledo , G.M. Wahl . Regulating the p53 pathway: in vitro hypotheses, in vivo veritas. Nat. Rev. Cancer , 12 , 909 - 923
    26. 26)
      • Y. Samuels , Z. Wang , A. Bardelli , N. Silliman , J. Ptak , S. Szabo . High frequency of mutations of the PIK3CA gene in human cancers. Science , 5670
    27. 27)
      • L.V. Zhang , O.D. King , S.L. Wong , D.S. Goldberg , A.H.Y. Tong , G. Lesage , B. Andrews , H. Bussey , C. Boone , F.P. Roth . Motifs, themes and thematic maps of an integrated Saccharomyces cerevisiae interaction network. J. Biol. , 2
    28. 28)
      • D. Angeli , J.E. Ferrell , E.D. Sontag . Detection of multistability, bifurcations, and hysteresis in a large class of biological positive-feedback systems. Proc. Natl. Acad. Sci. USA , 7 , 1822 - 1827
    29. 29)
      • A.H. Bild , G. Yao , J.T. Chang , Q. Wang , A. Potti , D. Chasse . Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature , 7074 , 353 - 357
    30. 30)
      • G.S. Martin . Cell signaling and cancer. Cancer Cell , 3 , 167 - 174
    31. 31)
      • A. Bardelli , V.E. Velculescu . Mutational analysis of gene families in human cancer. Curr. Opin. Genet. Dev. , 1 , 5 - 12
    32. 32)
      • P. Stephens , S. Edkins , H. Davies , C. Greenman , C. Cox , C. Hunter . A screen of the complete protein kinase gene family identifies diverse patterns of somatic mutations in human breast cancer. Nat. Genet. , 6 , 590 - 592
    33. 33)
      • Q. Cui , Z. Yu , E.O. Purisima , E. Wang . Principles of microRNA regulation of a human cellular signaling network. Mol. Syst. Biol.
    34. 34)
      • R. Bianco , D. Melisi , F. Ciardiello , G. Tortora . Key cancer cell signal transduction pathways as therapeutic targets. Eur. J. Cancer , 3 , 290 - 294
    35. 35)
      • N.M. Luscombe , M. Madan Babu , H. Yu , M. Snyder , S.A. Teichmann , M. Gerstein . Genomic analysis of regulatory network dynamics reveals large topological changes. Nature , 7006 , 308 - 312
    36. 36)
      • N.I. Kashtan , S. Itzkovitz , R. Milo , U. Alon . Efficient sampling algorithm for estimating subgraph concentrations and detecting network motifs. Bioinformatics , 11 , 1746 - 1758
    37. 37)
      • A.A. Prinz , D. Bucher , E. Marder . Similar network activity from disparate circuit parameters. Nat. Neurosci. , 12 , 1345 - 1352
    38. 38)
      • G.L. Bond , W. Hu , A. Levine . A single nucleotide polymorphism in the MDM2 gene: from a molecular and cellular explanation to clinical effect. Cancer Res. , 13 , 5481 - 5484
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