Inferring Disease-Related Domain Using Network-Based Method

  • Zhongwen Zhang
  • Peng Chen
  • Jun Zhang
  • Bing WangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9771)


Domain-domain interaction (DDI) network analysis has been widely applied in the investigation of the mechanisms of diseases. This project focus on mapping the melanoma-related mutations to domain and domain-domain interaction (DDI) level and use potential correlation method to find the human domains that have never been found to have anything to do with the melanoma-related study. Firstly, we extract melanoma-related mutations from COSMIC database, then map the mutations to protein database UniProt and domain database Pfam to find the melanoma-related domains; Secondly, we get the melanoma-related DDI information and the human DDI information from the DDI database iPfam; Thirdly, we construct the melanoma-related DDI network and human DDI network and then combine two approaches to use potential correlation method to analyze the two DDI networks. Finally, we find that among all the human domains who have potential correlation relationship with melanoma, there are 27 human domains that have never been found to have anything to do with melanoma in the existing literature and study. The result shows the effectiveness of our method and further study based on these human domains may lead to some new methods to cure melanoma.


DDI Melanoma Potential correlation method 



This work was supported by the National Science Foundation of China (Nos. 61472282, 61300058 and 61271098) and Anhui Provincial Natural Science Foundation (No.1508085MF129).


  1. 1.
    Siegel, R., Naishadham, D., Jemal, A.: Cancer statistics, 2013. CA Cancer J. Clin. 63, 11–30 (2013)CrossRefGoogle Scholar
  2. 2.
    Dutton-Regester, K., Hayward, N.: Reviewing the somatic genetics of melanoma: from current to future analytical approaches. Pigment Cell Melanoma Res. 25, 144–154 (2012)CrossRefGoogle Scholar
  3. 3.
    Meyerson, M., Gabriel, S., Getz, G.: Advances in understanding cancer genomes through second-generation sequencing. Nat. Rev. Genet. 11(10), 685–696 (2010)CrossRefGoogle Scholar
  4. 4.
    Reis-Filho, J.S.: Next-generation sequencing. Breast Cancer Res. 11(Suppl. 3), S12 (2009)CrossRefGoogle Scholar
  5. 5.
    Lawrence, M.S., Stojanov, P., Polak, P., et al.: Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499(7457), 214–218 (2013)CrossRefGoogle Scholar
  6. 6.
    Kunz, M., Dannemann, M., Kelso, J.: High-throughput sequencing of the melanoma genome. Exp. Dermatol. 22(1), 10–17 (2013)CrossRefGoogle Scholar
  7. 7.
    Pleasance, E.D., Cheetham, R.K., Stephens, P.J., et al.: A comprehensive catalogue of somatic mutations from a human cancer genome. Nature 463(7278), 191–196 (2010)CrossRefGoogle Scholar
  8. 8.
    Wei, X., Walia, V., Lin, J.C., et al.: Exome sequencing identifies GRIN2A as frequently mutated in melanoma. Nat. Genet. 43(5), 442–446 (2011)CrossRefGoogle Scholar
  9. 9.
    Yokoyama, S., Woods, S.L., Boyle, G.M., et al.: A novel recurrent mutation in MITF predisposes to familial and sporadic melanoma. Nature 480(7375), 99–103 (2011)CrossRefGoogle Scholar
  10. 10.
    Berger, M.F., Hodis, E., Heffernan, T.P., et al.: Melanoma genome sequencing reveals frequent PREX2 mutations. Nature 485(7399), 502–506 (2012)Google Scholar
  11. 11.
    Wang, B., Sun, W., Zhang, J., et al.: Current status of machine learning-based methods for identifying protein-protein interaction sites. Curr. Bioinform. 8(2), 177–182 (2013)CrossRefGoogle Scholar
  12. 12.
    Zhu, L., You, Z.H., Huang, D.S., et al.: t-LSE: a novel robust geometric approach for modeling protein-protein interaction networks. PLoS ONE 8(4), e58368 (2013)CrossRefGoogle Scholar
  13. 13.
    Krogan, N.J., Cagney, G., Yu, H., et al.: Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature 440(7084), 637–643 (2006)CrossRefGoogle Scholar
  14. 14.
    Forbes, S.A., Beare, D., Gunasekaran, P., et al.: COSMIC: exploring the world’s knowledge of somatic mutations in human cancer. Nucleic Acids Res. 43(D1), D805–D811 (2015)CrossRefGoogle Scholar
  15. 15.
    Finn R.D, Bateman, A., Clements, J., et al.: Pfam: the protein families database. Nucleic acids research, 2013: gkt1223Google Scholar
  16. 16.
    UniProt Consortium and others. UniProt: a hub for protein information. Nucleic Acids Res. gku989 (2014)Google Scholar
  17. 17.
    Finn, R.D., Miller, B.L., Clements, J., et al.: iPfam: a database of protein family and domain interactions found in the Protein Data Bank. Nucleic Acids Res. 42(D1), D364–D373 (2014)CrossRefGoogle Scholar
  18. 18.
    Shannon, P., Markiel, A., Ozier, O., et al.: Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13(11), 2498–2504 (2003)CrossRefGoogle Scholar
  19. 19.
    Scardoni, G., Tosadori, G., Faizan, M., Spoto, F., Fabbri, F., Laudanna, C.: Biological network analysis with CentiScaPe: centralities and experimental dataset integration[J]. F1000Res. 3 (2014)Google Scholar
  20. 20.
    Monji, M., Senju, S., Nakatsura, T., et al.: Head and neck cancer antigens recognized by the humoral immune system. Biochem. Biophys. Res. Commun. 294(3), 734–741 (2002)CrossRefGoogle Scholar
  21. 21.
    Das, A., et al.: Role of voltage-gated T-type calcium channels in the viability of human melanoma. Universitat de Lleida (2012)Google Scholar
  22. 22.
    Titz, B., Low, T., Komisopoulou, E., et al.: The proximal signaling network of the BCR-ABL1 oncogene shows a modular organization. Oncogene 29(44), 5895–5910 (2010)CrossRefGoogle Scholar
  23. 23.
    Williams, K.C., McNeilly, R.E., Coppolino, M.G.: SNAP23, Syntaxin4, and vesicle-associated membrane protein 7 (VAMP7) mediate trafficking of membrane type 1–matrix metalloproteinase (MT1-MMP) during invadopodium formation and tumor cell invasion. Mol. Biol. Cell 25(13), 2061–2070 (2014)CrossRefGoogle Scholar
  24. 24.
    Huang, C.M., Elmets, C.A., van Kampen, K.R., et al.: Prospective highlights of functional skin proteomics. Mass Spectrom. Rev. 24(5), 647–660 (2005)CrossRefGoogle Scholar
  25. 25.
    Wulff, H., Castle, N.A., Pardo, L.A.: Voltage-gated potassium channels as therapeutic targets. Nat. Rev. Drug Discov. 8(12), 982–1001 (2009)CrossRefGoogle Scholar
  26. 26.
    Bedi, U.: Regulation of H2B monoubiquitination pathway in breast cancer. Niedersächsische Staats-und Universitätsbibliothek Göttingen (2014)Google Scholar
  27. 27.
    Huang, X.P., Zhao, C.X., Li, Q.J., et al.: Alteration of RPL14 in squamous cell carcinomas and preneoplastic lesions of the esophagus. Gene 366(1), 161–168 (2006)CrossRefGoogle Scholar
  28. 28.
    Allen-Vercoe, E., Holt, R., Moore, R., et al.: Detection of fusobacterium in a gastrointestinal sample to diagnose gastrointestinal cancer: U.S. Patent Application 13/877,421. 2011-10-4Google Scholar
  29. 29.
    Amsterdam, A., Sadler, K.C., Lai, K., et al.: Many ribosomal protein genes are cancer genes in zebrafish. PLoS Biol. 2(5), e139 (2004)CrossRefGoogle Scholar
  30. 30.
    Gay, N.J., Gangloff, M.: Structure and function of Toll receptors and their ligands. Annu. Rev. Biochem. 76, 141–165 (2007)CrossRefGoogle Scholar
  31. 31.
    Jain, A.K., Jain, S., Rana, A.C.: Metabolic enzyme considerations in cancer therapy. Malays. J. Med. Sci.: MJMS 14(1), 10 (2007)Google Scholar
  32. 32.
    Zhang, X., Wang, W., Wang, H., et al.: Identification of ribosomal protein S25 (RPS25)–MDM2-p53 regulatory feedback loop. Oncogene 32(22), 2782–2791 (2013)CrossRefGoogle Scholar
  33. 33.
    McGill, G.G., Horstmann, M., Widlund, H.R., et al.: Bcl2 regulation by the melanocyte master regulator Mitf modulates lineage survival and melanoma cell viability. Cell 109(6), 707–718 (2002)CrossRefGoogle Scholar
  34. 34.
    Sennoune, S.R., Luo, D., Martínez-Zaguilán, R.: Plasmalemmal vacuolar-type H+-ATPase in cancer biology. Cell Biochem. Biophys. 40(2), 185–206 (2004)CrossRefGoogle Scholar
  35. 35.
    Gunawardhana, S., Zins, K., Lucas, T., et al.: Novel CSF-1 receptor ligand IL-34 modulates macrophage-breast cancer cell crosstalk. Cancer Res. 74(19 Suppl.), 1160 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Zhongwen Zhang
    • 1
    • 2
    • 3
  • Peng Chen
    • 4
  • Jun Zhang
    • 5
  • Bing Wang
    • 1
    • 2
    • 3
    Email author
  1. 1.School of Electronics and Information EngineeringTongji UniversityShanghaiChina
  2. 2.The Advanced Research Institute of Intelligent Sensing NetworkTongji UniversityShanghaiChina
  3. 3.The Key Laboratory of Embedded System and Service ComputingTongji UniversityShanghaiChina
  4. 4.Institute of Health SciencesAnhui UniversityHefeiChina
  5. 5.College of Electrical Engineering and AutomationAnhui UniversityHefeiChina

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