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International Conference on Intelligent Computing

ICIC 2016: Intelligent Computing Theories and Application pp 775–783Cite as

Inferring Disease-Related Domain Using Network-Based Method

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9771))

Abstract

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.

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References

  1. Siegel, R., Naishadham, D., Jemal, A.: Cancer statistics, 2013. CA Cancer J. Clin. 63, 11–30 (2013)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  3. Meyerson, M., Gabriel, S., Getz, G.: Advances in understanding cancer genomes through second-generation sequencing. Nat. Rev. Genet. 11(10), 685–696 (2010)

    Article  Google Scholar 

  4. Reis-Filho, J.S.: Next-generation sequencing. Breast Cancer Res. 11(Suppl. 3), S12 (2009)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  6. Kunz, M., Dannemann, M., Kelso, J.: High-throughput sequencing of the melanoma genome. Exp. Dermatol. 22(1), 10–17 (2013)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  15. Finn R.D, Bateman, A., Clements, J., et al.: Pfam: the protein families database. Nucleic acids research, 2013: gkt1223

    Google Scholar 

  16. UniProt Consortium and others. UniProt: a hub for protein information. Nucleic Acids Res. gku989 (2014)

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  26. Bedi, U.: Regulation of H2B monoubiquitination pathway in breast cancer. Niedersächsische Staats-und Universitätsbibliothek Göttingen (2014)

    Google Scholar 

  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)

    Article  Google Scholar 

  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. 2011-10-4

    Google Scholar 

  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)

    Article  Google Scholar 

  30. Gay, N.J., Gangloff, M.: Structure and function of Toll receptors and their ligands. Annu. Rev. Biochem. 76, 141–165 (2007)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

Download references

Acknowledgement

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

Author information

Authors and Affiliations

  1. School of Electronics and Information Engineering, Tongji University, Shanghai, China

    Zhongwen Zhang & Bing Wang

  2. The Advanced Research Institute of Intelligent Sensing Network, Tongji University, Shanghai, China

    Zhongwen Zhang & Bing Wang

  3. The Key Laboratory of Embedded System and Service Computing, Tongji University, Shanghai, China

    Zhongwen Zhang & Bing Wang

  4. Institute of Health Sciences, Anhui University, Hefei, Anhui, China

    Peng Chen

  5. College of Electrical Engineering and Automation, Anhui University, Hefei, Anhui, China

    Jun Zhang

Authors
  1. Zhongwen Zhang
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  2. Peng Chen
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  3. Jun Zhang
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  4. Bing Wang
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Corresponding author

Correspondence to Bing Wang .

Editor information

Editors and Affiliations

  1. Tongji University , Shanghai, China

    De-Shuang Huang

  2. Polytechnic of Bari , Bari, Italy

    Vitoantonio Bevilacqua

  3. University of Wollongong , North Wollongong, New South Wales, Australia

    Prashan Premaratne

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© 2016 Springer International Publishing Switzerland

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Zhang, Z., Chen, P., Zhang, J., Wang, B. (2016). Inferring Disease-Related Domain Using Network-Based Method. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2016. Lecture Notes in Computer Science(), vol 9771. Springer, Cham. https://doi.org/10.1007/978-3-319-42291-6_77

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  • DOI: https://doi.org/10.1007/978-3-319-42291-6_77

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42290-9

  • Online ISBN: 978-3-319-42291-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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