Analysis of Protein Interaction Network for Colorectal Cancer

  • Zlate RistovskiEmail author
  • Kire Trivodaliev
  • Slobodan Kalajdziski
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 665)


In this paper we create and analyze a protein-protein interaction network (PPIN) of colorectal cancer (CRC). First we identify proteins that are related to the CRC (set of seed proteins). Using this set we generate the CRC PPIN with the help of Cytoscape. We analyze this PPIN in a twofold manner. We first extract important topological features for proteins in the network which we use to determine CRC essential proteins. Next we perform a modular analysis by discovering CRC significant functional terms through the process of GO enrichment within densely connected subgroups (clusters) of the PPIN. The modular analysis results in a mapping from the CRC significant terms to CRC significant proteins. Finally, we combine the topological and modular evidence for the proteins in the CRC PPIN, exclude the initial seed proteins and obtain a list of proteins that could be taken as possible bio-markers for CRC.


Colorectal cancer Protein-protein interaction network Network analysis Gene Ontology Clustering Cytoscape 



The work in this paper was partially financed by the Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University, Skopje, as part of the “Analysis of nutrigenomic data” project.


  1. 1.
    Kreeger, P.K., Lauffenburger, D.A.: Cancer systems biology: a network modeling perspective. Carcinogenesis 31(1), 2–8 (2010)CrossRefGoogle Scholar
  2. 2.
    Consortium, U., et al.: UniProt: a hub for protein information. Nucleic Acids Res. gku989 (2014)Google Scholar
  3. 3.
    Alberghina, L., Höfer, T., Vanoni, M.: Molecular networks and system-level properties. J. Biotechnol. 144(3), 224–233 (2009)CrossRefGoogle Scholar
  4. 4.
    Wachi, S., Yoneda, K., Wu, R.: Interactome-transcriptome analysis reveals the high centrality of genes differentially expressed in lung cancer tissues. Bioinformatics 21(23), 4205–4208 (2005)CrossRefGoogle Scholar
  5. 5.
    Rhodes, D.R., Chinnaiyan, A.M.: Integrative analysis of the cancer transcriptome. Nat. Genet. 37, S31–S37 (2005)CrossRefGoogle Scholar
  6. 6.
    Mani, K.M., Lefebvre, C., Wang, K., Lim, W.K., Basso, K., Dalla-Favera, R., Califano, A.: A systems biology approach to prediction of oncogenes and molecular perturbation targets in B-cell lymphomas. Mol. Syst. Biol. 4(1), 169 (2008)Google Scholar
  7. 7.
    Jonsson, P.F., Bates, P.A.: Global topological features of cancer proteins in the human interactome. Bioinformatics 22(18), 2291–2297 (2006)CrossRefGoogle Scholar
  8. 8.
    Aragues, R., Sander, C., Oliva, B.: Predicting cancer involvement of genes from heterogeneous data. BMC Bioinform. 9(1), 1 (2008)CrossRefGoogle Scholar
  9. 9.
    Forbes, S.A., Bindal, N., Bamford, S., Cole, C., Kok, C.Y., Beare, D., Jia, M., Shepherd, R., Leung, K., Menzies, A., et al.: COSMIC: mining complete cancer genomes in the catalogue of somatic mutations in cancer. Nucleic Acids Res. gkq929 (2010)Google Scholar
  10. 10.
    Maglott, D., Ostell, J., Pruitt, K.D., Tatusova, T.: Entrez gene: gene-centered information at NCBI. Nucleic Acids Res. 33(Suppl. 1), D54–D58 (2005)Google Scholar
  11. 11.
    Aranda, B., Blankenburg, H., Kerrien, S., Brinkman, F.S., Ceol, A., Chautard, E., Dana, J.M., De Las Rivas, J., Dumousseau, M., Galeota, E., et al.: PSICQUIC and PSISCORE: accessing and scoring molecular interactions. Nat. Methods 8(7), 528–529 (2011)CrossRefGoogle Scholar
  12. 12.
    Bader, G.D., Betel, D., Hogue, C.W.: BIND: the biomolecular interaction network database. Nucleic Acids Res. 31(1), 248–250 (2003)CrossRefGoogle Scholar
  13. 13.
    Liu, T., Lin, Y., Wen, X., Jorissen, R.N., Gilson, M.K.: BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities. Nucleic Acids Res. 35(Suppl. 1), D198–D201 (2007)CrossRefGoogle Scholar
  14. 14.
    Stark, C., Breitkreutz, B.J., Reguly, T., Boucher, L., Breitkreutz, A., Tyers, M.: BioGRID: a general repository for interaction datasets. Nucleic Acids Res. 34(Suppl. 1), D535–D539 (2006)CrossRefGoogle Scholar
  15. 15.
    Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Bader, G.D., Hogue, C.W.: An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinform. 4(1), 1 (2003)CrossRefGoogle Scholar
  17. 17.
    Morris, J.H., Apeltsin, L., Newman, A.M., Baumbach, J., Wittkop, T., Su, G., Bader, G.D., Ferrin, T.E.: clusterMaker: a multi-algorithm clustering plugin for cytoscape. BMC Bioinform. 12(1), 1 (2011)CrossRefGoogle Scholar
  18. 18.
    Bindea, G., Mlecnik, B., Hackl, H., Charoentong, P., Tosolini, M., Kirilovsky, A., Fridman, W.H., Pagès, F., Trajanoski, Z., Galon, J.: ClueGO: a cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics 25(8), 1091–1093 (2009)CrossRefGoogle Scholar
  19. 19.
    Alvord, G., Roayaei, J., Stephens, R., Baseler, M.W., Lane, H.C., Lempicki, R.A.: The david gene functional classification tool: a novel biological module-centric algorithm to functionally analyze large gene lists. Genome Biol. 8(9), 183 (2007)CrossRefGoogle Scholar
  20. 20.
    Maere, S., Heymans, K., Kuiper, M.: BiNGO: a cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics 21(16), 3448–3449 (2005)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Zlate Ristovski
    • 1
    Email author
  • Kire Trivodaliev
    • 1
  • Slobodan Kalajdziski
    • 1
  1. 1.Faculty of Computer Science and EngineeringSs. Cyril and Methodius UniversitySkopjeMacedonia

Personalised recommendations