Article Contents
Article Contents

# Algebraic and topological indices of molecular pathway networks in human cancers

• Protein-protein interaction networks associated with diseases have gained prominence as an area of research. We investigate algebraic and topological indices for protein-protein interaction networks of 11 human cancers derived from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. We find a strong correlation between relative automorphism group sizes and topological network complexities on the one hand and five year survival probabilities on the other hand. Moreover, we identify several protein families (e.g. PIK, ITG, AKT families) that are repeated motifs in many of the cancer pathways. Interestingly, these sources of symmetry are often central rather than peripheral. Our results can aide in identification of promising targets for anti-cancer drugs. Beyond that, we provide a unifying framework to study protein-protein interaction networks of families of related diseases (e.g. neurodegenerative diseases, viral diseases, substance abuse disorders).
Mathematics Subject Classification: Primary: 92C42; Secondary: 94C15.

 Citation:

•  [1] R. Albert, H.Jeong and A. L. Barabási, Error and attack tolerance of complex networks, Nature, 406 (2000), 378-382. [2] W. Alexander, Inhibiting the Akt pathway in cancer treatment, Three leading candidates, Pharmacy and Therapeutics, 36 (2011), 225-227. [3] F. Berger, P. Gritzmann S. de Vries, Minimum cycle bases for network graphs, Algorithmica, 40 (2004), 51-62.doi: 10.1007/s00453-004-1098-x. [4] B. Bollobás, Random Graphs, Cambridge University Press, Cambridge, 2001. [5] D. Breitkreutz, L. Hlatky, E. Rietman, J. A. Tuszynski, Molecular signaling network complexity is correlated with cancer patient survivability, Proc. Natl. Acad. Sci. USA, 109 (2012), 9209-9212. [6] N. Chandra and J. Padiadpu, Network approaches to drug discovery, Expert Opin. Drug Discov., 8 (2013), 7-20. [7] P. Csermely, T. Korcsmáros, H. J. M. Kiss, G. London, R. Nussinov, Structure and dynamics of molecular networks: A novel paradigm of drug discovery. A comprehensive review, Pharmacol. Therapeut., 138 (2013), 333-408. [8] D. Garlaschelli, F. Ruzzenenti and R. Basosi, Complex networks and symmetry I: A review, Symmetry, 2 (2010), 1683-1709, arXiv:1006.3923.doi: 10.3390/sym2031683. [9] D. Holmes, PI3K pathway inhibitors approach junction, Nat. Rev. Drug Discov., 10 (2011), 563-564. [10] D. W. Huang, B. T. Sherman and R. A. Lempicki, Bioinformatics enrichment tools: Paths toward the comprehensive functional analysis of large gene lists, Nucleic Acid Res., 37 (2009), 1-13. [11] D. W. Huang, B. T. Sherman and R. A. Lempicki, Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources, Nature Protocols, 4 (2009), 44-57, http://david.abcc.ncifcrf.gov/ [12] B. H. Junker, F. Schreiber (editors), Analysis of Biological Networks, John Wiley & Sons, Hoboken, NJ, 2008. [13] M. Kanehisa and S. Goto, KEGG: Kyoto Encyclopedia of Genes and Genomes, Nucleic Acid Res., 28 (2000), 23-30, http://www.genome.jp/kegg/. [14] M. Kanehisa, S. Goto, S. Kawashima, Y. Okuno and M. Hattori, The KEGG resource for deciphering the genome, Nucleic Acid Res., 32 (2004), D277-D280. [15] H. Katebi, K. A. Sakallah and I. L. Markov, Graph symmetry detection and canonical labeling: Differences and synergies, In A Voronkov, editor, The Alan Turing Centenary, pages 181-195. EasyChair, 2012,http://vlsicad.eecs.umich.edu/BK/SAUCY/. [16] T. Kavitha, C. Liebchen, K. Mehlhorn, D. Michail, R. Rizzie, T. Ueckerdt and K. A. Zweig, Cycle bases in graphs characterization, algorithms, complexity, and applications, Computer Science Review, 3 (2009), 199-243. [17] E. V. Koonin, Y. I. Wolf and G. P. Karev (editors), Power Laws, Scale-Free Networks and Genome Biology, Landes Bioscience, Austin, TX, 2006. [18] A. Ma'ayan and B. D. MacArthur (editors), New Frontiers of Network Analysis in Systems Biology, Springer Verlag, Dordrecht, Heidelberg, New York, London, 2012. [19] B. D. MacArthur, R. J. Sánchez-García and J. W. Anderson, Symmetry in complex networks, Discr. Appl. Math., 156 (2008), 3525-3531.doi: 10.1016/j.dam.2008.04.008. [20] National Cancer Institute, Surveillance, Epidemiology and End Results (SEER) Program, 2013, http://seer.cancer.gov/. [21] E. A. Rietman, R. L. Karp and J. A. Tuszynski, Review and application of group theory to molecular systems biology, Theor. Biol. Med. Model., 8 (2011), p21. [22] P. Shannon, A. Markiel, O. Ozier, N. S. Baliga, J. T. Wang, D. Ramage, N. Amin, B. Schwikowski and T. Ideker, Cytoscape: A software environment for integrated models of biomolecular interaction networks, Genome Res., 13 (2003), 2498-2504. http://cytoscape.org. [23] K. Takemoto and K. Kihara, Modular organization of cancer signaling networks is associated with patient survivability, BioSystems, 113 (2013), 149-154. [24] The GAP Group, GAP - Groups, Algorithms, and Programming, University of St Andrews, St Andrews, United Kingdom, 2013. http://www.gap-system.org. [25] W. Winterbach, P. van Mieghem, M. Reinders, H. Wang and D. de Ridder, Topology of molecular interaction networks, BMC Syst. Biol., 7 (2013), p90. [26] Y. Xiao, B. D. MacArthur, H. Wang, M. Xiong and W. Wang, Network quotients: Structural skeletons of complex systems, Phys. Rev. E, 78 (2008), 046102, arXiv:0802.4318. [27] J. D. Zhang and S. Wiemann, KEGGgraph: A graph approach to KEGG PATHWAY in R and Bioconductor, Bioinformatics, 25 (2009), 1470-1471, http://bioconductor.org.
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