Pathogenic Genes Selection Model of Genetic Disease based on Network Motifs Slicing Feedback

Author(s): Shengli Zhang*, Zekun Tong, Haoyu Yin, Yifan Feng

Journal Name: Current Proteomics

Volume 16 , Issue 5 , 2019

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Graphical Abstract:


Background: Finding the pathogenic gene is very important for understanding the pathogenesis of the disease, locating effective drug targets and improving the clinical level of medical treatment. However, the existing methods for finding the pathogenic genes still have limitations, for instance the computational complexity is high, and the combination of multiple genes and pathways has not been considered to search for highly related pathogenic genes and so on.

Methods: We propose a pathogenic genes selection model of genetic disease based on Network Motifs Slicing Feedback (NMSF). We find a point set which makes the conductivity of the motif minimum then use it to substitute for the original gene pathway network. Based on the NMSF, we propose a new pathogenic genes selection model to expand pathogenic gene set.

Results: According to the gene set we have obtained, selection of key genes will be more accurate and convincing. Finally, we use our model to screen the pathogenic genes and key pathways of liver cancer and lung cancer, and compare the results with the existing methods.

Conclusion: The main contribution is to provide a method called NMSF which simplifies the gene pathway network to make the selection of pathogenic gene simple and feasible. The fact shows our result has a wide coverage and high accuracy and our model has good expeditiousness and robustness.

Keywords: Genetic disease, gene pathway network, network motifs slicing feedback, pathogenic gene set expansion, robustness, algorithm.

Mckusick, V.A. Mendelian inheritance in man and its online version, OMIM. Am. J. Hum. Genet., 2007, 80(4), 588-604.
Botstein, D.; Risch, N. Discovering genotypes underlying human phenotypes: past successes for mendelian disease, future approaches for complex disease. Nat. Genet., 2003, 33(2), 228-237.
Oti, M.; Brunner, H. The modular nature of genetic diseases. Clin. Genet., 2007, 71(1), 1-11.
Wood, L.D.; Parsons, D.W.; Jones, S.; Lin, J.; Sjöblom, T.; Leary, R.J.; Shen, D.; Boca, S.M.; Barber, T.; Ptak, J. The genomic landscapes of human breast and colorectal cancers. Science, 2007, 318(5853), 1108-1113.
Lim, J.; Hao, T.; Shaw, C.; Patel, A.J.; Szabó, G.; Rual, J.F.; Fisk, C.J.; Li, N.; Smolyar, A.; Hill, D.E. A protein-protein interaction network for human inherited ataxias and disorders of Purkinje cell degeneration. Cell, 2006, 125(4), 801-814.
Lage, K.; Karlberg, E.O.; Størling, Z.M.; Pedersen, A.G.; Rigina, O.; Hinsby, A.M.; Tümer, Z.; Pociot, F.; Tommerup, N.; Moreau, Y. A human phenome-interactome network of protein complexes implicated in genetic disorders. Nat. Biotechnol., 2007, 25(3), 309-316.
Wijmenga, C.; Franke, L.H.; Egmont-Petersen, M. Comparison of a functional human gene network with a network reconstructed by applying genetical genomics; , 2019. In press
Wu, X.B.; Jiang, R.; Zhang, M.Q.; Li, S. Network-based global inference of human disease genes. Mol. Syst. Biol., 2008, 4(1), 189.
Köhler, S.; Bauer, S.; Horn, D.; Robinson, P.N. Walking the interactome for prioritization of candidate disease genes. Am. J. Hum. Genet., 2008, 82(4), 949-958.
Vanunu, O.; Magger, O.; Ruppin, E.; Shlomi, T.; Sharan, R. Associating genes and protein complexes with disease via network propagation. PLOS Comput. Biol., 2010, 6(1)e1000641
Benson, A.R.; Gleich, D.F.; Leskovec, J. Higher-order organization of complex networks. Science, 2016, 353(6295), 163-166.
Stoynoff, S. Network motifs: simple building blocks of complex networks. Science, 2002, 298(5594), 824-827.
Paranjape, A.; Benson, A.R.; Leskovec, J. Motifs in temporal networks. In 17th Proc. Tenth ACM Int. Conf. Web Search Data Mining, New York, NY, USA2017, pp. 601-610.
Leskovec, J.; Benson, A.R.; Gleich, D.F. Higher-order organization of complex networks. Science, 2016, 353(8), 163-166.
Borotkanics, R.; Lehmann, H. Network motifs that recur across species, including gene regulatory and protein-protein interaction networks. Arch. Toxicol., 2015, 89(4), 489-499.
Tsourakakis, C.E.; Pachocki, J.; Mitzenmacher, M. Scalable motif-aware graph clustering. In Proc. 26th Int. World Wide Web Conf. Committe, 2017, pp. 1451-1460.
Albieri, V.; Didelez, V. Comparison of statistical methods for finding network motifs. Stat. Appl. Genet. Mol. Biol., 2015, 13(4), 403-422.
Cao, J.; Lu, X.X.; Li, Y.; Zhu, L.Q.; Yang, C.; Ou, C.; Tang, Y.P. Applying gene set enrichment analysis and meta-analysis to screen key genes controlling the development and progression of hepatic carcinoma. World Chin. J. Digestology, 2012, 20(9), 754-758.
Huang, D.L.; Zou, J.J.; Zhang, J.R. Bioinformatics analysis of the key risk genes in lung cancer. China J. Cancer Prev. Treat., 2012, 39(23), 1890-1895.
Wikipedia, mutation.
Kegg, insulin signaling pathway - homo sapiens (human). kegg-bin/show_pathway?hsa049102016.

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Article Details

Year: 2019
Page: [392 - 401]
Pages: 10
DOI: 10.2174/1570164616666190123141726
Price: $25

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