Identifying Network Subgraph-Associated Essential Genes in Molecular Networks
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 33087
Identifying Network Subgraph-Associated Essential Genes in Molecular Networks

Authors: Efendi Zaenudin, Chien-Hung Huang, Ka-Lok Ng

Abstract:

Essential genes play an important role in the survival of an organism. It has been shown that cancer-associated essential genes are genes necessary for cancer cell proliferation, where these genes are potential therapeutic targets. Also, it was demonstrated that mutations of the cancer-associated essential genes give rise to the resistance of immunotherapy for patients with tumors. In the present study, we focus on studying the biological effects of the essential genes from a network perspective. We hypothesize that one can analyze a biological molecular network by decomposing it into both three-node and four-node digraphs (subgraphs). These network subgraphs encode the regulatory interaction information among the network’s genetic elements. In this study, the frequency of occurrence of the subgraph-associated essential genes in a molecular network was quantified by using the statistical parameter, odds ratio. Biological effects of subgraph-associated essential genes are discussed. In summary, the subgraph approach provides a systematic method for analyzing molecular networks and it can capture useful biological information for biomedical research.

Keywords: Biological molecular networks, essential genes, graph theory, network subgraphs.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 493

References:


[1] Juhas, M., L. Eberl, and J.I. Glass, Essence of life: essential genes of minimal genomes. Trends Cell Biol, 2011. 21(10): p. 562-8.
[2] Chen, W.-H., et al., OGEE v2: an update of the online gene essentiality database with special focus on differentially essential genes in human cancer cell lines. Nucleic Acids Research, 2016. 45(D1): p. D940-D944.
[3] Zhan, T. and M. Boutros, Towards a compendium of essential genes - From model organisms to synthetic lethality in cancer cells. Critical reviews in biochemistry and molecular biology, 2016. 51(2): p. 74-85.
[4] Gilvary, C., et al., A machine learning approach predicts essential genes and pharmacological targets in cancer. 2019, bioRxiv.
[5] Pertesi, M., et al., Essential genes shape cancer genomes through linear limitation of homozygous deletions. Communications Biology, 2019. 2(1): p. 262.
[6] Patel, S.J., et al., Identification of essential genes for cancer immunotherapy. Nature, 2017. 548(7669): p. 537-542.
[7] Dickerson, J.E., et al., Defining the role of essential genes in human disease. PloS one, 2011. 6(11): p. e27368-e27368.
[8] Zhang, R., H.Y. Ou, and C.T. Zhang, DEG: a database of essential genes. Nucleic Acids Research, 2004. 32(suppl_1): p. D271-D272.
[9] Huang, C.-H., et al., Computational analysis of molecular networks using spectral graph theory, complexity measures and information theory. bioRxiv, 2019: p. 536318.
[10] Mowshowitz, A., Entropy and the complexity of graphs: II. The information content of digraphs and infinite graphs. The bulletin of mathematical biophysics, 1968. 30(2): p. 225-240.
[11] Lee, C.H. Huang, and K.L. Ng, In silico study of significant network motifs in the cancer networks. Master’s thesis, National Formosa University, Taiwan., 2016.
[12] Hsieh, W.T., et al., Transcription factor and microRNA-regulated network motifs for cancer and signal transduction networks. BMC Syst Biol, 2015. 9 Suppl 1: p. S5.
[13] Nakaya, A., et al., KEGG OC: a large-scale automatic construction of taxonomy-based ortholog clusters. Nucleic Acids Res, 2013. 41(Database issue): p. D353-7.
[14] Nishida, K., et al., KEGGscape: a Cytoscape app for pathway data integration. F1000Res, 2014. 3: p. 144.
[15] Arakelyan, A. and L. Nersisyan, KEGGParser: parsing and editing KEGG pathway maps in Matlab. Bioinformatics, 2013. 29(4): p. 518-9.
[16] Alon, U., An Introduction to Systems Biology: design principles of biological circuits. 2006: Chapman and Hall/CRC.
[17] Shen-Orr, S.S., et al., Network motifs in the transcriptional regulation network of Escherichia coli. Nature Genetics, 2002. 31(1): p. 64-68.
[18] Chan, S.W., et al., The Hippo pathway in biological control and cancer development. J Cell Physiol, 2011. 226(4): p. 928-39.
[19] Pan, D., Hippo signaling in organ size control. Genes Dev, 2007. 21(8): p. 886-97.
[20] Boopathy, G.T.K. and W. Hong, Role of Hippo Pathway-YAP/TAZ Signaling in Angiogenesis. Frontiers in Cell and Developmental Biology, 2019. 7(49).
[21] Karin, M., et al., NF-kappaB in cancer: from innocent bystander to major culprit. Nat Rev Cancer, 2002. 2(4): p. 301-10.
[22] Park, M.H. and J.T. Hong, Roles of NF-κB in Cancer and Inflammatory Diseases and Their Therapeutic Approaches. Cells, 2016. 5(2).
[23] Baker, R.G., M.S. Hayden, and S. Ghosh, NF-κB, inflammation, and metabolic disease. Cell Metab, 2011. 13(1): p. 11-22.
[24] Sabir, J.S.M., et al., Dissecting the Role of NF-κb Protein Family and Its Regulators in Rheumatoid Arthritis Using Weighted Gene Co-Expression Network. Frontiers in Genetics, 2019. 10(1163).
[25] Yamashita, M. and E. Passegué, TNF-α Coordinates Hematopoietic Stem Cell Survival and Myeloid Regeneration. Cell Stem Cell, 2019. 25(3): p. 357-372.e7.
[26] Sun, S.-C., Non-canonical NF-κB signaling pathway. Cell research, 2011. 21(1): p. 71-85.
[27] Hayden, M.S. and S. Ghosh, Regulation of NF-κB by TNF family cytokines. Seminars in immunology, 2014. 26(3): p. 253-266.
[28] Yilmaz, A., et al., Defining essential genes for human pluripotent stem cells by CRISPR-Cas9 screening in haploid cells. Nat Cell Biol, 2018. 20(5): p. 610-619.
[29] Yu, L., et al., A survey of essential gene function in the yeast cell division cycle. Molecular biology of the cell, 2006. 17(11): p. 4736-4747.
[30] Zaenudin, E., et al., A Parallel Algorithm to Generate Connected Network Motifs IAENG International Journal of Computer Science, 2019 46(4): p. pp518-523.