Login Register Cart 0
Generic placeholder image

Current Genomics

Editor-in-Chief

ISSN (Print): 1389-2029
ISSN (Online): 1875-5488

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Deciphering the Novel Target Genes Involved in the Epigenetics of Hepatocellular Carcinoma Using Graph Theory Approach

Author(s): Nimisha Roy, Utkarsh Raj, Sneha Rai and Pritish K. Varadwaj*

Volume 20, Issue 8, 2019

Page: [545 - 555] Pages: 11

DOI: 10.2174/1389202921666191227100441

Price: $65

Abstract

Background: Even after decades of research, cancer, by and large, remains a challenge and is one of the major causes of death worldwide. For a very long time, it was believed that cancer is simply an outcome of changes at the genetic level but today, it has become a well-established fact that both genetics and epigenetics work together resulting in the transformation of normal cells to cancerous cells.

Objective: In the present scenario, researchers are focusing on targeting epigenetic machinery. The main advantage of targeting epigenetic mechanisms is their reversibility. Thus, cells can be reprogrammed to their normal state. Graph theory is a powerful gift of mathematics which allows us to understand complex networks.

Methodology: In this study, graph theory was utilized for quantitative analysis of the epigenetic network of hepato-cellular carcinoma (HCC) and subsequently finding out the important vertices in the network thus obtained. Secondly, this network was utilized to locate novel targets for hepato-cellular carcinoma epigenetic therapy.

Results: The vertices represent the genes involved in the epigenetic mechanism of HCC. Topological parameters like clustering coefficient, eccentricity, degree, etc. have been evaluated for the assessment of the essentiality of the node in the epigenetic network.

Conclusion: The top ten novel epigenetic target genes involved in HCC reported in this study are cdk6, cdk4, cdkn2a, smad7, smad3, ccnd1, e2f1, sf3b1, ctnnb1, and tgfb1.

Keywords: Graph theory, clustering coefficient, eccentricity, epigenome, stress centrality, hepatocellular carcinoma.

« Previous Next »
Graphical Abstract

[1]
Waddington, C.H. The epigenotype. Endeavour, 1942, 1, 18-20.
[2]
Egger, G.; Liang, G.; Aparicio, A.; Jones, P.A. Epigenetics in human disease and prospects for epigenetic therapy. Nature, 2004, 429(6990), 457-463.
[http://dx.doi.org/10.1038/nature02625] [PMID: 15164071]
[3]
Jones, P.A.; Baylin, S.B. The fundamental role of epigenetic events in cancer. Nat. Rev. Genet., 2002, 3(6), 415-428.
[http://dx.doi.org/10.1038/nrg816] [PMID: 12042769]
[4]
Miranda, T.B.; Jones, P.A. DNA methylation: the nuts and bolts of repression. J. Cell. Physiol., 2007, 213(2), 384-390.
[http://dx.doi.org/10.1002/jcp.21224] [PMID: 17708532]
[5]
Jones, P.A.; Laird, P.W. Cancer epigenetics comes of age. Nat. Genet., 1999, 21(2), 163-167.
[http://dx.doi.org/10.1038/5947] [PMID: 9988266]
[6]
Feinberg, A.P.; Ohlsson, R.; Henikoff, S. The epigenetic progenitor origin of human cancer. Nat. Rev. Genet., 2006, 7(1), 21-33.
[http://dx.doi.org/10.1038/nrg1748] [PMID: 16369569]
[7]
Jones, P.A.; Martienssen, R. A blueprint for a human epigenome project: the AACR human epigenome workshop. Cancer Res., 2005, 65(24), 11241-11246.
[http://dx.doi.org/10.1158/0008-5472.CAN-05-3865] [PMID: 16357125]
[8]
Raj, U.; Kumar, H.; Gupta, S.; Varadwaj, P.K. Novel DOT1L receptor natural inhibitors involved in mixed lineage leukemia: a virtual screening, molecular docking and dynamics simulation study. Asian Pac. J. Cancer Prev., 2015, 16(9), 3817-3825.
[http://dx.doi.org/10.7314/APJCP.2015.16.9.3817] [PMID: 25987043]
[9]
Raj, U.; Varadwaj, P.K. Epigenetics and its role in human cancer. In: Translational Bioinformatics and Its Application. Translational Medicine Research; Wei, D.Q.; Ma, Y.; Cho, W.; Xu, Q.; Zhou, F., Eds.; Springer, Dordrecht. 249-267.
[http://dx.doi.org/10.1007/978-94-024-1045-7_11]
[10]
He, L.; Hannon, G.J. MicroRNAs: small RNAs with a big role in gene regulation. Nat. Rev. Genet., 2004, 5(7), 522-531.
[http://dx.doi.org/10.1038/nrg1379] [PMID: 15211354]
[11]
Lu, J.; Getz, G.; Miska, E.A.; Alvarez-Saavedra, E.; Lamb, J.; Peck, D. MicroRNA expression profiles classify human cancers. Nature, 2005, 435(7043), 834-838.
[12]
Campbell, R.M.; Tummino, P.J. Cancer epigenetics drug discovery and development: the challenge of hitting the mark. J. Clin. Invest., 2014, 124(1), 64-69.
[http://dx.doi.org/10.1172/JCI71605] [PMID: 24382391]
[13]
Huber, W.; Carey, V.J.; Long, L.; Falcon, S.; Gentleman, R. Graphs in molecular biology. BMC Bioinformatics, 2007, 8(6)(Suppl. 6), S8.
[http://dx.doi.org/10.1186/1471-2105-8-S6-S8] [PMID: 17903289]
[14]
Lesne, A. Complex networks: from graph theory to biology. Lett. Math. Phys., 2006, 78(3), 235-262.
[http://dx.doi.org/10.1007/s11005-006-0123-1]
[15]
Mason, O.; Verwoerd, M. Graph theory and networks in Biology. IET Syst. Biol., 2007, 1(2), 89-119.
[http://dx.doi.org/10.1049/iet-syb:20060038] [PMID: 17441552]
[16]
Hahn, M.W.; Kern, A.D. Comparative genomics of centrality and essentiality in three eukaryotic protein-interaction networks. Mol. Biol. Evol., 2005, 22(4), 803-806.
[http://dx.doi.org/10.1093/molbev/msi072] [PMID: 15616139]
[17]
Ma’ayan, A. Introduction to network analysis in systems biology. Sci. Signal., 2011, 4(190), tr5.
[http://dx.doi.org/10.1126/scisignal.2001965] [PMID: 21917719]
[18]
EASL-EORTC clinical practice guidelines: management of hepatocellular carcinoma. J. Hepatol., 2012, 56(4), 908-943.
[http://dx.doi.org/10.1016/j.jhep.2011.12.001] [PMID: 22424438]
[19]
Archer, K.J.; Mas, V.R.; Maluf, D.G.; Fisher, R.A. High-throughput assessment of CpG site methylation for distinguishing between HCV-cirrhosis and HCV-associated hepatocellular carcinoma. Mol. Genet. Genomics, 2010, 283(4), 341-349.
[http://dx.doi.org/10.1007/s00438-010-0522-y] [PMID: 20165882]
[20]
Shen, J.; Wang, S.; Zhang, Y.J.; Kappil, M.; Wu, H.C.; Kibriya, M.G.; Wang, Q.; Jasmine, F.; Ahsan, H.; Lee, P.H.; Yu, M.W.; Chen, C.J.; Santella, R.M. Genome-wide DNA methylation profiles in hepatocellular carcinoma. Hepatology, 2012, 55(6), 1799-1808.
[http://dx.doi.org/10.1002/hep.25569] [PMID: 22234943]
[21]
Revill, K.; Wang, T.; Lachenmayer, A.; Kojima, K. Genome-wide methylation analysis and epigenetic unmasking identify tumor suppressor genes in hepatocellular carcinoma. Gastroenterology, 2013, 145(6), 1424-1435.
[http://dx.doi.org/10.1053/j.gastro.2013.08.055]
[22]
Shen, J.; Wang, S.; Zhang, Y.J.; Wu, H.C.; Kibriya, M.G.; Jasmine, F.; Ahsan, H.; Wu, D.P.; Siegel, A.B.; Remotti, H.; Santella, R.M. Exploring genome-wide DNA methylation profiles altered in hepatocellular carcinoma using Infinium HumanMethylation 450 BeadChips. Epigenetics, 2013, 8(1), 34-43.
[http://dx.doi.org/10.4161/epi.23062] [PMID: 23208076]
[23]
Mah, W.C.; Thurnherr, T.; Chow, P.K.; Chung, A.Y.; Ooi, L.L.; Toh, H.C.; The, B.T.; Saunthararajah, Y.; Lee, C.G. Methylation profiles reveal distinct subgroup of hepatocellular carcinoma patients with poor prognosis. PLoS One, 2014, 9(8) e104158
[http://dx.doi.org/10.1371/journal.pone.0104158]
[24]
Edgar, R.; Domrachev, M.; Lash, A.E. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res., 2002, 30(1), 207-210.
[http://dx.doi.org/10.1093/nar/30.1.207] [PMID: 11752295]
[25]
Jensen, L.J.; Kuhn, M.; Stark, M.; Chaffron, S.; Creevey, C.; Muller, J.; Doerks, T.; Julien, P.; Roth, A.; Simonovic, M.; Bork, P.; von Mering, C. STRING 8--a global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res., 2009, 37(Database issue), D412-D416.
[http://dx.doi.org/10.1093/nar/gkn760] [PMID: 18940858]
[26]
Shannon, P.; Markiel, A.; Ozier, O.; Baliga, N.S.; Wang, J.T.; Ramage, D.; Amin, N.; Schwikowski, B.; Ideker, T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res., 2003, 13(11), 2498-2504.
[http://dx.doi.org/10.1101/gr.1239303] [PMID: 14597658]
[27]
Tosadori, G.; Bestvina, I.; Spoto, F.; Laudanna, C.; Scardoni, G. Creating, generating and comparing random network models with NetworkRandomizer. F1000 Res., 2017, 5, 2524.
[28]
Barabasi, A.L.; Albert, R. Emergence of scaling in random networks. Science, 1999, 286(5439), 509-512.
[29]
Assenov, Y.; Ramírez, F.; Schelhorn, S.E.; Lengauer, T.; Albrecht, M. Computing topological parameters of biological networks. Bioinformatics, 2008, 24(2), 282-284.
[http://dx.doi.org/10.1093/bioinformatics/btm554] [PMID: 18006545]
[30]
Scardoni, G.; Tosadori, G.; Faizan, M.; Spoto, F.; Fabbri, F.; Laudanna, C. Biological network analysis with CentiScaPe: centralities and experimental dataset integration. F1000 Res., 2014, 3, 139.
[http://dx.doi.org/10.12688/f1000research.4477.1] [PMID: 26594322 ]
[31]
Lima-Mendez, G.; van Helden, J. The powerful law of the power law and other myths in network biology. Mol. Biosyst., 2009, 5(12), 1482-1493.
[http://dx.doi.org/10.1039/b908681a] [PMID: 20023717]
[32]
Maslov, S.; Sneppen, K. Specificity and stability in topology of protein networks. Science, 2002, 296(5569), 910-913.
[http://dx.doi.org/10.1126/science.1065103] [PMID: 11988575]
[33]
Barabási, A.L.; Gulbahce, N.; Loscalzo, J. Network medicine: a network-based approach to human disease. Nat. Rev. Genet., 2011, 12(1), 56-68.
[http://dx.doi.org/10.1038/nrg2918] [PMID: 21164525]
[34]
Yu, H.; Kim, P.M.; Sprecher, E.; Trifonov, V.; Gerstein, M. The importance of bottlenecks in protein networks: correlation with gene essentiality and expression dynamics. PLOS Comput. Biol., 2007, 3(4)e59
[http://dx.doi.org/10.1371/journal.pcbi.0030059] [PMID: 17447836]
[35]
Newman, M.E. A measure of betweenness centrality based on random walks. Soc. Networks, 2005, 27(1), 39-54.
[http://dx.doi.org/10.1016/j.socnet.2004.11.009]
[36]
Wu, J.; Mao, X.; Cai, T.; Luo, J.; Wei, L. KOBAS server: a web-based platform for automated annotation and pathway identification. Nucleic Acids Res., 2006, 34(suppl_2), W720-W724.
[37]
Chen, S.H.; Chin, C.H.; Wu, H.H.; Ho, C.W.; Ko, M.T.; Lin, C.Y. 2009.cyto-Hubba: a Cytoscape plug-in for hub object analysis in network biology. 20th International Conference on Genome Informatics,
[38]
Sengupta, U.; Ukil, S.; Dimitrova, N.; Agrawal, S. Expression-based network biology identifies alteration in key regulatory pathways of type 2 diabetes and associated risk/complications. PLoS One, 2009, 4(12) e8100
[http://dx.doi.org/10.1371/journal.pone.0008100] [PMID: 19997558]
[39]
Jones, P.A.; Issa, J.P.J.; Baylin, S. Targeting the cancer epigenome for therapy. Nat. Rev. Genet., 2016, 17(10), 630-641.
[http://dx.doi.org/10.1038/nrg.2016.93] [PMID: 27629931]
[40]
Thillai, K.; Ross, P.; Sarker, D. Molecularly targeted therapy for advanced hepatocellular carcinoma - a drug development crisis? World J. Gastrointest. Oncol., 2016, 8(2), 173-185.
[http://dx.doi.org/10.4251/wjgo.v8.i2.173] [PMID: 26909132]

Rights & Permissions Print Cite
Article Metrics
14
1
© 2024 Bentham Science Publishers | Privacy Policy