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Current Genomics

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

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Research Article

Bioinformatics Analysis based on Multiple Databases Identifies Hub Genes Associated with Hepatocellular Carcinoma

Author(s): Lu Zeng , Xiude Fan , Xiaoyun Wang , Huan Deng , Kun Zhang , Xiaoge Zhang , Shan He , Na Li , Qunying Han and Zhengwen Liu*

Volume 20, Issue 5, 2019

Page: [349 - 361] Pages: 13

DOI: 10.2174/1389202920666191011092410

Price: $65

Abstract

Background: Hepatocellular carcinoma (HCC) is the most common liver cancer and the mechanisms of hepatocarcinogenesis remain elusive.

Objective: This study aims to mine hub genes associated with HCC using multiple databases.

Methods: Data sets GSE45267, GSE60502, GSE74656 were downloaded from GEO database. Differentially expressed genes (DEGs) between HCC and control in each set were identified by limma software. The GO term and KEGG pathway enrichment of the DEGs aggregated in the datasets (aggregated DEGs) were analyzed using DAVID and KOBAS 3.0 databases. Protein-protein interaction (PPI) network of the aggregated DEGs was constructed using STRING database. GSEA software was used to verify the biological process. Association between hub genes and HCC prognosis was analyzed using patients’ information from TCGA database by survminer R package.

Results: From GSE45267, GSE60502 and GSE74656, 7583, 2349, and 553 DEGs were identified respectively. A total of 221 aggregated DEGs, which were mainly enriched in 109 GO terms and 29 KEGG pathways, were identified. Cell cycle phase, mitotic cell cycle, cell division, nuclear division and mitosis were the most significant GO terms. Metabolic pathways, cell cycle, chemical carcinogenesis, retinol metabolism and fatty acid degradation were the main KEGG pathways. Nine hub genes (TOP2A, NDC80, CDK1, CCNB1, KIF11, BUB1, CCNB2, CCNA2 and TTK) were selected by PPI network and all of them were associated with prognosis of HCC patients.

Conclusion: TOP2A, NDC80, CDK1, CCNB1, KIF11, BUB1, CCNB2, CCNA2 and TTK were hub genes in HCC, which may be potential biomarkers of HCC and targets of HCC therapy.

Keywords: Hepatocellular carcinoma, hub gene, bioinformatics, differentially expressed gene, database, mRNA.

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[1]
Forner, A.; Reig, M.; Bruix, J. Hepatocellular carcinoma. Lancet, 2018, 391(10127), 1301-1314.
[http://dx.doi.org/10.1016/S0140-6736(18)30010-2] [PMID: 29307467]
[2]
Xie, M.; Yang, Z.; Liu, Y.; Zheng, M. The role of HBV-induced autophagy in HBV replication and HBV related-HCC. Life Sci., 2018, 205, 107-112.
[http://dx.doi.org/10.1016/j.lfs.2018.04.051] [PMID: 29709654]
[3]
Kunnathuparambil, S.G.; Anoobjohn, K.; Zameer, P.K.M.; Sreesh, S.; Narayan, P.; Vinayakumar, K.R. Is alcohol abstinence a risk factor for development of hepatocellular carcinoma (HCC) in alcohol related cirrhosis? J. Clin. Exp. Hepatol., 2013, 3(1), S104-S104.
[http://dx.doi.org/10.1016/j.jceh.2013.03.176]
[4]
Kühn, T.; Nonnenmacher, T.; Sookthai, D.; Schübel, R.; Quintana Pacheco, D.A.; von Stackelberg, O.; Graf, M.E.; Johnson, T.; Schlett, C.L.; Kirsten, R.; Ulrich, C.M.; Kaaks, R.; Kauczor, H.U.; Nattenmüller, J. Anthropometric and blood parameters for the prediction of NAFLD among overweight and obese adults. BMC Gastroenterol., 2018, 18(1), 113.
[http://dx.doi.org/10.1186/s12876-018-0840-9] [PMID: 30005625]
[5]
Deng, H.; Eckel, S.P.; Liu, L.; Lurmann, F.W.; Cockburn, M.G.; Gilliland, F.D. Particulate matter air pollution and liver cancer survival. Int. J. Cancer, 2017, 141(4), 744-749.
[http://dx.doi.org/10.1002/ijc.30779] [PMID: 28589567]
[6]
Gentleman, R.C.; Carey, V.J.; Bates, D.M.; Bolstad, B.; Dettling, M.; Dudoit, S.; Ellis, B.; Gautier, L.; Ge, Y.; Gentry, J.; Hornik, K.; Hothorn, T.; Huber, W.; Iacus, S.; Irizarry, R.; Leisch, F.; Li, C.; Maechler, M.; Rossini, A.J.; Sawitzki, G.; Smith, C.; Smyth, G.; Tierney, L.; Yang, J.Y.; Zhang, J. Bioconductor: Open software development for computational biology and bioinformatics. Genome Biol., 2004, 5(10), R80.
[http://dx.doi.org/10.1186/gb-2004-5-10-r80] [PMID: 15461798]
[7]
Barrett, T.; Troup, D.B.; Wilhite, S.E.; Ledoux, P.; Evangelista, C.; Kim, I.F.; Tomashevsky, M.; Marshall, K.A.; Phillippy, K.H.; Sherman, P.M.; Muertter, R.N.; Holko, M.; Ayanbule, O.; Yefanov, A.; Soboleva, A. NCBI GEO: Archive for functional genomics data sets--10 years on. Nucleic Acids Res., 2011, 39(Database issue), D1005-D1010.
[http://dx.doi.org/10.1093/nar/gkq1184] [PMID: 21097893]
[8]
Chen, C.L.; Tsai, Y.S.; Huang, Y.H.; Liang, Y.J.; Sun, Y.Y.; Su, C.W.; Chau, G.Y.; Yeh, Y.C.; Chang, Y.S.; Hu, J.T.; Wu, J.C. Lymphoid enhancer factor 1 contributes to hepatocellular carcinoma progression through transcriptional regulation of epithelial-mesenchymal transition regulators and stemness genes. Hepatol Commun., 2018, 2(11), 1392-1407.
[http://dx.doi.org/10.1002/hep4.1229] [PMID: 30411085]
[9]
Wang, Y.H.; Cheng, T.Y.; Chen, T.Y.; Chang, K.M.; Chuang, V.P.; Kao, K.J. Plasmalemmal Vesicle Associated Protein (PLVAP) as a therapeutic target for treatment of hepatocellular carcinoma. BMC Cancer, 2014, 14, 815.
[http://dx.doi.org/10.1186/1471-2407-14-815] [PMID: 25376302]
[10]
Ritchie, M.E.; Phipson, B.; Wu, D.; Hu, Y.; Law, C.W.; Shi, W.; Smyth, G.K. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res., 2015, 43(7)e47
[http://dx.doi.org/10.1093/nar/gkv007] [PMID: 25605792]
[11]
Kolde, R.; Laur, S.; Adler, P.; Vilo, J. Robust rank aggregation for gene list integration and meta-analysis. Bioinformatics, 2012, 28(4), 573-580.
[http://dx.doi.org/10.1093/bioinformatics/btr709] [PMID: 22247279]
[12]
Huang, W.; Sherman, B.T.; Lempicki, R.A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc., 2009, 4(1), 44-57.
[http://dx.doi.org/10.1038/nprot.2008.211] [PMID: 19131956]
[13]
Ai, C.; Kong, L. CGPS: A machine learning-based approach integrating multiple gene set analysis tools for better prioritization of biologically relevant pathways. J. Genet. Genomics, 2018, 45(9), 489-504.
[http://dx.doi.org/10.1016/j.jgg.2018.08.002] [PMID: 30292791]
[14]
Szklarczyk, D.; Morris, J.H.; Cook, H.; Kuhn, M.; Wyder, S.; Simonovic, M.; Santos, A.; Doncheva, N.T.; Roth, A.; Bork, P.; Jensen, L.J.; von Mering, C. The STRING database in 2017: Quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res., 2017, 45(D1), D362-D368.
[http://dx.doi.org/10.1093/nar/gkw937] [PMID: 27924014]
[15]
Subramanian, A.; Tamayo, P.; Mootha, V.K.; Mukherjee, S.; Ebert, B.L.; Gillette, M.A.; Paulovich, A.; Pomeroy, S.L.; Golub, T.R.; Lander, E.S.; Mesirov, J.P. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA, 2005, 102(43), 15545-15550.
[http://dx.doi.org/10.1073/pnas.0506580102] [PMID: 16199517]
[16]
Grossman, R.L.; Heath, A.P.; Ferretti, V.; Varmus, H.E.; Lowy, D.R.; Kibbe, W.A.; Staudt, L.M. Toward a shared vision for cancer genomic data. N. Engl. J. Med., 2016, 375(12), 1109-1112.
[http://dx.doi.org/10.1056/NEJMp1607591] [PMID: 27653561]
[17]
Brown, C. hash: Full Feature Implementation of Hash/Associated Arrays/Dictionaries. Available from:. https://CRAN.R-project.org/package=hash [Accessed on: June 27, 2019]
[18]
Kassambara, A.; Kosinski, M.; Biecek, P.; Fabian, S. Survminer: Drawing survival curves using ‘ggplot2’R package version 0.4.4. Available from:. https://CRAN.R-project.org/package=survminer [Accessed on: June 27, 2019]
[19]
Slamon, D.J.; Press, M.F. Alterations in the TOP2A and HER2 genes: Association with adjuvant anthracycline sensitivity in human breast cancers. J. Natl. Cancer Inst., 2009, 101(9), 615-618.
[http://dx.doi.org/10.1093/jnci/djp092] [PMID: 19401550]
[20]
Amirnasr, A.; Verdijk, R.M.; van Kuijk, P.F.; Taal, W.; Sleijfer, S.; Wiemer, E.A.C. Expression and inhibition of BRD4, EZH2 and TOP2A in neurofibromas and malignant peripheral nerve sheath tumors. PLoS One, 2017, 12(8)e0183155
[http://dx.doi.org/10.1371/journal.pone.0183155] [PMID: 28813519]
[21]
Wong, N.; Yeo, W.; Wong, W.L.; Wong, N.L.; Chan, K.Y.; Mo, F.K.; Koh, J.; Chan, S.L.; Chan, A.T.; Lai, P.B.; Ching, A.K.; Tong, J.H.; Ng, H.K.; Johnson, P.J.; To, K.F. TOP2A overexpression in hepatocellular carcinoma correlates with early age onset, shorter patients survival and chemoresistance. Int. J. Cancer, 2009, 124(3), 644-652.
[http://dx.doi.org/10.1002/ijc.23968] [PMID: 19003983]
[22]
Ju, L.L.; Chen, L.; Li, J.H.; Wang, Y.F.; Lu, R.J.; Bian, Z.L.; Shao, J.G. Effect of NDC80 in human hepatocellular carcinoma. World J. Gastroenterol., 2017, 23(20), 3675-3683.
[http://dx.doi.org/10.3748/wjg.v23.i20.3675] [PMID: 28611520]
[23]
Liu, B.; Yao, Z.; Hu, K.; Huang, H.; Xu, S.; Wang, Q.; Yang, Y.; Ren, J. ShRNA-mediated silencing of the Ndc80 gene suppress cell proliferation and affected hepatitis B virus-related hepatocellular carcinoma. Clin. Res. Hepatol. Gastroenterol., 2016, 40(3), 297-303.
[http://dx.doi.org/10.1016/j.clinre.2015.08.002] [PMID: 26382282]
[24]
Santamaría, D.; Barrière, C.; Cerqueira, A.; Hunt, S.; Tardy, C.; Newton, K.; Cáceres, J.F.; Dubus, P.; Malumbres, M.; Barbacid, M. CDK1 is sufficient to drive the mammalian cell cycle. Nature, 2007, 448(7155), 811-815.
[http://dx.doi.org/10.1038/nature06046] [PMID: 17700700]
[25]
Costa-Cabral, S.; Brough, R.; Konde, A.; Aarts, M.; Campbell, J.; Marinari, E.; Riffell, J.; Bardelli, A.; Torrance, C.; Lord, C.J.; Ashworth, A. CDK1 is a synthetic lethal target for KRAS mutant tumours. PLoS One, 2016, 11(2)e0149099
[http://dx.doi.org/10.1371/journal.pone.0149099] [PMID: 26881434]
[26]
Zhao, J.; Han, S.X.; Ma, J.L.; Ying, X.; Liu, P.; Li, J.; Wang, L.; Zhang, Y.; Ma, J.; Zhang, L.; Zhu, Q. The role of CDK1 in apoptin-induced apoptosis in hepatocellular carcinoma cells. Oncol. Rep., 2013, 30(1), 253-259.
[http://dx.doi.org/10.3892/or.2013.2426] [PMID: 23619525]
[27]
Zhou, J.; Han, S.; Qian, W.; Gu, Y.; Li, X.; Yang, K. Metformin induces miR-378 to downregulate the CDK1, leading to suppression of cell proliferation in hepatocellular carcinoma. OncoTargets Ther., 2018, 11, 4451-4459.
[http://dx.doi.org/10.2147/OTT.S167614] [PMID: 30104887]
[28]
Zhang, Y.; Huang, W.; Ran, Y.; Xiong, Y.; Zhong, Z.; Fan, X.; Wang, Z.; Ye, Q. miR-582-5p inhibits proliferation of hepatocellular carcinoma by targeting CDK1 and AKT3. Tumour Biol., 2015, 36(11), 8309-8316.
[http://dx.doi.org/10.1007/s13277-015-3582-0] [PMID: 26002580]
[29]
Glotzer, M.; Murray, A.W.; Kirschner, M.W. Cyclin is degraded by the ubiquitin pathway. Nature, 1991, 349(6305), 132-138.
[http://dx.doi.org/10.1038/349132a0] [PMID: 1846030]
[30]
Sherr, C.J.; Roberts, J.M. Living with or without cyclins and cyclin-dependent kinases. Genes Dev., 2004, 18(22), 2699-2711.
[http://dx.doi.org/10.1101/gad.1256504] [PMID: 15545627]
[31]
Chai, N.; Xie, H.H.; Yin, J.P.; Sa, K.D.; Guo, Y.; Wang, M.; Liu, J.; Zhang, X.F.; Zhang, X.; Yin, H.; Nie, Y.Z.; Wu, K.C.; Yang, A.G.; Zhang, R. FOXM1 promotes proliferation in human hepatocellular carcinoma cells by transcriptional activation of CCNB1. Biochem. Biophys. Res. Commun., 2018, 500(4), 924-929.
[http://dx.doi.org/10.1016/j.bbrc.2018.04.201] [PMID: 29705704]
[32]
Gao, C.L.; Wang, G.W.; Yang, G.Q.; Yang, H.; Zhuang, L. Karyopherin subunit-α 2 expression accelerates cell cycle progression by upregulating CCNB2 and CDK1 in hepatocellular carcinoma. Oncol. Lett., 2018, 15(3), 2815-2820.
[PMID: 29435009]
[33]
Yang, F.; Gong, J.; Wang, G.; Chen, P.; Yang, L.; Wang, Z. Waltonitone inhibits proliferation of hepatoma cells and tumorigenesis via FXR-miR-22-CCNA2 signaling pathway. Oncotarget, 2016, 7(46), 75165-75175.
[http://dx.doi.org/10.18632/oncotarget.12614] [PMID: 27738335]
[34]
Asbaghi, Y.; Thompson, L.L.; Lichtensztejn, Z.; McManus, K.J. KIF11 silencing and inhibition induces chromosome instability that may contribute to cancer. Genes Chromosomes Cancer, 2017, 56(9), 668-680.
[http://dx.doi.org/10.1002/gcc.22471] [PMID: 28510357]
[35]
Xu, B.; Xu, T.; Liu, H.; Min, Q.; Wang, S.; Song, Q. MiR-490-5p suppresses cell proliferation and invasion by targeting BUB1 in hepatocellular carcinoma cells. Pharmacology, 2017, 100(5-6), 269-282.
[http://dx.doi.org/10.1159/000477667] [PMID: 28810242]
[36]
Liu, X.; Liao, W.; Yuan, Q.; Ou, Y.; Huang, J. TTK activates Akt and promotes proliferation and migration of hepatocellular carcinoma cells. Oncotarget, 2015, 6(33), 34309-34320.
[http://dx.doi.org/10.18632/oncotarget.5295] [PMID: 26418879]
[37]
Võsa, U.; Kolde, R.; Vilo, J.; Metspalu, A.; Annilo, T. Comprehensive meta-analysis of microRNA expression using a robust rank aggregation approach. Methods Mol. Biol., 2014, 1182, 361-373.
[http://dx.doi.org/10.1007/978-1-4939-1062-5_28] [PMID: 25055923]
[38]
Watanuki, A.; Ohwada, S.; Fukusato, T.; Makita, F.; Yamada, T.; Kikuchi, A.; Morishita, Y. Prognostic significance of DNA topoisomerase IIalpha expression in human hepatocellular carcinoma. Anticancer Res., 2002, 22(2B), 1113-1119.
[PMID: 12168909]
[39]
Zhang, L.; Huang, Y.; Ling, J.; Zhuo, W.; Yu, Z.; Shao, M.; Luo, Y.; Zhu, Y. Screening and function analysis of hub genes and pathways in hepatocellular carcinoma via bioinformatics approaches. Cancer Biomark., 2018, 22(3), 511-521.
[http://dx.doi.org/10.3233/CBM-171160] [PMID: 29843214]
[40]
Xing, T.; Yan, T.; Zhou, Q. Identification of key candidate genes and pathways in hepatocellular carcinoma by integrated bioinformatical analysis. Exp. Ther. Med., 2018, 15(6), 4932-4942.
[http://dx.doi.org/10.3892/etm.2018.6075] [PMID: 29805517]
[41]
Chen, Z.; Chen, J.; Huang, X.; Wu, Y.; Huang, K.; Xu, W.; Xie, L.; Zhang, X.; Liu, H. Identification of potential key genes for hepatitis B Virus-associated hepatocellular carcinoma by bioinformatics analysis. J. Comput. Biol., 2019, 26(5), 485-494.
[http://dx.doi.org/10.1089/cmb.2018.0244] [PMID: 30864827]
[42]
Ni, W.; Zhang, S.; Jiang, B.; Ni, R.; Xiao, M.; Lu, C.; Liu, J.; Qu, L.; Ni, H.; Zhang, W.; Zhou, P. Identification of cancer-related gene network in hepatocellular carcinoma by combined bioinformatic approach and experimental validation. Pathol. Res. Pract., 2019, 215(6)152428
[http://dx.doi.org/10.1016/j.prp.2019.04.020] [PMID: 31064721]
[43]
Wu, M.; Liu, Z.; Li, X.; Zhang, A.; Lin, D.; Li, N. Analysis of potential key genes in very early hepatocellular carcinoma. World J. Surg. Oncol., 2019, 17(1), 77.
[http://dx.doi.org/10.1186/s12957-019-1616-6] [PMID: 31043166]
[44]
Li, C.; Zhou, D.; Jiang, X.; Liu, M.; Tang, H.; Mei, Z. Identifying hepatocellular carcinoma-related hub genes by bioinformatics analysis and CYP2C8 is a potential prognostic biomarker. Gene, 2019, 698, 9-18.
[http://dx.doi.org/10.1016/j.gene.2019.02.062] [PMID: 30825595]
[45]
Wu, M.; Liu, Z.; Zhang, A.; Li, N. Identification of key genes and pathways in hepatocellular carcinoma: A preliminary bioinformatics analysis. Medicine (Baltimore), 2019, 98(5)e14287
[http://dx.doi.org/10.1097/MD.0000000000014287] [PMID: 30702595]
[46]
Jin, B.; Wang, W.; Du, G.; Huang, G.Z.; Han, L.T.; Tang, Z.Y.; Fan, D.G.; Li, J.; Zhang, S.Z. Identifying hub genes and dysregulated pathways in hepatocellular carcinoma. Eur. Rev. Med. Pharmacol. Sci., 2015, 19(4), 592-601.
[PMID: 25753876]
[47]
Zhu, Q.; Sun, Y.; Zhou, Q.; He, Q.; Qian, H. Identification of key genes and pathways by bioinformatics analysis with TCGA RNA sequencing data in hepatocellular carcinoma. Mol. Clin. Oncol., 2018, 9(6), 597-606.
[http://dx.doi.org/10.3892/mco.2018.1728] [PMID: 30546887]
[48]
Zhang, Y.; Wang, S.; Xiao, J.; Zhou, H. Bioinformatics analysis to identify the key genes affecting the progression and prognosis of hepatocellular carcinoma. Biosci. Rep., 2019, 39(2)BSR20181845
[http://dx.doi.org/10.1042/BSR20181845] [PMID: 30705088]
[49]
Li, L.; Lei, Q.; Zhang, S.; Kong, L.; Qin, B. Screening and identification of key biomarkers in hepatocellular carcinoma: Evidence from bioinformatic analysis. Oncol. Rep., 2017, 38(5), 2607-2618.
[http://dx.doi.org/10.3892/or.2017.5946] [PMID: 28901457]

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