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Transcriptional modules related to hepatocellular carcinoma survival: coexpression network analysis |
Xinsen Xu,Yanyan Zhou,Runchen Miao,Wei Chen,Kai Qu,Qing Pang,Chang Liu() |
Department of Hepatobiliary Surgery, the First Affiliated Hospital, School of Medicine, Xi’an Jiaotong University, Xi’an 710061, China |
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Abstract We performed weighted gene coexpression network analysis (WGCNA) to gain insights into the molecular aspects of hepatocellular carcinoma (HCC). Raw microarray datasets (including 488 samples) were downloaded from the Gene Expression Omnibus (GEO) website. Data were normalized using the RMA algorithm. We utilized the WGCNA to identify the coexpressed genes (modules) after non-specific filtering. Correlation and survival analyses were conducted using the modules, and gene ontology (GO) enrichment was applied to explore the possible mechanisms. Eight distinct modules were identified by the WGCNA. Pink and red modules were associated with liver function, whereas turquoise and black modules were inversely correlated with tumor staging. Poor outcomes were found in the low expression group in the turquoise module and in the high expression group in the red module. In addition, GO enrichment analysis suggested that inflammation, immune, virus-related, and interferon-mediated pathways were enriched in the turquoise module. Several potential biomarkers, such as cyclin-dependent kinase 1 (CDK1), topoisomerase 2α (TOP2A), and serpin peptidase inhibitor clade C (antithrombin) member 1 (SERPINC1), were also identified. In conclusion, gene signatures identified from the genome-based assays could contribute to HCC stratification. WGCNA was able to identify significant groups of genes associated with cancer prognosis.
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Keywords
hepatocellular carcinoma
coexpression
module
microarray
prognosis
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Corresponding Author(s):
Chang Liu
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Just Accepted Date: 23 March 2016
Online First Date: 05 April 2016
Issue Date: 27 May 2016
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1 |
Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A. Global cancer statistics, 2012. CA Cancer J Clin 2015; 65(2): 87–108
https://doi.org/10.3322/caac.21262
pmid: 25651787
|
2 |
Maluccio M, Covey A. Recent progress in understanding, diagnosing, and treating hepatocellular carcinoma. CA Cancer J Clin 2012; 62(6): 394–399
https://doi.org/10.3322/caac.21161
pmid: 23070690
|
3 |
Jeng KS, Chang CF, Jeng WJ, Sheen IS, Jeng CJ. Heterogeneity of hepatocellular carcinoma contributes to cancer progression. Crit Rev Oncol Hematol 2015; 94(3): 337–347
https://doi.org/10.1016/j.critrevonc.2015.01.009
pmid: 25680939
|
4 |
Mínguez B, Hoshida Y, Villanueva A, Toffanin S, Cabellos L, Thung S, Mandeli J, Sia D, April C, Fan JB, Lachenmayer A, Savic R, Roayaie S, Mazzaferro V, Bruix J, Schwartz M, Friedman SL, Llovet JM. Gene-expression signature of vascular invasion in hepatocellular carcinoma. J Hepatol 2011; 55(6): 1325–1331
https://doi.org/10.1016/j.jhep.2011.02.034
pmid: 21703203
|
5 |
Yu GR, Kim SH, Park SH, Cui XD, Xu DY, Yu HC, Cho BH, Yeom YI, Kim SS, Kim SB, Chu IS, Kim DG. Identification of molecular markers for the oncogenic differentiation of hepatocellular carcinoma. Exp Mol Med 2007; 39(5): 641–652
https://doi.org/10.1038/emm.2007.70
pmid: 18059140
|
6 |
Roessler S, Jia HL, Budhu A, Forgues M, Ye QH, Lee JS, Thorgeirsson SS, Sun Z, Tang ZY, Qin LX, Wang XW. A unique metastasis gene signature enables prediction of tumor relapse in early-stage hepatocellular carcinoma patients. Cancer Res 2010; 70(24): 10202–10212
https://doi.org/10.1158/0008-5472.CAN-10-2607
pmid: 21159642
|
7 |
Woo HG, Park ES, Cheon JH, Kim JH, Lee JS, Park BJ, Kim W, Park SC, Chung YJ, Kim BG, Yoon JH, Lee HS, Kim CY, Yi NJ, Suh KS, Lee KU, Chu IS, Roskams T, Thorgeirsson SS, Kim YJ. Gene expression-based recurrence prediction of hepatitis B virus-related human hepatocellular carcinoma. Clin Cancer Res 2008; 14(7): 2056–2064
https://doi.org/10.1158/1078-0432.CCR-07-1473
pmid: 18381945
|
8 |
Ping Y, Deng Y, Wang L, Zhang H, Zhang Y, Xu C, Zhao H, Fan H, Yu F, Xiao Y, Li X. Identifying core gene modules in glioblastoma based on multilayer factor-mediated dysfunctional regulatory networks through integrating multi-dimensional genomic data. Nucleic Acids Res 2015; 43(4): 1997–2007
https://doi.org/10.1093/nar/gkv074
pmid: 25653168
|
9 |
Oldham MC, Konopka G, Iwamoto K, Langfelder P, Kato T, Horvath S, Geschwind DH. Functional organization of the transcriptome in human brain. Nat Neurosci 2008; 11(11): 1271–1282
https://doi.org/10.1038/nn.2207
pmid: 18849986
|
10 |
Liu ZP. Reverse engineering of genome-wide gene regulatory networks from gene expression data. Curr Genomics 2015; 16(1): 3–22
https://doi.org/10.2174/1389202915666141110210634
pmid: 25937810
|
11 |
Zhang B, Horvath S. A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol 2005; 4: Article17
|
12 |
Horvath S, Zhang B, Carlson M, Lu KV, Zhu S, Felciano RM, Laurance MF, Zhao W, Qi S, Chen Z, Lee Y, Scheck AC, Liau LM, Wu H, Geschwind DH, Febbo PG, Kornblum HI, Cloughesy TF, Nelson SF, Mischel PS. Analysis of oncogenic signaling networks in glioblastoma identifies ASPM as a molecular target. Proc Natl Acad Sci USA 2006; 103(46): 17402–17407
https://doi.org/10.1073/pnas.0608396103
pmid: 17090670
|
13 |
Ignatiadis M, Singhal SK, Desmedt C, Haibe-Kains B, Criscitiello C, Andre F, Loi S, Piccart M, Michiels S, Sotiriou C. Gene modules and response to neoadjuvant chemotherapy in breast cancer subtypes: a pooled analysis. J Clin Oncol 2012; 30(16): 1996–2004
https://doi.org/10.1200/JCO.2011.39.5624
pmid: 22508827
|
14 |
Liang Y, Diehn M, Watson N, Bollen AW, Aldape KD, Nicholas MK, Lamborn KR, Berger MS, Botstein D, Brown PO, Israel MA. Gene expression profiling reveals molecularly and clinically distinct subtypes of glioblastoma multiforme. Proc Natl Acad Sci USA 2005; 102(16): 5814–5819
https://doi.org/10.1073/pnas.0402870102
pmid: 15827123
|
15 |
He D, Liu ZP, Honda M, Kaneko S, Chen L. Coexpression network analysis in chronic hepatitis B and C hepatic lesions reveals distinct patterns of disease progression to hepatocellular carcinoma. J Mol Cell Biol 2012; 4(3): 140–152
https://doi.org/10.1093/jmcb/mjs011
pmid: 22467683
|
16 |
Ivliev AE, 't Hoen PA, Sergeeva MG. Coexpression network analysis identifies transcriptional modules related to proastrocytic differentiation and sprouty signaling in glioma. Cancer Res 2010; 70(24): 10060–10070
https://doi.org/10.1158/0008-5472.CAN-10-2465
pmid: 21159630
|
17 |
Clarke C, Madden SF, Doolan P, Aherne ST, Joyce H, O’Driscoll L, Gallagher WM, Hennessy BT, Moriarty M, Crown J, Kennedy S, Clynes M. Correlating transcriptional networks to breast cancer survival: a large-scale coexpression analysis. Carcinogenesis 2013; 34(10): 2300–2308
https://doi.org/10.1093/carcin/bgt208
pmid: 23740839
|
18 |
Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 2008; 9(1): 559
https://doi.org/10.1186/1471-2105-9-559
pmid: 19114008
|
19 |
Taminau J, Meganck S, Lazar C, Steenhoff D, Coletta A, Molter C, Duque R, de Schaetzen V, Weiss Solís DY, Bersini H, Nowé A. Unlocking the potential of publicly available microarray data using inSilicoDb and inSilicoMerging R/Bioconductor packages. BMC Bioinformatics 2012; 13(1): 335
https://doi.org/10.1186/1471-2105-13-335
pmid: 23259851
|
20 |
Langfelder P, Zhang B, Horvath S. Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R. Bioinformatics 2008; 24(5): 719–720
https://doi.org/10.1093/bioinformatics/btm563
pmid: 18024473
|
21 |
Wang J, Huang Q, Liu ZP, Wang Y, Wu LY, Chen L, Zhang XS. NOA: a novel Network Ontology Analysis method. Nucleic Acids Res 2011; 39(13): e87
https://doi.org/10.1093/nar/gkr251
pmid: 21543451
|
22 |
Hu Z, Snitkin ES, DeLisi C. VisANT: an integrative framework for networks in systems biology. Brief Bioinform 2008; 9(4): 317–325
https://doi.org/10.1093/bib/bbn020
pmid: 18463131
|
23 |
Villanueva A, Minguez B, Forner A, Reig M, Llovet JM. Hepatocellular carcinoma: novel molecular approaches for diagnosis, prognosis, and therapy. Annu Rev Med 2010; 61(1): 317–328
https://doi.org/10.1146/annurev.med.080608.100623
pmid: 20059340
|
24 |
Iizuka N, Oka M, Yamada-Okabe H, Nishida M, Maeda Y, Mori N, Takao T, Tamesa T, Tangoku A, Tabuchi H, Hamada K, Nakayama H, Ishitsuka H, Miyamoto T, Hirabayashi A, Uchimura S, Hamamoto Y. Oligonucleotide microarray for prediction of early intrahepatic recurrence of hepatocellular carcinoma after curative resection. Lancet 2003; 361(9361): 923–929
https://doi.org/10.1016/S0140-6736(03)12775-4
pmid: 12648972
|
25 |
Kurokawa Y, Matoba R, Takemasa I, Nagano H, Dono K, Nakamori S, Umeshita K, Sakon M, Ueno N, Oba S, Ishii S, Kato K, Monden M. Molecular-based prediction of early recurrence in hepatocellular carcinoma. J Hepatol 2004; 41(2): 284–291
https://doi.org/10.1016/j.jhep.2004.04.031
pmid: 15288478
|
26 |
Singal AK, Salameh H, Kuo YF, Fontana RJ. Meta-analysis: the impact of oral anti-viral agents on the incidence of hepatocellular carcinoma in chronic hepatitis B. Aliment Pharmacol Ther 2013; 38(2): 98–106
https://doi.org/10.1111/apt.12344
pmid: 23713520
|
27 |
Utsunomiya T, Shimada M, Kudo M, Ichida T, Matsui O, Izumi N, Matsuyama Y, Sakamoto M, Nakashima O, Ku Y, Takayama T, Kokudo N; Liver Cancer Study Group of Japan.A comparison of the surgical outcomes among patients with HBV-positive, HCV-positive, and non-B non-C hepatocellular carcinoma: a nationwide study of 11,950 patients. Ann Surg 2015; 261(3): 513–520 PMID:25072437
https://doi.org/10.1097/SLA.0000000000000821
|
28 |
Asghar U, Witkiewicz AK, Turner NC, Knudsen ES. The history and future of targeting cyclin-dependent kinases in cancer therapy. Nat Rev Drug Discov 2015; 14(2): 130–146
https://doi.org/10.1038/nrd4504
pmid: 25633797
|
29 |
Panvichian R, Tantiwetrueangdet A, Angkathunyakul N, Leelaudomlipi S. TOP2A amplification and overexpression in hepatocellular carcinoma tissues. BioMed Res Int 2015; 2015: 381602
https://doi.org/10.1155/2015/381602
pmid: 25695068
|
30 |
Iwako H, Tashiro H, Amano H, Tanimoto Y, Oshita A, Kobayashi T, Kuroda S, Tazawa H, Nambu J, Mikuriya Y, Abe T, Ohdan H. Prognostic significance of antithrombin III levels for outcomes in patients with hepatocellular carcinoma after curative hepatectomy. Ann Surg Oncol 2012; 19(9): 2888–2896
https://doi.org/10.1245/s10434-012-2338-y
pmid: 22466667
|
31 |
Larsson H, Sjöblom T, Dixelius J, Ostman A, Ylinenjärvi K, Björk I, Claesson-Welsh L. Antiangiogenic effects of latent antithrombin through perturbed cell-matrix interactions and apoptosis of endothelial cells. Cancer Res 2000; 60(23): 6723–6729
pmid: 11118058
|
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