Please wait a minute...
Frontiers of Medicine

ISSN 2095-0217

ISSN 2095-0225(Online)

CN 11-5983/R

Postal Subscription Code 80-967

2018 Impact Factor: 1.847

Front. Med.    2016, Vol. 10 Issue (2) : 183-190     DOI: 10.1007/s11684-016-0440-4
RESEARCH ARTICLE |
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
Download: PDF(794 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
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.

Keywords hepatocellular carcinoma      coexpression      module      microarray      prognosis     
Corresponding Authors: Chang Liu   
Just Accepted Date: 23 March 2016   Online First Date: 05 April 2016    Issue Date: 27 May 2016
URL:  
http://academic.hep.com.cn/fmd/EN/10.1007/s11684-016-0440-4     OR     http://academic.hep.com.cn/fmd/EN/Y2016/V10/I2/183
Fig.1  Flow chart for the coexpression network analysis.
Fig.2  Network analysis of gene expression in hepatocellular carcinoma (HCC) identified modules of coexpressed genes. (A) The dendrogram was produced by unsupervised hierarchical clustering of genes on the basis of topological overlap. (B) We calculated their eigengenes and clustered them according to their correlation to quantify the coexpression similarity of entire modules. We chose the height cut of 0.4, resulting in eight merged coexpressed modules.
Fig.3  Correlation analysis of the identified modules with clinicopathological variables of hepatocellular carcinoma. Pearson’s correlation coefficient between modules and clinicopathological variables are shown, accompanied by the corresponding P value in brackets.
Overall survival Tumor-free survival
HR CI P value HR CI P value
ME turquoise 0.487 0.321–0.738 <0.001 0.649 0.462–0.912 0.013
ME pink 1.362 0.910–2.040 0.133 1.210 0.864–1.695 0.267
ME red 1.445 0.963–2.169 0.076 1.461 1.039–2.053 0.029
ME magenta 1.046 0.701–1.560 0.827 0.975 0.697–1.365 0.883
ME gray 0.879 0.588–1.312 0.527 0.947 0.677–1.326 0.753
ME brown 1.131 0.758–1.688 0.547 1.087 0.776–1.521 0.628
ME blue 1.241 0.830–1.856 0.293 1.105 0.789–1.548 0.561
ME black 0.766 0.513–1.144 0.192 0.851 0.608–1.191 0.347
Tab.1  Correlation between gene coexpression modules and survival time
Fig.4  Survival analysis based on the gene expression pattern in the turquoise module. Impacts of the turquoise module expression on the (A) overall survival and (B) tumor-free survival. (C) GO enrichment analysis for the 1046 probe sets included in the turquoise module. The original significance were transformed to “−log10(FDR)” to plot the curve. (D) Visualization of the network connections among the most connected genes in the turquoise module, generated by the VisANT software.
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
doi: 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
doi: 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
doi: 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
doi: 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
doi: 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
doi: 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
doi: 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
doi: 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
doi: 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
doi: 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
doi: 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
doi: 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
doi: 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
doi: 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
doi: 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
doi: 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
doi: 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
doi: 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
doi: 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
doi: 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
doi: 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
doi: 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
doi: 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
doi: 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
doi: 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
doi: 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
doi: 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
doi: 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
doi: 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
[1] Zhi Xu,Chunxiang Cao,Haiyan Xia,Shujing Shi,Lingzhi Hong,Xiaowei Wei,Dongying Gu,Jianmin Bian,Zijun Liu,Wenbin Huang,Yixin Zhang,Song He,Nikki Pui-Yue Lee,Jinfei Chen. Protein phosphatase magnesium-dependent 1δ is a novel tumor marker and target in hepatocellular carcinoma[J]. Front. Med., 2016, 10(1): 52-60.
[2] Aixiu Qiao,Feng Gu,Xiaojing Guo,Xinmin Zhang,Li Fu. Breast cancer-associated fibroblasts: their roles in tumor initiation, progression and clinical applications[J]. Front. Med., 2016, 10(1): 33-40.
[3] Jing Zhang,Shan Gao,Zhongping Duan,Ke-Qin Hu. Overview on acute-on-chronic liver failure[J]. Front. Med., 2016, 10(1): 1-17.
[4] Felice Ho-Ching Tsang,Sandy Leung-Kuen Au,Lai Wei,Dorothy Ngo-Yin Fan,Joyce Man-Fong Lee,Carmen Chak-Lui Wong,Irene Oi-Lin Ng,Chun-Ming Wong. MicroRNA-142-3p and microRNA-142-5p are downregulated in hepatocellular carcinoma and exhibit synergistic effects on cell motility[J]. Front. Med., 2015, 9(3): 331-343.
[5] Farhad Sahebjam,John M. Vierling. Autoimmune hepatitis[J]. Front. Med., 2015, 9(2): 187-219.
[6] Guanghua Rong,Wenlin Bai,Zheng Dong,Chunping Wang,Yinying Lu,Zhen Zeng,Jianhui Qu,Min Lou,Hong Wang,Xudong Gao,Xiujuan Chang,Linjing An,Yan Chen,Yongping Yang. Cryotherapy for cirrhosis-based hepatocellular carcinoma: a single center experience from 1595 treated cases[J]. Front. Med., 2015, 9(1): 63-71.
[7] Marielle Reataza,David K. Imagawa. Advances in managing hepatocellular carcinoma[J]. Front. Med., 2014, 8(2): 175-189.
[8] Kai Qu,Ting Lin,Zhixin Wang,Sinan Liu,Hulin Chang,Xinsen Xu,Fandi Meng,Lei Zhou,Jichao Wei,Minghui Tai,Yafeng Dong,Chang Liu. Reactive oxygen species generation is essential for cisplatin-induced accelerated senescence in hepatocellular carcinoma[J]. Front. Med., 2014, 8(2): 227-235.
[9] Du Yan, Han Xue, Pu Rui, Xie Jiaxin, Zhang Yuwei, Cao Guangwen. Association of miRNA-122-binding site polymorphism at the interleukin-1 α gene and its interaction with hepatitis B virus mutations with hepatocellular carcinoma risk[J]. Front. Med., 2014, 8(2): 217-226.
[10] Carmen Chak-Lui Wong, Alan Ka-Lun Kai, Irene Oi-Lin Ng. The impact of hypoxia in hepatocellular carcinoma metastasis[J]. Front Med, 2014, 8(1): 33-41.
[11] Lunxiu Qin. Osteopontin is a promoter for hepatocellular carcinoma metastasis: a summary of 10 years of studies[J]. Front Med, 2014, 8(1): 24-32.
[12] Ting Lin, Kai Qu, Xinsen Xu, Min Tian, Jie Gao, Chun Zhang, Ying Di, Yuelang Zhang, Chang Liu. Sclerosing cholangitis in critically ill patients: an important and easily ignored problem based on a German experience[J]. Front Med, 2014, 8(1): 118-126.
[13] Lu Wang. Identification of cancer gene fusions based on advanced analysis of the human genome or transcriptome[J]. Front Med, 2013, 7(3): 280-289.
[14] Beicheng Sun, Michael Karin. Inflammation and liver tumorigenesis[J]. Front Med, 2013, 7(2): 242-254.
[15] Meilin Ma, Xiang Wang, Jingyan Tang, Huiliang Xue, Jing Chen, Ci Pan, Hua Jiang, Shuhong Shen. Early T-cell precursor leukemia: a subtype of high risk childhood acute lymphoblastic leukemia[J]. Front Med, 2012, 6(4): 416-420.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed