Transcriptional modules related to hepatocellular carcinoma survival: coexpression network analysis

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

References

[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

[2] Maluccio M, Covey A. Recent progress in understanding, diagnosing, and treating hepatocellular carcinoma. CA Cancer J Clin 2012; 62(6): 394–399

[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

[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

[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

[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

[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

[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

[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

[10] Liu ZP. Reverse engineering of genome-wide gene regulatory networks from gene expression data. Curr Genomics 2015; 16(1): 3–22

[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

[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

[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

[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

[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

[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

[18] Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 2008; 9(1): 559

[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

[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

[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

[22] Hu Z, Snitkin ES, DeLisi C. VisANT: an integrative framework for networks in systems biology. Brief Bioinform 2008; 9(4): 317–325

[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

[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

[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

[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

[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): 513–520PMID:25072437

[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

[29] Panvichian R, Tantiwetrueangdet A, Angkathunyakul N, Leelaudomlipi S. TOP2A amplification and overexpression in hepatocellular carcinoma tissues. BioMed Res Int 2015; 2015: 381602

[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

[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

Acknowledgements

This manuscript was supported by the National Natural Science Foundation of China (No. 81402022) and the Fund of China Scholarship Council (No. 201406280106).

Compliance with ethics guidelines

Xinsen Xu, Yanyan Zhou, Runchen Miao, Wei Chen, Kai Qu, Qing Pang, and Chang Liu declare that they have no conflict of interest. We state that the protocol for the research project has been approved by a suitably constituted Ethics Committee of the institution and that it conforms to the provisions of the Declaration of Helsinki.

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2016 Higher Education Press and Springer-Verlag Berlin Heidelberg