Pathology & Oncology Research

, Volume 24, Issue 3, pp 507–514 | Cite as

Identification of Potential Gene Network Associated with HCV-Related Hepatocellular Carcinoma Using Microarray Analysis

  • Yang Cheng
  • Jian Ping
  • Jianjie ChenEmail author
Original Article


In order to identify potential specific gene networks of Hepatitis C virus (HCV) related hepatocellular carcinoma (HCC), weighted gene co-expression network analysis (WGCNA) was performed, which may provide an insight into the potential mechanism of the HCC development. HCV-related HCC and normal sample data were downloaded from GEO, T test of limma package was used to screen different expression genes (DEGs); KEGG pathway was used to analyze related biochemical pathways, and WGCNA was used to construct clustering trees and screen hub genes in the HCC-specific modules. A total of 1151 DEGs were authenticated between the HCC and normal liver tissue samples, including 433 upregulated and 718 downregulated genes. Among these genes, three specific modules of HCC were constructed, including Tan, Yellow and Cyan, but only Yellow module had a significant enrichment score in substance combination module with three hub genes: SLA2547, EFNA4 and MME. Although Tan and Cyan separately had four and three hub genes, but the bio-functions of them did not have significant enrichment scores (score < 2). SLA2547, EFNA4 and MME may play important roles in the substance combination of HCV-related HCC, so studying the function of this gene network may provide us a deeper understanding of HCV-related HCC.


Hepatitis C virus Hepatocelluar carcinoma Kyoto encyclopedia of gene and genomes pathway Weighted gene co-expression network analysis Differentially expressed gene 



This work is funded by grants from Outstanding Leaders Training Program of Pudong Health Bureau of Shanghai (PWRL2016-01), three-year plan of action of traditional Chinese medicine in Shanghai (ZY3-RCPY-1-1001 and ZY3-JSFC-1-1011), Science and Technology Commission of Pudong New Area Shanghai (PKJ2014-Y37), Prof. Jian-jie Chen Studio (Shanghai Legendary Medical Practitioner of Traditional Chinese Medicine, ZYSNXD-CC-MZY003).

Compliance with Ethical Standards

Competing Interests

The authors declare that they have no competing interests.


  1. 1.
    Jin B, Wang W, Du G, Huang GZ, Han LT, Tang ZY et al (2015) Identifying hub genes and dysregulated pathways in hepatocellular carcinoma. Eur Rev Med Pharmacol Sci 19:592–601PubMedGoogle Scholar
  2. 2.
    Luna J, Scheel TH, Danino T, Shaw K, Mele A, Fak J et al (2015) Hepatitis C virus RNA functionally sequesters miR-122. Cell 160:1099–1110CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Castello G, Scala S, Palmieri G, Curley SA, Izzo F (2010) HCV-related hepatocellular carcinoma: from chronic inflammation to cancer. Clin Immunol 134:237–250CrossRefPubMedGoogle Scholar
  4. 4.
    Petrizzo A, Caruso FP, Tagliamonte M, Tornesello ML, Ceccarelli M, Costa V et al (2016) Identification and validation of HCC-specific Gene transcriptional signature for tumor antigen discovery. Sci Rep 6:29258CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Kakehashi A, Ishii N, Sugihara E, Min G, Saya H, Wanibuchi H (2016) CD44 variant 9 is a potential biomarker of tumor initiating cells predicting survival outcome in hepatitis C virus-positive patients with resected hepatocellular carcinoma. Cancer Sci 107:609–618CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Boj S, Vanes J, Huch M, Li VW, José A, Hatzis P et al (2012) Diabetes risk gene and Wnt effector Tcf7l2/TCF4 controls hepatic response to perinatal and adult metabolic demand. Cell 151:1595–1607CrossRefPubMedGoogle Scholar
  7. 7.
    Park SH, Rehermann B (2014) Immune responses to HCV and other hepatitis viruses. Immunity 40:13–24CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Syed GH, Amako Y, Siddiqui A (2009) Hepatitis C virus hijacks host lipid metabolism. Trends Endocrinol Metab 21:33–40CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Lambert MP, Paliwal A, Vaissière T, Chemin I, Zoulim F, Tommasino M et al (2011) Aberrant DNA methylation distinguishes hepatocellular carcinoma associated with HBV and HCV infection and alcohol intake. J Hepatol 54:705–715CrossRefPubMedGoogle Scholar
  10. 10.
    Hodo Y, Honda M, Tanaka A, Nomura Y, Arai K, Yamashita T et al (2013) Association of interleukin-28B genotype and hepatocellular carcinoma recurrence in patients with chronic hepatitis C. Clin Cancer Res 19:1827–1837CrossRefPubMedGoogle Scholar
  11. 11.
    Zhang J, Baddoo M, Han C, Strong MJ, Cvitanovic J, Moroz K et al (2016) Gene network analysis reveals a novel 22-gene signature of carbon metabolism in hepatocellular carcinoma. Oncotarget 7:49232–49245PubMedPubMedCentralGoogle Scholar
  12. 12.
    Xu Y, Cui J, Puett D (2014) Understanding cancer invasion and metastasis. Springer, New YorkCrossRefGoogle Scholar
  13. 13.
    Ueda T, Honda M, Horimoto K, Aburatani S, Saito S, Yamashita T et al (2013) Gene expression profiling of hepatitis B- and hepatitis C-related hepatocellular carcinoma using graphical Gaussian modeling ☆. Genomics 101:238–248CrossRefPubMedGoogle Scholar
  14. 14.
    † SC, † PSS, Lee J, Park J, Shin EC, Choi C (2015) Prolonged silencing of diacylglycerol acyltransferase-1 induces a dedifferentiated phenotype in human liver cells. J Cell Mol Med 20:38–47Google Scholar
  15. 15.
    Irizarry RA, Hobbs B, Collin F, Beazerbarclay YD, Antonellis KJ, Scherf U et al (2003) Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4:249–264CrossRefPubMedGoogle Scholar
  16. 16.
    Diboun I, Wernisch L, Orengo CA, Koltzenburg M (2006) Microarray analysis after RNA amplification can detect pronounced differences in gene expression using limma. BMC Genomics 7:1–14CrossRefGoogle Scholar
  17. 17.
    Wixon J, Kell D (2000) The Kyoto encyclopedia of genes and genomes--KEGG. Yeast 17:48–55CrossRefPubMedGoogle Scholar
  18. 18.
    Huang DW, Sherman BT, Lempicki RA (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature Protocol 4:44–57CrossRefGoogle Scholar
  19. 19.
    Langfelder P, Horvath S (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinf 9:1–13CrossRefGoogle Scholar
  20. 20.
    Bushkofsky JR, Maguire M, Larsen MC, Fong YH, Jefcoate CR (2016) Cyp1b1 affects external control of mouse hepatocytes, fatty acid homeostasis and signaling involving HNF4α and PPARα. Arch Biochem Biophys 597:30–47CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Lamming DW, Demirkan G, Boylan JM, Mihaylova MM, Peng T, Ferreira J et al (2014) Hepatic signaling by the mechanistic target of rapamycin complex 2 (mTORC2). Faseb Journal Official Publication of the Federation of American Societies for Experimental Biology 28:300–315CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Archer KJ, Mas VR, Maluf DG, Fisher RA (2010) High-throughput assessment of CpG site methylation for distinguishing between HCV-cirrhosis and HCV-associated hepatocellular carcinoma. Molecular Genetics & Genomics Mgg 283:341–349CrossRefPubMedGoogle Scholar
  23. 23.
    Tong J, Xie J, Ren H, Liu J, Zhang W, Wei C et al (2015) Comparison of glomerular Transcriptome profiles of adult-onset steroid sensitive focal segmental Glomerulosclerosis and minimal change disease. PLoS One 10:e0140453CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Arvanitis DN, Davy A (1900) Regulation and misregulation of Eph/ephrin expression. Cell Adhes Migr 6:131–137CrossRefGoogle Scholar
  25. 25.
    Saintigny P, Peng S, Zhang L, Sen B, Wistuba II, Lippman SM et al (2012) Global evaluation of Eph receptors and Ephrins in lung adenocarcinomas identifies EphA4 as an inhibitor of cell migration and invasion. Mol Cancer Ther 11:2021–2032CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Abdou AG, Abd el-Wahed MM, Asaad NY, Samaka RM, Abdallaha R (2010) Ephrin A4 expression in osteosarcoma, impact on prognosis, and patient outcome. Indian J Cancer 47:46–52CrossRefPubMedGoogle Scholar
  27. 27.
    Hernaez R (2011) Genetic factors associated with the presence and progression of nonalcoholic fatty liver disease: a narrative review. Gastroenterol Hepatol 35:32–41CrossRefPubMedGoogle Scholar
  28. 28.
    Sanchez-Antolín G, Almohalla-Alvarez C, Bueno P, Almansa R, Iglesias V, Rico L et al (2015) Evidence of active pro-fibrotic response in blood of patients with cirrhosis. PLoS One 10:e0137128CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    English DP, Santin AD (2012) Claudins overexpression in ovarian cancer: potential targets for Clostridium perfringens enterotoxin (CPE) based diagnosis and therapy. Int J Mol Sci 14:10412–10437CrossRefGoogle Scholar
  30. 30.
    Lin H, Liu W, Fang Z, Liang X, Li J, Bai Y et al (2015) Overexpression of DHX32 contributes to the growth and metastasis of colorectal cancer. Sci Rep 5:9247CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Kim KH, Lee MS (2014) Autophagy - a key player in cellular and body metabolism. Nat Rev Endocrinol 10:322–337CrossRefPubMedGoogle Scholar
  32. 32.
    Sun T, Yi H, Yang C, Kishnani PS, Sun B (2016) Starch binding domain-containing protein 1 plays a dominant role in glycogen transport to lysosomes in liver. J Biol Chem 291:16479–16484CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Arányi Lajos Foundation 2017

Authors and Affiliations

  1. 1.Department of Liver DiseaseHospital for Infectious Diseases of Pudong New AreaShanghaiPeople’s Republic of China
  2. 2.Shuguang Hospital affiliated to Shanghai University of Traditional Chinese MedicineShanghaiPeople’s Republic of China

Personalised recommendations