Microarray analysis for the identification of specific proteins and functional modules involved in the process of hepatocellular carcinoma originating from cirrhotic liver

Fan,Wufeng; Ye,Guangming

doi:10.3892/mmr.2018.8555

Microarray analysis for the identification of specific proteins and functional modules involved in the process of hepatocellular carcinoma originating from cirrhotic liver

Authors:
- Wufeng Fan
- Guangming Ye
View Affiliations

Affiliations: Section of Medical Affairs, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, Hubei 441000, P.R. China, Center for Gene Diagnosis, Zhongnan Hospital of Wuhan University, Wuhan, Hubei 430071, P.R. China
Published online on: February 2, 2018 https://doi.org/10.3892/mmr.2018.8555
Pages: 5619-5626
Copyright: © Fan et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )

Metrics: Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )

Cited By (CrossRef): 0 citations Loading Articles...

Abstract

In order to identify the potential pathogenesis of hepatocellular carcinoma (HCC) developing from cirrhosis, a microarray‑based transcriptome profile was analyzed. The GSE63898 expression profile was downloaded from the Gene Expression Omnibus database, which included data from 228 HCC tissue samples and 168 cirrhotic tissue samples. The Robust Multi‑array Average in the Affy package of R was used for raw data processing and Student's t‑test was used to screen differentially expressed genes (DEGs). An enrichment analysis was then conducted using the Database for Annotation, Visualization and Integrated Discovery online tool, and the protein‑protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes and Cytoscape. Furthermore, the MCODE plug‑in of Cytoscape was used to conduct a sub‑module analysis. A total of 634 DEGs were identified between HCC and cirrhosis, of which 165 were upregulated and 469 were downregulated. According to the cut‑off criteria, the PPI network was constructed and Jun proto‑oncogene, AP‑1 transcription factor subunit (degree, 39), Fos proto‑oncogene, AP‑1 transcription factor subunit (degree, 34) and v‑myc avian myelocytomatosis viral oncogene homolog (degree, 32) were identified as the hub nodes of the PPI network. Based on the sub‑module analysis, four specific modules were identified. In particular, module 1 was significantly enriched in the chemokine signaling pathway, and C‑X‑C motif chemokine ligand 12, C‑C motif chemokine receptor 7 (CCR7) and C‑C motif chemokine ligand 5 (CCL5) were three important proteins in this module. Module 4 was significantly enriched in chemical carcinogenesis, and cytochrome P450 family 2 subfamily E member 1, cytochrome P450 family 2 subfamily C member 9 (CYP2C9) and cytochrome P450 family 2 subfamily A member 6 (CYP2A6) were three important proteins in this module. In conclusion, the present study revealed that CCR7, CCL5, CYP2C9 and CYP2A6 are novel genes identified in the development of HCC; however, the actual functions of these genes require verification.

View Figures

View References

1	Siegel RL, Miller KD and Jemal A: Cancer statistics, 2016. CA Cancer J Clin. 66:7–30. 2016. View Article : Google Scholar : PubMed/NCBI
2	Fan Q, He M, Deng X, Wu WK, Zhao L, Tang J, Wen G, Sun X and Liu Y: Derepression of c-Fos caused by microRNA-139 down-regulation contributes to the metastasis of human hepatocellular carcinoma. Cell Biochem Funct. 31:319–324. 2013. View Article : Google Scholar : PubMed/NCBI
3	Archambeaud I, Auble H, Nahon P, Planche L, Fallot G, Faroux R, Gournay J, Samuel D, Kury S and Féray C: Risk factors for hepatocellular carcinoma in caucasian patients with non-viral cirrhosis: The importance of prior obesity. Liver Int. 35:1872–1876. 2015. View Article : Google Scholar : PubMed/NCBI
4	Saunders D, Seidel D, Allison M and Lyratzopoulos G: Systematic review: The association between obesity and hepatocellular carcinoma-epidemiological evidence. Aliment Pharmacol Ther. 31:1051–1063. 2010.PubMed/NCBI
5	Naveau S: Body mass index and risk of liver cirrhosis in middle aged UK women: prospective study. Gastroentérol Clin Biol. 34:429–430. 2010. View Article : Google Scholar
6	Zhang DY and Friedman SL: Fibrosis-dependent mechanisms of hepatocarcinogenesis. Hepatology. 56:769–775. 2012. View Article : Google Scholar : PubMed/NCBI
7	Su TH, Kao JH and Liu CJ: Molecular mechanism and treatment of viral hepatitis-related liver fibrosis. Int J Mol Sci. 15:10578–10604. 2014. View Article : Google Scholar : PubMed/NCBI
8	Friedman SL: Hepatic stellate cells: Protean, multifunctional, and enigmatic cells of the liver. Physiol Rev. 88:125–172. 2008. View Article : Google Scholar : PubMed/NCBI
9	De Minicis S, Seki E, Uchinami H, Kluwe J, Zhang Y, Brenner DA and Schwabe RF: Gene expression profiles during hepatic stellate cell activation in culture and in vivo. Gastroenterology. 132:1937–1946. 2007. View Article : Google Scholar : PubMed/NCBI
10	Liu WT, Jing YY, Yu GF, Han ZP, Yu DD, Fan QM, Ye F, Li R, Gao L, Zhao QD, et al: Toll like receptor 4 facilitates invasion and migration as a cancer stem cell marker in hepatocellular carcinoma. Cancer Lett. 358:136–143. 2014. View Article : Google Scholar : PubMed/NCBI
11	Lee SK, Kim MH, Cheong JY, Cho SW, Yang SJ and Kwack K: Integrin alpha V polymorphisms and haplotypes in a Korean population are associated with susceptibility to chronic hepatitis and hepatocellular carcinoma. Liver Int. 29:187–195. 2009. View Article : Google Scholar : PubMed/NCBI
12	Fu BH, Wu ZZ and Qin J: Effects of integrins on laminin chemotaxis by hepatocellular carcinoma cells. Mol Biol Rep. 37:1665–1670. 2010. View Article : Google Scholar : PubMed/NCBI
13	Vlodavsky I, Miao HQ, Medalion B, Danagher P and Ron D: Involvement of heparan sulfate and related molecules in sequestration and growth promoting activity of fibroblast growth factor. Cancer Metastasis Rev. 15:177–186. 1996. View Article : Google Scholar : PubMed/NCBI
14	Villanueva A, Portela A, Sayols S, Battiston C, Hoshida Y, Méndez-González J, Imbeaud S, Letouzé E, Hernandez-Gea V, Cornella H, et al: DNA methylation-based prognosis and epidrivers in hepatocellular carcinoma. Hepatology. 61:1945–1956. 2015. View Article : Google Scholar : PubMed/NCBI
15	Bolstad BM, Irizarry RA, Astrand M and Speed TP: A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics. 19:185–193. 2003. View Article : Google Scholar : PubMed/NCBI
16	Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U and Speed TP: Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics. 4:249–264. 2003. View Article : Google Scholar : PubMed/NCBI
17	Gautier L, Cope L, Bolstad BM and Irizarry RA: Affy-analysis of Affymetrix GeneChip data at the probe level. Bioinformatics. 20:307–315. 2004. View Article : Google Scholar : PubMed/NCBI
18	Smyth GK: limma: Linear models for microarray data. Springer; New York: pp. 397–420. 2005
19	Benjamini Y and Hochberg Y: Controlling the false discovery rate: A practical and powerful approach to multiple testing. J R Stat Soc. 57:289–300. 1995.
20	Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, et al: Gene ontology: Tool for the unification of biology. The gene ontology consortium. Nat Genet. 25:25–29. 2000. View Article : Google Scholar : PubMed/NCBI
21	Kanehisa M and Goto S: KEGG: Κyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28:27–30. 2000. View Article : Google Scholar : PubMed/NCBI
22	Huang DW, Sherman BT, Tan Q, Collins JR, Alvord WG, Roayaei J, Stephens R, Baseler MW, Lane HC and Lempicki RA: The DAVID gene functional classification tool: A novel biological module-centric algorithm to functionally analyze large gene lists. Genome Biol. 8:R1832007. View Article : Google Scholar : PubMed/NCBI
23	von Mering C, Huynen M, Jaeggi D, Schmidt S, Bork P and Snel B: STRING: A database of predicted functional associations between proteins. Nucleic Acids Res. 31:258–261. 2003. View Article : Google Scholar : PubMed/NCBI
24	Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B and Ideker T: Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 13:2498–2504. 2003. View Article : Google Scholar : PubMed/NCBI
25	Tang Y, Li M, Wang J, Pan Y and Wu FX: CytoNCA: A cytoscape plugin for centrality analysis and evaluation of protein interaction networks. Biosystems. 127:67–72. 2015. View Article : Google Scholar : PubMed/NCBI
26	He X and Zhang J: Why do hubs tend to be essential in protein networks? PLoS Genet. 2:e882006. View Article : Google Scholar : PubMed/NCBI
27	Bader GD and Hogue CW: An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics. 4:22003. View Article : Google Scholar : PubMed/NCBI
28	Ehling J and Tacke F: Role of chemokine pathways in hepatobiliary cancer. Cancer Lett. 379:173–183. 2015. View Article : Google Scholar : PubMed/NCBI
29	Charo IF and Ransohoff RM: The many roles of chemokines and chemokine receptors in inflammation. N Engl J Med. 354:610–621. 2006. View Article : Google Scholar : PubMed/NCBI
30	Marra F and Tacke F: Roles for chemokines in liver disease. Gastroenterology. 147:577–594.e1. 2014. View Article : Google Scholar : PubMed/NCBI
31	Wald O, Pappo O, Safadi R, Dagan-Berger M, Beider K, Wald H, Franitza S, Weiss I, Avniel S, Boaz P, et al: Involvement of the CXCL12/CXCR4 pathway in the advanced liver disease that is associated with hepatitis C virus or hepatitis B virus. Eur J Immunol. 34:1164–1174. 2004. View Article : Google Scholar : PubMed/NCBI
32	Ghanem I, Riveiro ME, Paradis V, Faivre S, de Parga PM and Raymond E: Insights on the CXCL12-CXCR4 axis in hepatocellular carcinoma carcinogenesis. Am J Transl Res. 6:340–352. 2014.PubMed/NCBI
33	Schimanski CC, Bahre R, Gockel I, Müller A, Frerichs K, Hörner V, Teufel A, Simiantonaki N, Biesterfeld S, Wehler T, et al: Dissemination of hepatocellular carcinoma is mediated via chemokine receptor CXCR4. Br J Cancer. 95:210–217. 2006. View Article : Google Scholar : PubMed/NCBI
34	Shah AD, Bouchard MJ and Shieh AC: Interstitial fluid flow increases hepatocellular carcinoma cell invasion through CXCR4/CXCL12 and MEK/ERK signaling. PLoS One. 10:e01423372015. View Article : Google Scholar : PubMed/NCBI
35	Xiang ZL, Zeng ZC, Tang ZY, Fan J, Zhuang PY, Liang Y, Tan YS and He J: Chemokine receptor CXCR4 expression in hepatocellular carcinoma patients increases the risk of bone metastases and poor survival. BMC Cancer. 9:1762009. View Article : Google Scholar : PubMed/NCBI
36	Neve Polimeno M, Ierano C, D'Alterio C, Simona Losito N, Napolitano M, Portella L, Scognamiglio G, Tatangelo F, Maria Trotta A, Curley S, et al: CXCR4 expression affects overall survival of HCC patients whereas CXCR7 expression does not. Cell Mol Immunol. 12:474–482. 2015. View Article : Google Scholar : PubMed/NCBI
37	Shibuta K, Mori M, Shimoda K, Inoue H, Mitra P and Barnard GF: Regional expression of CXCL12/CXCR4 in liver and hepatocellular carcinoma and cell-cycle variation during in vitro differentiation. Jpn J Cancer Res. 93:789–797. 2002. View Article : Google Scholar : PubMed/NCBI
38	Schimanski CC, Bahre R, Gockel I, Junginger T, Simiantonaki N, Biesterfeld S, Achenbach T, Wehler T, Galle PR and Moehler M: Chemokine receptor CCR7 enhances intrahepatic and lymphatic dissemination of human hepatocellular cancer. Oncol Rep. 16:109–113. 2006.PubMed/NCBI
39	Luo KQ, Shi YN and Peng JC: The effect of chemokine CC motif ligand 19 on the proliferation and migration of hepatocellular carcinoma. Tumour Biol. 35:12575–12581. 2014. View Article : Google Scholar : PubMed/NCBI
40	Shi JY, Yang LX, Wang ZC, Wang LY, Zhou J, Wang XY, Shi GM, Ding ZB, Ke AW, Dai Z, et al: CC chemokine receptor like 1 functions as a tumor suppressor by impairing CCR7-related chemotaxis in hepatocellular carcinoma. J Pathol. 235:546–558. 2015. View Article : Google Scholar : PubMed/NCBI
41	Heinzel K, Benz C and Bleul CC: A silent chemokine receptor regulates steady-state leukocyte homing in vivo. Proc Natl Acad Sci USA. 104:pp. 8421–8426. 2007; View Article : Google Scholar : PubMed/NCBI
42	Liang CM, Chen L, Hu H, Ma HY, Gao LL, Qin J and Zhong CP: Chemokines and their receptors play important roles in the development of hepatocellular carcinoma. World J Hepatol. 7:1390–1402. 2015. View Article : Google Scholar : PubMed/NCBI
43	Mohs A, Kuttkat N, Reißing J, Zimmermann HW, Sonntag R, Proudfoot A, Youssef SA, de Bruin A4, Cubero FJ and Trautwein C: Functional role of CCL5/RANTES for HCC progression during chronic liver disease. J Hepatol. 66:743–753. 2017. View Article : Google Scholar : PubMed/NCBI
44	Bai H, Weng Y, Bai S, Jiang Y, Li B, He F, Zhang R, Yan S, Deng F, Wang J and Shi Q: CCL5 secreted from bone marrow stromal cells stimulates the migration and invasion of Huh7 hepatocellular carcinoma cells via the PI3K-Akt pathway. Int J Oncol. 45:333–343. 2014. View Article : Google Scholar : PubMed/NCBI
45	Sadeghi M, Lahdou I, Oweira H, Daniel V, Terness P, Schmidt J, Weiss KH, Longerich T, Schemmer P, Opelz G and Mehrabi A: Serum levels of chemokines CCL4 and CCL5 in cirrhotic patients indicate the presence of hepatocellular carcinoma. Br J Cancer. 113:756–762. 2015. View Article : Google Scholar : PubMed/NCBI
46	Korobkova EA: Effect of natural polyphenols on CYP metabolism: implications for diseases. Chem Res Toxicol. 28:1359–1390. 2015. View Article : Google Scholar : PubMed/NCBI
47	Puszyk WM, Hlady R, Robertson K, Cabrera R and Liu C: Epigenetic signatures of alcohol abuse in hepatocellular carcinoma. FASEB J. 30 1 Suppl:S516.112016.
48	Kinoshita M and Miyata M: Underexpression of mRNA in human hepatocellular carcinoma focusing on eight loci. Hepatology. 36:433–438. 2002. View Article : Google Scholar : PubMed/NCBI
49	Wu X, Li C, Xing G, Qi X and Ren J: Resveratrol downregulates Cyp2e1 and attenuates chemically induced hepatocarcinogenesis in SD rats. J Toxicol Pathol. 26:385–392. 2013. View Article : Google Scholar : PubMed/NCBI
50	Chen H, Shen ZY, Xu W, Fan TY, Li J, Lu YF, Cheng ML and Liu J: Expression of P450 and nuclear receptors in normal and end-stage Chinese livers. World J Gastroenterol. 20:8681–8690. 2014. View Article : Google Scholar : PubMed/NCBI
51	Yu D, Green B, Marrone A, Guo Y, Kadlubar S, Lin D, Fuscoe J, Pogribny I and Ning B: Suppression of CYP2C9 by microRNA hsa-miR-128-3p in human liver cells and association with hepatocellular carcinoma. Sci Rep. 5:85342015. View Article : Google Scholar : PubMed/NCBI
52	Myung SJ, Yoon JH and Yu SJ: STAT3 & Cytochrome P450 2C9: A novel signaling pathway in liver cancer stem cells. Biomed Pharmacother. 66:612–616. 2012. View Article : Google Scholar : PubMed/NCBI
53	Jover R, Bort R, Gómez-Lechón MJ and Castell JV: Cytochrome P450 regulation by hepatocyte nuclear factor 4 in human hepatocytes: A study using adenovirus-mediated antisense targeting. Hepatology. 33:668–675. 2001. View Article : Google Scholar : PubMed/NCBI
54	Fushiya N, Takagi I, Nishino H, Akizuki S and Ohnishi A: Genetic polymorphisms of enzymes related to oral tegafur/uracil therapeutic efficacy in patients with hepatocellular carcinoma. Anticancer Drugs. 24:617–622. 2013.PubMed/NCBI
55	Iizuka N, Oka M, Hamamoto Y, Mori N, Tamesa T, Tangoku A, Miyamoto T, Uchimura S, Tamesa T, Tangoku A, et al: Altered levels of cytochrome p450 genes in hepatitis B or C virus-infected liver identified by oligonucleotide microarray. Cancer Genomics Proteomics. 1:53–58. 2004.
56	Sotaniemi EA, Rautio A, Bäckstrom M, Arvela P and Pelkonen O: CYP3A4 and CYP2A6 activities marked by the metabolism of lignocaine and coumarin in patients with liver and kidney diseases and epileptic patients. Br J Clin Pharmacol. 39:71–76. 1995. View Article : Google Scholar : PubMed/NCBI