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BMC Cancer
Published in: BMC Cancer 1/2017

Open Access 01-12-2017 | Research article

Gene expression, regulation of DEN and HBx induced HCC mice models and comparisons of tumor, para-tumor and normal tissues

Authors: Qin Tang, Qi Wang, Qiong Zhang, Sheng-Yan Lin, Yanhong Zhu, Xiangliang Yang, An-Yuan Guo

Published in: BMC Cancer | Issue 1/2017

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Abstract

Background

Hepatocellular carcinoma (HCC) is the leading cause of cancer mortality. Chemical and virus induction are two major risk factors, however, the potential molecular mechanisms of their differences remain elusive. In this study, to identify the similarities and differences between chemical and virus induced HCC models, we compared the gene expression profiles between DEN and HBx mice models, as well as the differences among tumor, para-tumor and normal tissues.

Methods

We sequenced both gene and microRNA (miRNA) expression for HCC tumor tissues, para-tumor and normal liver tissues from DEN model mice (30-week-old) and downloaded the corresponding microarray expression data of HBx model from GEO database. Then differentially expressed genes (DEGs), miRNAs and transcription factors (TFs) were detected by R packages and performed functional enrichment analysis. To explore the gene regulatory network in HCC models, miRNA and TF regulatory networks were constructed by target prediction.

Results

For model comparison, although DEGs between tumor and normal tissues in DEN and HBx models only had a small part of overlapping, they shared common pathways including lipid metabolism, oxidation-reduction process and immune process. For tissue comparisons in each model, genes in oxidation-reduction process were down-regulated in tumor tissues and genes in inflammatory response showed the highest expression level in para-tumor tissues. Genes highly expressed in both tumor and para-tumor tissues in two models mainly participated in immune and inflammatory response. Genes expressed in HBx model were also involved in cell proliferation and cell migration etc. Network analysis revealed that several miRNAs such as miR-381-3p, miR-142a-3p, miR-214-3p and TFs such as Egr1, Atf3 and Klf4 were the core regulators in HCC.

Conclusions

Through the comparative analyses, we found that para-tumor tissue is a highly inflammatory tissue while the tumor tissue is specific with both inflammatory and cancer signaling pathways. The DEN and HBx mice models have different gene expression pattern but shared pathways. This work will help to elucidate the molecular mechanisms underlying different HCC models.
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Literature
1.
Lafaro KJ, Demirjian AN, Pawlik TM. Epidemiology of hepatocellular carcinoma. Surg Oncol Clin N Am. 2015;24:1–17.CrossRefPubMed
2.
Bosetti C, Turati F, La Vecchia C. Hepatocellular carcinoma epidemiology. Best Pract Res Clin Gastroenterol. 2014;28:753–70.CrossRefPubMed
3.
Marengo A, Rosso C, Bugianesi E. Liver cancer: connections with obesity, fatty liver, and cirrhosis. Annu Rev Med. 2016;67:103–17.CrossRefPubMed
4.
Kremsdorf D, Soussan P, Paterlini-Brechot P, Brechot C. Hepatitis B virus-related hepatocellular carcinoma: paradigms for viral-related human carcinogenesis. Oncogene. 2006;25:3823–33.CrossRefPubMed
5.
6.
7.
Schmidt J, Jo M, Zhou T, Kim Y, MacLeod AR. Abstract 4359: characterization of GalNAc-conjugated generation 2.5 ASOs in DEN and DEN/CCL4-induced HCC tumors. Cancer Res. 2015;75:4359.CrossRef
8.
Chang W, He W, Li PP, Song SS, Yuan PF, Lu JT, et al. Protective effects of Celastrol on diethylnitrosamine-induced hepatocellular carcinoma in rats and its mechanisms. Eur J Pharmacol. 2016;784:173–80.CrossRefPubMed
9.
Jo W, Yu ES, Chang M, Park HK, Choi HJ, Ryu JE, et al. Metformin inhibits early stage diethylnitrosamineinduced hepatocarcinogenesis in rats. Mol Med Rep. 2016;13:146–52.CrossRefPubMed
10.
Marrone AK, Shpyleva S, Chappell G, Tryndyak V, Uehara T, Tsuchiya M, et al. Differentially expressed MicroRNAs provide mechanistic insight into fibrosis-associated liver carcinogenesis in mice. Mol Carcinog. 2016;55:808–17.CrossRefPubMed
11.
Tu T, Budzinska MA, Shackel NA, Jilbert AR. Conceptual models for the initiation of hepatitis B virus-associated hepatocellular carcinoma. Liver Int. 2015;35:1786–800.CrossRefPubMed
12.
Kim CM, Koike K, Saito I, Miyamura T, Jay G. HBx gene of hepatitis B virus induces liver cancer in transgenic mice. Nature. 1991;351:317–20.CrossRefPubMed
13.
Lu JW, Hsia Y, Yang WY, Lin YI, Li CC, Tsai TF, et al. Identification of the common regulators for hepatocellular carcinoma induced by hepatitis B virus X antigen in a mouse model. Carcinogenesis. 2012;33:209–19.CrossRefPubMed
14.
Ye H, Zhang C, Wang BJ, Tan XH, Zhang WP, Teng Y, et al. Synergistic function of Kras mutation and HBx in initiation and progression of hepatocellular carcinoma in mice. Oncogene. 2014;33:5133–8.CrossRefPubMed
15.
He XX, Guo AY, Xu CR, Chang Y, Xiang GY, Gong J, et al. Bioinformatics analysis identifies miR-221 as a core regulator in hepatocellular carcinoma and its silencing suppresses tumor properties. Oncol Rep. 2014;32:1200–10.CrossRefPubMed
16.
Yang Y, Chang S, Zhao Z, Hou NI, He K, Wang X, et al. MicroRNA-214 suppresses the proliferation of human hepatocellular carcinoma cells by targeting E2F3. Oncol Lett. 2015;10:3779–84.PubMedPubMedCentral
17.
He XX, Chang Y, Meng FY, Wang MY, Xie QH, Tang F, et al. MicroRNA-375 targets AEG-1 in hepatocellular carcinoma and suppresses liver cancer cell growth in vitro and in vivo. Oncogene. 2012;31:3357–69.CrossRefPubMed
18.
Lin Y, Zhang Q, Zhang HM, Liu W, Liu CJ, Li Q, et al. Transcription factor and miRNA co-regulatory network reveals shared and specific regulators in the development of B cell and T cell. Sci Rep. 2015;5:15215.CrossRefPubMedPubMedCentral
19.
20.
Zhang N, Duan WD, Leng JJ, Zhou L, Wang X, Xu YZ, et al. STAT3 regulates the migration and invasion of a stemlike subpopulation through microRNA21 and multiple targets in hepatocellular carcinoma. Oncol Rep. 2015;33:1493–8.CrossRefPubMed
21.
Zhang H-M, Li Q, Zhu X, Liu W, Hu H, Liu T, et al. miR-146b-5p within BCR-ABL1–positive microvesicles promotes leukemic transformation of hematopoietic cells. Cancer Res. 2016;76:2901–11.CrossRefPubMed
22.
Zhang HM, Kuang S, Xiong X, Gao T, Liu C, Guo AY. Transcription factor and microRNA co-regulatory loops: important regulatory motifs in biological processes and diseases. Brief Bioinform. 2015;16:45–58.CrossRefPubMed
23.
Ye H, Liu X, Lv M, Wu Y, Kuang S, Gong J, et al. MicroRNA and transcription factor co-regulatory network analysis reveals miR-19 inhibits CYLD in T-cell acute lymphoblastic leukemia. Nucleic Acids Res. 2012;40:5201–14.CrossRefPubMedPubMedCentral
24.
Pertea M, Kim D, Pertea GM, Leek JT, Salzberg SL. Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. Nat Protoc. 2016;11:1650–67.CrossRefPubMedPubMedCentral
25.
26.
27.
Wang L, Feng Z, Wang X, Wang X, Zhang X. DEGseq: an R package for identifying differentially expressed genes from RNA-seq data. Bioinformatics. 2010;26:136–8.CrossRefPubMed
28.
Robinson MD, McCarthy DJ, Smyth GK. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139–40.CrossRefPubMed
29.
Zwillinger D, Kokoska S. CRC standard probability and statistics tables and formulae. 1999. Crc Press.
30.
Hsu SD, Huang HY, Chou CH, Sun YM, Hsu MT, Tsou AP. Integrated analyses to reconstruct microRNA-mediated regulatory networks in mouse liver using high-throughput profiling. BMC Genomics. 2015;16(Suppl 2):S12.CrossRefPubMedPubMedCentral
31.
Wu L, Cai C, Wang X, Liu M, Li X, Tang H. MicroRNA-142-3p, a new regulator of RAC1, suppresses the migration and invasion of hepatocellular carcinoma cells. FEBS Lett. 2011;585:1322–30.CrossRefPubMed
32.
Xia H, Ooi LL, Hui KM. MiR-214 targets beta-catenin pathway to suppress invasion, stem-like traits and recurrence of human hepatocellular carcinoma. PLoS One. 2012;7:e44206.CrossRefPubMedPubMedCentral
33.
Hao MW, Liang YR, Liu YF, Liu L, Wu MY, Yang HX. Transcription factor EGR-1 inhibits growth of hepatocellular carcinoma and esophageal carcinoma cell lines. World J Gastroenterol. 2002;8:203–7.CrossRefPubMedPubMedCentral
34.
Xiaoyan L, Shengbing Z, Yu Z, Lin Z, Chengjie L, Jingfeng L, et al. Low expression of activating transcription factor 3 in human hepatocellular carcinoma and its clinicopathological significance. Pathol Res Pract. 2014;210:477–81.CrossRefPubMed
35.
Yao S, Tian C, Ding Y, Ye Q, Gao Y, Yang N, et al. Down-regulation of Kruppel-like factor-4 by microRNA-135a-5p promotes proliferation and metastasis in hepatocellular carcinoma by transforming growth factor-beta1. Oncotarget. 2016;7:42566–78.CrossRefPubMedPubMedCentral
36.
Teng YC, Shen ZQ, Kao CH, Tsai TF. Hepatocellular carcinoma mouse models: hepatitis B virus-associated hepatocarcinogenesis and haploinsufficient tumor suppressor genes. World J Gastroenterol. 2016;22:300–25.CrossRefPubMedPubMedCentral
37.
Kanda M, Sugimoto H, Kodera Y. Genetic and epigenetic aspects of initiation and progression of hepatocellular carcinoma. World J Gastroenterol. 2015;21:10584–97.CrossRefPubMedPubMedCentral
38.
Chandran UR, Dhir R, Ma C, Michalopoulos G, Becich M, Gilbertson J. Differences in gene expression in prostate cancer, normal appearing prostate tissue adjacent to cancer and prostate tissue from cancer free organ donors. BMC Cancer. 2005;5:45.CrossRefPubMedPubMedCentral
39.
Grivennikov SI, Karin M. Inflammatory cytokines in cancer: tumour necrosis factor and interleukin 6 take the stage. Ann Rheum Dis. 2011;70(Suppl 1):i104–8.CrossRefPubMed
40.
41.
Bianco R, Melisi D, Ciardiello F, Tortora G. Key cancer cell signal transduction pathways as therapeutic targets. Eur J Cancer. 2006;42:290–4.CrossRefPubMed
42.
Selaru FM, Olaru AV, Kan T, David S, Cheng Y, Mori Y, et al. MicroRNA-21 is overexpressed in human cholangiocarcinoma and regulates programmed cell death 4 and tissue inhibitor of metalloproteinase 3. Hepatology. 2009;49:1595–601.CrossRefPubMedPubMedCentral
43.
Frankel LB, Christoffersen NR, Jacobsen A, Lindow M, Krogh A, Lund AH. Programmed cell death 4 (PDCD4) is an important functional target of the microRNA miR-21 in breast cancer cells. J Biol Chem. 2008;283:1026–33.CrossRefPubMed
44.
Dutto I, Tillhon M, Cazzalini O, Stivala LA, Prosperi E. Biology of the cell cycle inhibitor p21(CDKN1A): molecular mechanisms and relevance in chemical toxicology. Arch Toxicol. 2015;89:155–78.CrossRefPubMed
45.
Broude EV, Demidenko ZN, Vivo C, Swift ME, Davis BM, Blagosklonny MV, et al. p21 (CDKN1A) is a negative regulator of p53 stability. Cell Cycle. 2007;6:1468–71.CrossRefPubMed
46.
Mu X, Pradere JP, Affo S, Dapito DH, Friedman R, Lefkovitch JH, et al. Epithelial transforming growth factor-beta signaling does not contribute to liver fibrosis but protects mice from cholangiocarcinoma. Gastroenterology. 2016;150:720–33.CrossRefPubMed
Metadata
Title
Gene expression, regulation of DEN and HBx induced HCC mice models and comparisons of tumor, para-tumor and normal tissues
Authors
Qin Tang
Qi Wang
Qiong Zhang
Sheng-Yan Lin
Yanhong Zhu
Xiangliang Yang
An-Yuan Guo
Publication date
01-12-2017
Publisher
BioMed Central
Published in
BMC Cancer / Issue 1/2017
Electronic ISSN: 1471-2407
DOI
https://doi.org/10.1186/s12885-017-3860-x

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