Identification of the common regulators for hepatocellular carcinoma induced by hepatitis B virus X antigen in a mouse model

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Advance Access publication October 21, 2011

Identification of the common regulators for hepatocellular carcinoma induced by

hepatitis B virus X antigen in a mouse model

Jeng-Wei Lu

1,2,y

_{, Yu Hsia}

1,y

_{, Wan-Yu Yang}

1

_{, Yu-I Lin}

1

_,

Chao-Chin Li

3

_{, Ting-Fen Tsai}

1,3

_{, Ko-Wei Chang}

4

_,

Grace S.Shieh

4

_{, Shih-Feng Tsai}

1

_{, Horng-Dar Wang}

5

_and

Chiou-Hwa Yuh

1,6,7,

1

Institute of Molecular and Genomic Medicine, National Health Research Institutes, Taiwan,2_{Department of Life Sciences, National Central University,}

Taiwan,3_{Department of Life Sciences and Institute of Genome Sciences,}

National Yang-Ming University, Taiwan,4Institute of Statistical Science, Academia Sinica, Taiwan,5_{Department of Life Science and Institute of}

Biotechnology, National Tsing-Hua University, Taiwan,6College of Life Science and Institute of Bioinformatics and Structural Biology, National Tsing-Hua University, Taiwan and7Department of Biological Science and Technology, National Chiao Tung University, Taiwan

_{To whom correspondence should be addressed. Institute of Molecular and}

Genomic Medicine, National Health Research Institutes, R2-3033, #35 Keyan Road, Zhunan Town, Miaoli County 350, Taiwan, ROC. Tel:þ886 37 246166 35338; Fax:þ886 37 586459;

Email: [email protected]

Hepatitis B virus X antigen plays an important role in the

development of human hepatocellular carcinoma (HCC). The

key regulators controlling the temporal downstream gene

expression for HCC progression remains unknown. In this study,

we took advantage of systems biology approach and analyzed the

microarray data of the HBx transgenic mouse as a screening

process to identify the differentially expressed genes and applied

the software Pathway Studio to identify potential pathways and

regulators involved in HCC. Using subnetwork enrichment

analysis, we identified five common regulator genes: EDN1,

BMP7, BMP4, SPIB and SRC. Upregulation of the common

reg-ulators was validated in the other independent HBx transgenic

mouse lines. Furthermore, we verified the correlation of their

RNA expression levels by using the human HCC samples, and

their protein levels by using the human liver disease tissue arrays.

EDN1, bone morphogenetic protein (BMP) 4 and BMP7 were

up-regulated in cirrhosis, BMP4, BMP7 and SRC were further

upre-gulated in hepatocellular or cholangiocellular carcinoma samples.

The trend of increasing expression of the common regulators

cor-relates well with the progression of human liver cancer.

Overex-pression of the common regulators increases the cell viability,

promotes migration and invasiveness and enhances the colony

for-mation ability in Hep3B cells. Our approach allows us to identify

the critical genes in hepatocarcinogenesis in an HBx-induced

mouse model. The validation of the gene expressions in the liver

cancer of human patients and their cellular function assays

sug-gests that the identified common regulators may serve as useful

molecular targets for the early-stage diagnosis or therapy for HCC.

Introduction

Hepatocellular carcinoma (HCC) is the major histological type of

pri-mary liver cancer worldwide (1,2), and viral infection [hepatitis B virus

(HBV) or hepatitis C virus (HCV)] is the primary risk factor in .80% of

cases (3,4). Chronic HBV infection is a major etiological factor in HCC

(5), possibly through triggering specific oncogenic pathways and causing

an accumulation of genetic and epigenetic alterations in regulatory genes

(6–8). Although HBV infection is recognized as playing an important

role in hepatocarcinogenesis, the underlying molecular mechanisms

re-main unknown.

Hepatitis B virus X antigen (HBx), a protein of 154 amino acids,

could cause enhanced colony formation or transformation of cells

in vitro in various cell lines (9–11). HBx has been shown to activate

gene expression via oncogenic Ras signaling by increasing

TATA-binding protein levels (12–14). Several HBx transgenic mouse models

that develop HCC have also been created (15–17). Recently, transgenic

mice model in which the albumin promoter drives the expression of

HBx were shown to develop HCC at

14 to 16 months without

chemical treatment (15,18). This HBx-induced HCC mouse model

has proven to be powerful for identifying potential chemopreventive

agents of HBV-related hepatocarcinogenesis (18).

Several microarray-based studies have been done to identify the

dis-ease genes involved in carcinogenesis (19–22). Gene expression

pro-filing of human liver cancer cells yielded evidence that HBx regulates

many genes that may be involved in HCC (11). No study has yet been

reported to identify the early-stage regulators that control the temporal

gene expression of several downstream genes, which may contribute to

HCC formation. Systems biology provides the means to identify the

common regulators in the spatial and temporal regulation of a specific

biological function. The common regulators are defined to be the key

genes (like commanders) that control the temporal expression of the

downstream oncogenes and tumor suppressor genes differentially

ex-pressed during different cancer formation stages. The expressions of the

common regulators occur earlier than their target genes, thus to regulate

the expression of those downstream target genes. Therefore, the

iden-tification of the common regulators at the early stage might be a more

effective switch to pause the systematically biological functions.

In this study, we applied systems biology approach to analyze the

microarray data from the whole genomic expression profile of the

liver at the different time points of HBx transgenic mouse, which

serves as a screening process to identify differentially expressed

genes. Afterward, we validated the expression profile of the identified

candidate regulators in four other independent HBx transgenic mouse

lines and applied the software Pathway studio to identify potential

pathways and regulators involved in the HCC carcinogenesis.

Further-more, we confirmed the expression profile in both RNA and proteins

of the identified candidate regulator genes in human HCC samples.

We found that the RNA levels of common regulators were upregulated

in human HCC samples and the protein levels of EDN1, bone

mor-phogenetic protein (BMP) 4 and BMP7 were upregulated in cirrhosis,

BMP4, BMP7 and SRC were upregulated in HCC or

cholangiocellu-lar carcinoma (CC) samples and EDN1 was upregulated in cirrhosis

and inflammation liver samples and then downregulated in HCC

specimens. Overexpression of those common regulators could

in-crease the cell viability, promote migration and invasiveness and

en-hance the colony formation ability in Hep3B cells. Taken together, it

indicates that our approach allows us to identify precancer oncogenes

and cancer stage maintenance genes that might serve as potential

therapeutic target molecules for HCC treatment. The identification

of the common regulator genes at a stage prior to cancer formation

during HCC formation may be a new means to develop the potential

drug targets to not only eliminate but also if possibly prevent HCC.

Material and methods

HBx transgenic mice used for microarray analysis

Four transgenic mice lines overexpressing HBx in the liver were successfully established (15). All of the HBx transgenic mice are in the C57BL/6 inbred genetic background. For transgene copy numbers, overexpression of HBx in the transgenic livers and reproduction and growth status, please see previous Abbreviations:BRCA1, breast cancer 1; CC, cholangiocellular carcinoma;

cDNA, complementary DNA; HBV, hepatitis B virus; HCV, hepatitis C virus; HCC, hepatocellular carcinoma; IHC, immunohistochemistry; MAPK, mito-gen-activated protein kinase; Q-PCR, quantitative polymerase chain reaction.

y_{These authors contributed equally to this work.}

at National Chiao Tung University Library on April 28, 2014

http://carcin.oxfordjournals.org/

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publication (15). All of the mice were housed in a specific-pathogen-free facility. All of the animal protocols were consistent with the recommendations outlined in the ‘Guide for the Care and Use of Laboratory Animals’ (National Academy Press, Washington, DC) and strictly follow the rules issued by Institutional Animal Care and Use Committees of the National Yang-Ming University and National Health Research Institutes.

Experimental design and microarray

To identify potential differentially expressed genes, we used one transgenic mouse for each time points (1.5, 8, 12, 14, and 16 months) for microarray analysis as a screening process. All of the mice are male and in the C57BL/6 genetic background. In 14- and 16-month-old mice, tumor and adjacent non-tumor tissues were obtained from the same lobe of the liver. For a control, RNA was isolated from liver samples obtained from age-matched wild-type C57BL/6 mice. The liver was excised from different stage HBx transgenic mice (line A106), and RNA was immediately isolated from the liver tissue. RNA samples were tested with an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA) for purity and quantity. Fragmented complementary DNA (cDNA) was prepared (1 lg) following Roche NimbleGen’s (Madison, WI) double-stranded (ds) cDNA synthesis protocol by using SuperScriptII ds cDNA synthesis kit (Invitrogen, Carlsbad, CA) followed by RNase treatment. RNA integrity, ds cDNA quality and all samples for microarray analysis passed NimbleGen quality control. A high-density DNA array prepared using Mask-less Array Synthesizer technology from NimbleGen Mus musculus 1-Plex Array (NimbleGen design name MM8_60mer_expr, NimbleGen design ID 5045, catalog number: A4543-00-01, Mus musculus 385K array for 42 586 genes, nine probes per gene) was used for hybridization. Labeling, hybridiza-tion and scanning were performed by NimbleGen Systems following a standard operating protocol (see www.nimblegen.com). The raw data (.pair file) were subjected to Robust Multi-Array Analysis, quantile normalization and back-ground correction, as implemented in the NimbleScan software package, ver-sion 2.4.27 (Roche NimbleGen). The microarray data presented in this study were approved by the Gene Expression Omnibus (series accession number GSE15251). The data were incorporated into GeneSpring 7.3 (Strand Life Sciences, San Francisco, CA; Agilent Technologies, Inc.). Per chip and per gene normalizations were applied according to GeneSpring’s guidelines. Statistical analyses

We developed a statistical method that combines ‘trend analyses’ and ‘differ-ential expression’ to identify the presumptive oncogenes and tumor suppressor genes. Supplementary files 6 contain the information of T-test from late versus early stage, and Supplementary Table 7, available at Carcinogenesis Online, is the gene list from T-test of late versus early stage. Supplementary files 8 contain the information of Trend Analysis, and Supplementary Table 9, available at Carcinogenesis Online, is the gene list from trend analysis. The gene expression curves show expression levels during the five experimental stages: E1to E5corresponding to 1.5, 8, 12, 14 and 16 months. Based on the

literature, we first obtained 415 known cancer genes (Supplementary Table 1 is available at Carcinogenesis Online). The Stat2 statistic was used to cluster these 415 genes according to an increasing or decreasing trend of their gene expression curves.

Stat25 1 T 1

XT1 t51

fsin½giðt þ 1Þ giðtÞ jgiðt þ 1Þ giðtÞjg;

where gi(t) denotes the log2ratio of expression of gene i in the tth experiment

and T 5 5. The Stat2 statistic measures whether there is an increasing or decreasing trend in the gene expression curve. This analysis resulted in six clusters of interest. From each cluster, we chose two to three typical genes (see ‘Table of typical genes’ in Supplementary Table 2, available at Carcinogenesis Online) to represent the trend observed in each subgroup. Owing to computer memory constraints, we excluded genes without proper names, such as xxxRik and LOCxxx and obtained a total of 18 000 genes. From this set, we selected genes that were highly correlated with at least one typical gene in each sub-group, such that their Pearson correlations (r) were .0.95, and these genes constituted six subgroups of ‘candidate genes’.

Hierarchical clustering

The expression profiles were clustered using the hierarchical clustering method with the complete linkage metric, and the clusters were visualized using Tree-View (http://rana.lbl.gov/EisenSoftware.htm). By the definitions of precancer and cancer subgroups, log ratios of genes from tumor samples (denoted as T) versus those from non-tumor samples (NT) should help distinguish the sub-groups of precancer upregulated (or downregulated) genes from the cancer upregulated (or downregulated) genes. Therefore, the log ratios of gene ex-pression levels at stages E1to E3, NT4, NT5, T4and T5and log2(T4/NT4) and

log2(T5/NT5) were used to cluster these 1940 genes, where NT4 and NT5

denote gene expression from non-tumor samples at stages 4 and 5 (14 and 16 months). Using hierarchical clustering with the complete linkage metric, we grouped these genes into 13 clusters. Furthermore, the majority of precancer (cancer) upregulated genes were grouped in Clusters 1 and 2 (3 and 4), whereas the majority of precancer (cancer) downregulated genes were grouped into subgroups 9 and 10 (11), respectively (Figure 2B). Hierarchical clustering was applied to log ratios of Ei/Ci, where Eiand Ciare gene expression levels

of the experimental and control groups, respectively, at stage i and stages 1–5 denote 1.5, 8, 12, 14 and 16 months, respectively. Note that the experimental groups at stages 4 and 5 consist of gene expression from both tumor samples (T4and T5) and non-tumor samples (NT4and NT5).

RNA extraction and quantitative polymerase chain reaction

Quantitative polymerase chain reaction (Q-PCR) was used to validate the microarray results and to delineate the networks associated with carcinogen-esis. We used samples from four different lines of mice, including the line examined in the microarray (line 106) and other lines. The liver was excised from 1.5-, 8-, 12-, 14- and 16-month-old male HBx transgenic mice, and RNA was isolated from the frozen liver tissue. RNA (100 lg) was reverse transcribed into cDNA using SuperScriptII ds cDNA synthesis kit (Invitrogen) followed by RNase treatment as described earlier (23). The resulting first-strand cDNA was used as template for qualitative PCR in triplicate using SYBR Green Q-PCR Master Mix Kit (Applied Biosystems, Carlsbad, CA). Oligonucleotide PCR primer pairs were designed to cross intron–exon boundaries from published sequences in the GenBank database using Primer 3 (24). Primers were pur-chased from Taiwanese company (Mission Biotech, Taipei, Taiwan, R.O.C.) and the oligonucleotide was column purified and salt free. The sequence of primers for Q-PCR is provided in the Supplementary Table 5, available at Carcinogenesis Online. The specificity of the amplification products was con-firmed by size estimations on a 2% agarose gel and by analyzing their melting curves. The primer dimer was ruled out by performing no template control and analyzing their dissociation curve; without template, no cycle (Cq) values could be determined. After normalization to 18S rRNA, the expression ratio between the experimental and the control groups was calculated using the comparative DDCt method. For each Q-PCR, triplicates were employed and median values were calculated. At least three independent samples were used for Q-PCR, and the standard error was calculated and incorporated into the presented data as medians ± standard errors.

Human liver cancer samples

Forty-nine human liver cancer samples with HBV positive were used in this study, in which 17 samples each were stage I or stage II, 15 samples of stage III. This study was approved by the Institutional Review Board (IRB) of National Health Research Institutes Human Ethics Committee code: EC0971102. Biosamples including tumor tissue RNA, paired non-tumor tissue RNA and related clinical HCC patient information were provided by Taiwan Liver Cancer Network (TLCN). Tumor histology was graded by an expert pathologist (Dr Huang, Shiu-Feng, National Health Research Institutes, Zhunan, Miaoli, Taiwan). Stage I refers to solitary primary tumors without vascular invasion and metastasis. Stage II refers to solitary tumors with vascular invasion or multiple tumors ,5 cm. Stage III refers to multiple tumors .5 cm, tumors involving a major branch of the portal or hepatic vein or tumors with direct invasion of adjacent organs other than the gallbladder or with perforation of the visceral peritoneum. Some of the stage III patients also had regional lymph node metastasis.

Plasmid construction

Human BMP4, BMP7, EDN1 and SRC cDNA was reverse transcription–PCR amplified using RNA isolated from Hep3B cells as template. The resulting PCR product was ligated into pDsRed-Monomer-Hyg-N1 (Clontech, Mountain View, CA). DNA sequence analysis confirmed the correct cDNA sequence was inserted in the vector. The primers used to amplify the cDNA are listed below. The under-lined sequences are restriction enzyme cutting site used for ligation.

SalI-F-BMP4: GATCGTCGACATGATTCCTGGTAACCGAATG; BamHI-R-BMP4: GATCGGATCCCAGCGGCACCCACATCCCTC; SalI-F-BMP7: GATCGTCGACATGCACGTGCGCTCACTGCGAGCTG; BamHI-R-BMP7: GATCGGATCCCAGTGGCAGCCACAGGCCCGG; SalI-F-EDN1: TCGACATGGATTATTTGCTCATGATTTTCTC; BamHI-R-EDN1: GATCG-GATCCCACCAATGTGCTCGGTTGTG; XhoI-F-SRC: CCGCTCGAGATG-GGTAGCAACAAGAGCAAGC and HindIII-R-SRC: CCCAAGCTTGAGG TTCTCCCCGGGCTG. Sequencing primers—DsRed-Mo-N1-F: CACCAAA ATCAACGGGACTT and DsRed-Mo-N1-R: GTCTGGGTGCCCTCGTAG. Immunohistochemistry

Human HCC tissue microarrays were purchased from US Biomax (Rockville, MD). Four liver disease spectrum tissue microarrays (catalogue#:BC03002;

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US Biomax) were used: each contains 15 primary HCC, 8 CC, 8 cirrhosis of liver, 5 virus hepatitis of liver and 2 normal adjacent liver samples. The slides were depar-affinized with xylene and rehydrated by passages through decreasing concentrations of ethanol (from 100% to 70%). Endogenous peroxidase activity was blocked by 10 min incubation with citric acid buffer at 95°C. Tissue sections were then treated with 3% H2O2for antigen retrieval. After rinsing in 5% fetal bovine serum for blocking,

tissue sections were incubated overnight at 4°C with primary antibody. The pri-mary antibodies used in the immunohistochemistry (IHC) are rabbit anti-BMP4 (1:100 dilution; GeneTex Inc., Hsinchu City, Taiwan, R.O.C.); mouse anti-BMP7 (Santa Cruz Biotechnology, Inc., Santa Cruz, CA 1:100 dilution); rabbit anti-Endothelin 1 (1:100 dilution; GeneTex Inc.), and rabbit anti-SRC (1:100 dilution; GeneTex Inc.). Tissue sections were washed three times in 1 phosphate-buffered saline buffer and incubated for 60 min at room temperature with goat anti-rabbit

IgG (Santa Cruz Biotechnology, Inc., at a 1:100 dilution) or goat anti-mouse IgG (Santa Cruz Biotechnology, Inc., at a 1:100 dilution). After washing in 1 phos-phate-buffered saline buffer, peroxidase activity was detected by incubating tissue sections for 3–5 min at room temperature with Liquid DAB Substrate Kit (Invi-trogen). Tissue sections were counterstained with Mayer’s hematoxylin (Merck LTD., Taipei, Taiwan, R.O.C.). The staining intensity of BMP4, BMP7, Endothe-lin 1 and SRC were scored as 0 (,5%), 1 (5–25%) and 2 (25–50%) or (50–100%), respectively, according to the percentage of positively stained cells.

Cell culture, transfection, migration, cell viability [3-(4,5-dimethylthiazole-2-yl)-2,5-biphenyl tetrazolium bromide] and colony formation assays Human Hep3B liver cancer cells were obtained from Bioresource Collection and Research Center in Taiwan and grown in Dulbecco’s modified Eagle’s

Fig. 1. Hepatocarcinogenesis in HBx transgenic mice. (A) Representative photographs of the gross liver morphology in HBx transgenic mice at different times after birth. At 1.5 (A-a) and 8 months (A-b), no overt gross morphological changes were detected. At 12 months (A-c), multiple nodules (white arrows) between 0.5 and 1.5 mm in diameter were detected. At 14 months (A-d), larger hyperplastic nodules, between 1 and 5 mm in diameter, were detected. In addition, HCC began to appear in the livers of the transgenic mice. At 16 months (A-e), an HCC incidence of80% was observed in the HBx transgenic mice. T, tumor; NT, non-tumor. (B) Hematoxylin and eosin stain of the liver histopathology during carcinogenesis comparing HBx transgenic mice (B-a, c, e, g, i and j) with wild-type (B-b, d, f, h and k) at different times after birth. At 1.5 months (B-a), liver degeneration, including steatosis, ballooning of the hepatocytes and abnormal arrangement of the sinusoid, were detectable in the HBx transgenic mice. However, these pathological changes largely disappeared concomitantly with quiescence of the hepatocyte cell cycle at2 months. At 8 months (B-c), focal necrosis and inflammation (arrow heads) were frequently observed in the transgenic livers. At 12 months (B-e), hepatic steatosis and multifocal areas of altered hepatocytes and dysplasia with mild to moderate pleomorphic nuclei were detected. At 14 months (B-g), unusual nuclei, cytoplasmic lipid droplets, necrosis, apoptosis and inflammation were frequently observed in both tumor and non-tumor tissues from the transgenic livers. At 16 months (B-i, j), pathological analysis revealed that the HCC cells were arranged in a well-differentiated trabecular pattern. Histopathology of the 16 months non-tumor tissue was similar to that obtained from 14 months mice.

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Fig. 2. Five common regulators and their downstream targets at 12 (A) and 14 months (B). The color of the genes indicates their expression level; red indicating upregulation and green indicating downregulation in HBx transgenic mice versus wild-type. The green highlighted genes are the five common regulators, whereas the pink highlighted genes are the predicted transcription factors, which had expression levels upregulated at the 8 months stage. The shapes of proteins represent the molecular functions of each gene product, either as transcription factor (with key-lock shape at the bottom), ligand (diamond shape), kinase (moon shape), receptor (T shape) or protein entity (oval shape).

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medium high glucose (Gibco, Carlsbad, CA) supplemented with 10% fetal bovine serum (Gibco) and penicillin–streptomycin mixture (Gibco). Hep3B cells (530 000 cells per well) were seeded into 60 mm wells with Dulbecco’s modified Eagle’s medium and 10% serum. Cells were transfected with plasmid constructs encoding BMP4, BMP7, EDN1 or SRC using Lipofectamine 2000 (Invitrogen). After 18 h, the cells were resuspended; half were used for Q-PCR analysis, the other half for seeding in the migration assay. After 48 h, cells on the inside of the transwell inserts were removed with a cotton swab and cells on the underside of the inserts were fixed and stained. Photographs of 10 random fields were taken, and cells were counted to calculate the average number of cells per field that had transmigrated. For 3-(4,5-dimethylthiazole-2-yl)-2,5-biphenyl tetrazolium bromide (MTT) assay, a total of 7.5 103_{cells (100 ll)}

were seeded in 96-well plates. 100 ll of MTT (AMRESCO, Solon, OH) (MTT, stock: 5 mg/ml MTT in phosphate-buffered saline, one to dilution dilution in Dulbecco’s modified Eagle’s medium) was added to each well every 24 h. The plates were incubated for 4 h before addition of dimethyl sulfoxide (AMRES-CO). After shocking for 10 min, the absorbance was measured at 570 nm using a microplate reader. For colony formation assay, the cells after transfected with the expression constructs encoding EDN1 or SRC by using Lipofectamine 2000 (Invitrogen) were seeded at a density of 500, 1000 or 2000 cells per 60 mm well with Dulbecco’s modified Eagle’s medium and 10% serum. After 12 days incubation, the cells were stained with crystal violet, the resultant pictures were taken and the colonies were counted and analyzed by t-test.

Results

Appearance of liver, histopathological examination and HBx level in

the transgenic mouse model

HBx is a known oncogenic protein encoded in HBV genome and

regulates apoptosis, cell cycle, DNA repair, protein degradation and

several signaling pathways (25,26). HBx transgenic mice driven by

albumin promoter developed HCC at

14–18 months (15,18). Among

the independent HBx transgenic lines, A106 significantly developed

HCC much faster. To understand the molecular mechanisms and

iden-tify the genes and pathways related to HBx-induced HCC, we chose

A106 HBx transgenic mice to analyze the gene expression profile at

different stages of HCC by using microarray.

Figure 1 represents the appearance and histopathology of the livers

used in the microarray analysis. Although no obvious gross

morpho-logical changes were apparent at 1.5 and 8 months, histopathology

examination showed severe degeneration at 1.5 months and focal

necrosis and inflammation at 8 months in the livers of HBx transgenic

mice. At 12 months, multiple small hyperplastic nodules were

de-tected. At

14 months of age, larger hyperplastic nodules were

de-tected in the transgenic livers, and HCC began to appear. At

16 months, the incidence of HCC in the HBx transgenic mice was

80%.

We examined HBx messenger RNA at different stages in our mouse

model and found that the level was

11 and 14% at 14 and 16 months

tumor samples compared with those at 1.5 months (Supplementary

Figure 1 is available at Carcinogenesis Online). The expression level

of HBx in the mouse model was similar to that observed in human

HCC development, in which the HBx level is usually very low during

the final stage of HCC development (27,28), see Discussion.

Potential disease genes identified by microarray analysis across HCC

stages

We established genome-wide gene expression profiles during

hepato-carcinogenesis in seven samples at five distinct stages (1.5, 8, 12, 14

and 16 months, both tumor and non-tumor from 14 to 16 months) with

age-matched wild-type mice as control (see Materials and Methods

for detail). RNA isolated from a single transgenic animal at each time

point was analyzed, and all the HBx transgenic mice are in the

C57BL/6 inbred genetic background. This microarray study was

in-tended as a screen, from which we validated some of the findings in

multiple independent transgenic lines. To identify as many potential

disease genes for HCC as possible, two methods were used

(Supple-mentary Figure 2A is available at Carcinogenesis Online). Firstly,

genes with 2-fold differential expression between HBx transgenic

and wild-type mice at each stage were identified by GeneSpring

7.3. Secondly, we developed a statistical method that combined trend

analyses and differential expression to identify the presumptive tumor

oncogenes and suppressor genes; see Supplementary Tables 1 and 2

are available at Carcinogenesis Online for details. We compared the

expression pattern in the early (1.5, 8 and 12 months) and late (14 and

16 months) groups because no tumor was detected at the first three

stages, whereas larger hyperplastic nodules were detected in the

trans-genic livers at 14 months and later. If non-tumor and tumor samples

had similar expression at 14 and 16 months, they were added to the

precancer subgroup. On the other hand, if only tumor samples had

differential expression at 14 and 16 months, they were included in the

cancer subgroup. The upregulated group consisted of genes with

ex-pression levels that were not significantly different between HBx and

wild-type mice at the first three stages but were increased 2-fold or

more at 14 or 16 months. Conversely, the downregulated group

con-sisted of genes that were not differentially expressed at the early three

stages but were downregulated 2-fold or more at 14 or 16 months. By

this process, we found 1940 genes, which were differentially

ex-pressed during hepatocarcinogenesis (data not shown). In addition

to up and downregulated genes at 1.5, 8 and 12 months, there were

precancer up and downregulated, cancer up and downregulated

groups of genes. The expression profiles were grouped using the

hierarchical clustering method with the complete linkage metric

(Sup-plementary Figure 2B is available at Carcinogenesis Online). All

specific groups of genes were clustered into distinct subgroups,

in-dicating that they shared similar expression profiles and might have

common functions.

Genes involved in the non-tumor to tumor transition (12–14 months)

period and their common upstream regulators

In A106 HBx transgenic mouse line, there is no cancer cells detected

in the liver at the 12 months stage; however, liver cancer cells were

found at the 14 months stage. It suggests that critical changes of

HBx-induced HCC carcinogenesis in A106 occurred between 12 and 14

months period. To identify genes involved in this transition period, we

searched for genes that exhibit 2-fold expression changes at 14 versus

12 months stage from 5775 genes that were merged from all the

statistical analysis done earlier (data not shown). Next, we applied

a subnetwork enrichment analysis algorithm (sNEA in Pathway

Stu-dio 6.0) to search the literatures for the potential upstream regulators.

Among them, five candidate genes were found including, Bmp4,

Bmp7, Edn1, Spib and Src, and each of the five regulates multiple

downstream genes, whereas other regulators only regulate less than

three downstream genes. Another criterion for selecting those five

regulators is that they are significantly upregulated in 12 versus 14

months stage since a key regulator usually expresses before their

target genes. Figure 2 illustrates the interactions between these five

regulators (highlighted in grs are depicted in Figure 2A and 2B, in

which red indicates upregulation of a gene in HBx versus wild-type

and green indicates downregulation. The term of common regulators

is derived from the concept of systems biology (29,30), in which the

activity of differential expressed genes are controlled by common

regulators. Here, five common regulators were identified in the

HBx-induced HCC mouse model.

Quantitative real-time reverse transcription–PCR validation of

microarray gene expression data

To confirm the expression array data, we examined RNA expression

with quantitative real-time reverse transcription–PCR. RNA was

iso-lated from liver of the line examined in the microarray (line 106) with

age-matched wild-type mice as controls. We applied Q-PCR to

vali-date the microarray data from seven samples [1.5 months, 8 months,

12 months, 14 months (T), 14 months (NT), 16 months (T) and 16

months (NT)] for 75 genes. Of these genes, 73.4% were completely

identical between the microarray and Q-PCR and 17.3% matched at

some stages but not all. Only 9.3% of the genes validated with Q-PCR

differed from the microarray (data not shown).

The microarray results for the common regulators and their target

genes were validated using Q-PCR. Three common regulators (Bmp4,

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Bmp7 and Edn1) were upregulated at 12 versus 14 months tumor

(Supplementary Figure 3A is available at Carcinogenesis Online).

Both Q-PCR and microarray showed upregulation of Bmp7 and

Edn1 at 12 months compared with 14 months tumor. Two common

regulators were upregulated at 14 months tumor versus 12 months

(Supplementary Figure 3B is available at Carcinogenesis Online).

Both Q-PCR and microarray showed upregulation of Src at 14 months

tumor compared with 12 months. Supplementary Figure 3C–G,

avail-able at Carcinogenesis Online, shows the expression ratio of the

downstream target genes at 14 months tumor versus 12 months for

those common regulators. Most of the downstream target genes that

were positively regulated by the common regulators identified from

the literature were indeed upregulated at 14 months according to both

microarray and Q-PCR data. Three genes were predicted to be

re-pressed by Edn1, but only one was downregulated at 14 months

(Sup-plementary Figure 3F is available at Carcinogenesis Online). The

results from 14 months non-tumor showed a similar pattern

(Supple-mentary Figure 4 is available at Carcinogenesis Online), reinforcing

that the transition between 12 and 14 months is a precancerous event,

which occurs before final cancer formation and could be a critical

juncture for hepatocarcinogenesis.

Correlation of the expression levels of the common regulators with

HCC stages in the HBx transgenic mice

To evaluate the correlation between the common regulators and liver

cancer, we examined the expression level for those genes from four

independent lines of HBx transgenic mouse (A105, A106, A110 and

A112). All four lines developed multiple nodules between 12 and 16

months, and tumors began to appear at 18 months for A106 and A110

(Supplementary Table 3 is available at Carcinogenesis Online). All

three common regulators (Bmp4, Bmp7 and Src) were upregulated at

18 months (Figure 3A–C). In contrast, Edn1 expression was upregulated

at 14 months (Figure 3D), and Spib was upregulated at many different

stages (Figure 3E). Comparing the Q-PCR results and the microarray

data, Edn1 was upregulated prior to tumor formation in all four

trans-genic mouse lines, consistent with the line A106 used in microarray

analysis. Src was upregulated at the tumor-forming stage in all four

transgenic lines, also consistent with the A106 microarray results.

How-ever, Bmp4 and Bmp7 were upregulated at the tumor-forming stage in all

four transgenic lines, in contrast to being upregulated at the precancer

stage based on microarray data. This inconsistency might indicate

mul-tiple roles of BMPs during hepatocarcinogenesis.

The expression patterns of the identified common regulators in human

HCC tissues

To check whether the identified common regulators play roles in

human HCC, we examined their expression profiles in the specimens

from the human liver cancer tissues compared with the normal liver

biopsies of the same patients by Q-PCR. Forty-nine HBV positive (þ)

HCC specimens were obtained from Taiwan Liver Cancer Network

(TLCN), which contain three stages (stage I, II and III) of HCC based

on the classification of Tumor-lymph Nodes-Metastasis-staging

sys-tem and described in Materials and Methods.

We plotted the tumor versus non-tumor ratio for the five common

regulators first (Figure 4AE) and then calculated the percentages of

the HCC patients with up or downregulation of each common

regu-lators (Figure 4F and G). BMP4 and SRC were found overexpressed in

most HBV (þ)–HCC patients (Figure 4A, E). About 40–67% of HCC

patients with .2-fold overexpression than normal control (Figure 4F).

Yet there is no trend of expression changes of BMP4 and SRC among

the three stages of HCC. Interestingly, the expression of EDN1 was

Fig. 3. Bmp4, Bmp7, Src, Edn1and Spib expression in four different transgenic mice. The expression levels of the common regulators from early to later for four lines were showed (blue for A105, red for A106, green for A110 and purple for A112). Each Q-PCR experiment was repeated more than three times, and the average result is plotted as the ratio for the expression in HBx transgenic/wild-type mice with standard deviation.

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downregulated in the liver cancers of HCC patients (Figure 4C), and its

lowered expression is proportional along the stage progression of HCC

(Figure 4G). Furthermore, the expressions of BMP7 and SPIB were all

downregulated in the liver samples of HBV(þ) HCC patients and their

downregulation is also correlated with the stage progression of HCC.

Both SRC and BMP4 were upregulated as early at stage I of HCC,

whereas overexpressions of EDN1, BMP7 and SPIB may occur earlier

than stage I of HCC. The data from the human liver cancers correlate to

the previous findings in the livers of the HBx transgenic mice, which

supports the notion that the common regulators are upregulated in

precancer stage and then downregulated during cancer progression.

The protein expression levels of the common regulators in the human

liver disease tissue arrays

To further examine whether the protein expression levels of these

common regulators correlate to the liver disease stages throughout

HCC, we performed IHC analysis of the common regulators on the

tissue arrays covering the different liver disease samples from normal,

inflammation, cirrhosis and malignant HCC and CC tissues. Before

the IHC examination on the tissue array, we performed the pilot IHC

on the tissue slices from the same HCC patients’ samples for Q-PCR

and proved that the data of the staining intensity from the IHC is

highly correlated to the Q-PCR results (data not shown). Next, we

assess the expression levels of the four common regulators by IHC on

the tissue arrays for the liver diseases and HCC stages, the intensity of

IHC were classified into five scores from 0 to 4. The Supplementary

Figure 5, available at Carcinogenesis Online, contains the

represen-tative images from the IHC to show the different levels of staining

from 0 to 4. From the tissue array result, we found that the protein

expressions of EDN1, BMP4 and BMP7 were upregulated in

cirrho-sis, BMP4, BMP7 and SRC expressions were upregulated in the HCC

or CC samples and the increase correlate to the progression of cancer

(Figure 5). EDN1 expression was upregulated in cirrhosis and

inflam-mation liver samples and then downregulated in HCC specimens. The

raw data of the tissue array are attached in the Supplementary Figure 6

and Table 4 are available at Carcinogenesis Online. The protein

ex-pression data of the common regulators are in accord with the RNA

expression results by Q-PCR in the human liver disease samples.

Overexpressions of BMP4, BMP7, EDN1 and SRC increase cell

viability, enhance cell migration and promote the cell colony

formation ability

To investigate the roles of the common regulators in the liver cancer

cells, we overexpressed the regulator genes in Hep3B cells and

per-formed MTT assay to test the cell viability and transwell assay to

examine the migration ability. After transient transfection of the

regu-lator genes into Hep3B cells, Q-PCR was used to measure the levels of

each overexpression. We found that transient transfection effectively

expresses the regulator genes in Hep3B cells with the overexpression

ranging from 46.1- to 43 246.6-fold compared with the control

Fig. 4. BMP4, BMP7, EDN1, SPIBand SRC RNA expression in human HBV (þ)–HCC samples. Messenger RNA expression of five common regulators in stages I, II and III was determined by Q-PCR. (A–E) The BMP4(A), BMP7(B), EDN1(C), SPIB(D), and SRC(E) messenger RNA expression level is calculated by normalized with 18s first and compared with the expression from adjacent non-tumor tissue. Each stage contains many patients and the expression ratios were plotted. The percentage of overexpression (F) and downregulation (G) was plotted. Orange is used to indicate overexpression, whereas green denotes the downregulation. The darker color correlates with the progress of the stage.

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(Figure 6A). Under MTT assay, overexpression of BMP4 and EDN1

dramatically increased the cell proliferation starting from the third day

after transfection. On the sixth day, it increased to

16-fold compared

with the first day; however, the control only increased

5.8-fold than

the first day. Overexpression of BMP7 and SRC also significantly

in-creased the cell viability (10-fold than the first day) (Figure 6B). The

transwell assay was used to detect the migration ability of the

trans-fected Hep3B cells with each of the regulator genes. It shows that the

cell migration ability increased

2-fold compared with the control

(Figure 6C). We also used colony formation assay to examine the

transforming ability of overexpressing EDN1 or SRC in Hep3B cells.

After transient transfection of EDN1 and SRC expression constructs

into Hep3B cells, we seeded three different densities of the cells and let

the cells grow for 12 days and then measured the total colony

occupy-ing area per plate. SRC overexpression can significantly increase the

colonies size at the lower cell density. In the higher cell density, both

EDN1 and SRC dramatically enhance the colonies forming ability of

the cells compare to the MOCK (Figure 6D, E). The results indicate

that the properties of the EDN1- and SRC-transfected cells were

changed, possibly due to activation of downstream target genes.

Discussion

HBx is regarded as a carcinogen highly related to HCC formation.

However, years can elapse between HBV infection and cancer

forma-tion, implying an indirect role of HBx in activating cellular

oncogenes. In the HBx transgenic mouse model, HBx induces

pro-gressive changes in the liver, tumor development correlates precisely

with the binding of HBx and p53 in the cytoplasm and HBx blocks the

entry of p53 into the nucleus (31). However, a direct and

dose-dependent apoptotic function of HBx indose-dependent of p53 status

profoundly affected cells dying upon apoptotic stimuli in vitro (32)

and in a transgenic mouse model (32).

Based on the HBx transgenic mouse model, the oncogenic effect of

HBx is related to its expression level, specific mouse strains, type of

HBx and the integration site on the genome (33). HBx under its own

regulatory element causes progressive histopathological changes in

the liver, beginning with multiple foci of altered hepatocytes,

fol-lowed by the appearance of neoplasia (34). The high incidence of

HCC (86%) in HBx transgenic mice strongly suggests that HBx plays

a positive role in hepatocarcinogenesis (35). Results from these mice

show that HBx promotes hepatocarcinogenesis without fibrosis

(17,36), which might be due to its strong oncogenic potential (37).

In some cases, HBx was unable to generate cancer by itself; its

oncogenic effect required other oncogenes such as c-myc (38).

The level of HBx decreases as the process of hepatocarcinogenesis

progresses in humans. The protein level of HBx in chronic active

hepatitis, cirrhosis and HCC patients was detected in 95, 39 and

17% of patients, respectively (27). Other studies showed 30

(28,39,40) or 58.8% of HCC patients were HBx positive (41). Only

one exception found a high level of HBx (84% of patients) in liver

cells from patients with chronic liver disease (42). This implies HBx

has multiple roles during different stages of carcinogenesis. Initially,

a high level of HBx might promote apoptosis and arrest cell cycle, and

individual hepatocytes with a higher level of HBx expression may

undergo cell death; this might partly explain the decrease of HBx

protein during hepatocarcinogenesis. Later, HBx might activate other

cellular events, promoting turmorigenesis. Thus, the in vitro study

using overexpression of HBx in cell culture might represent an early

effect unrelated to HCC because of the low amount of HBx in HCC

Fig. 5. BMP4, BMP7, EDN1 and SRC protein expression levels in the human liver disease spectrum tissue array. To assess the correlation between the expression of the four common regulators and the different stages of human HCC, the staining intensity of IHC were classified into five scores from 0 to 4, and the examples of each score were listed in the Supplementary Figure 7, available at Carcinogenesis Online. Each IHC result was evaluated and given a score and then average the scores pooled from the same stage of liver disease from the specific staining. (A) presents the score for BMP4 at different status of liver disease, (B) is for BMP7, (C) represents EDN1 and (D) illustrates the protein expression levels of SRC in different stages of liver disease.

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patients. In contrast, the HBx level in our mouse model reflected

human HCC; therefore, the genes and pathways identified in this

mouse model might mimic human hepatocarcinogenesis.

To investigate the RNA expression profiles during different stages

of HCC

development,

genome-wide

expression arrays

were

performed using liver RNA obtained during early to late stages of

carcinogenesis. We assessed the expression of candidate genes shown

previously to be associated with HBx-induced HCC (Supplementary

Table 2 is available at Carcinogenesis Online). Most of the genes

identified previously were upregulated in the HBx-induced HCC mouse

model. Both transforming growth factor-beta and the ransforming

growth factor-beta receptor were upregulated. Furthermore, Src tyrosine

kinase (SRC: Rous sarcoma oncogene) and RAS (small G-protein,

sar-coma viral oncogene)-related proteins were also upregulated, as were

mitogen-activated protein kinase (MAPK)-, signal-transducer and

acti-vator of transcription protein- and Janus kinase pathway-related genes.

Interestingly, these candidate genes were often upregulated during later

stages of carcinogenesis (14 and/or 16 months). Conversely, the

aden-osine triphosphatase involved in DNA repair was downregulated

as early as 1.5 months. These findings indicate that our expression

pro-filing data are reliable and that microarray data can be used to identify

markers of HCC pathogenesis at an early stage.

The reason we used subnetwork enrichment analysis to find the

common regulators for the differentially expressed genes in 14

months tumor versus 12 months was to search for the most critical

genes responsible for the expression of those target genes. We actually

found eight genes including MAPK3, breast cancer 1 (BRCA1) and

CD44 and the five common regulators focused in this paper. However,

according to literature, MAPK3, BRCA1 and CD44 upregulated

many genes, which were downregulated in 14 months tumors. Also,

MAPK3 only upregulated at 16 months’ tumor, and both BRCA1 and

CD44 are upregulated in multiple stages. Therefore, it is not possible

for MAPK3, BRCA1 and CD44 be the candidate genes for the

pre-cancer stage upregulated common regulators.

HBx transactivates transcription elements in the nucleus and

stimulates signal transduction pathways in the cytoplasm (43,44). In

the nucleus, HBx associates with CBP/p300 (C-terminal binding

pro-tein) and binds to the cAMP response element-binding elements of the

Fig. 6. Overexpressions of BMP4, BMP7, EDN1 and SRC increase cell viability, migration and colony formation ability. (A) Total RNA was isolated from BMP4, BMP7, EDN1 and SRC transfectants, and the expression level was examined by Q-PCR. Each Q-PCR experiment was repeated five times, and the average result is plotted as the ratio for the expression in overexpressed/mock with standard deviation. (B) Cell viability for each overexpression transfectants was measured by using MTT assay as described in the Materials and Methods. (C) Cell migration assays for each overexpression transfectants was performed by using transwell as described in the Materials and Methods. Each migration assay was repeated at least three times, and the average result is plotted as the fold difference for the number of migrated cells in overexpressed versus mock. The standard deviations from the three individual experiments were calculated and are indicated in the graph. (D) The images of the plates from the colony formation assays for MOCK, EDN1 or SRC overexpression transfectants were measured as described in the Materials and Methods. (E) Quantitative measurement of the total colonies area in different seeding density and treatments were shown. Three random fixed-size areas were chosen to count the colony occupying area and the average of the colony occupying area (mm2_{) is indicated with the standard deviation. T-test is}

used to calculate the statistical significance.P , 0.05,P , 0.01,P , 0.00.

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promoters of interleukin 8 and proliferating cell nuclear antigen (45).

In the cytoplasm, HBx activates the protein kinase C signal pathway by

elevation of Diacylglycerol (DAG), causing a significant translocation of

protein kinase C to the membrane and subsequently activates activator

protein 1 and other protein kinase C-dependent transcription factors such

as activator protein 2 and nuclear factor-kappaB (46). HBx promotes Ras–

GTP complex formation and activates the Ras/Raf/MAP kinase signaling

cascade leading to activation of AP-1 and cell proliferation (47). HBx also

activates the Janus kinase–signal-transducer and activator of transcription

protein signaling pathway (48). Moreover, it has been demonstrated that

HBx is the activator of the ATF6 and IRE1–XBP1 pathways of unfolded

protein response (49). HBx may have different functions during the early

or late stages of viral infection owing to its association with different

transcription factors and signal transduction pathways. We have

reviewed the hepatocarcinogenesis and mechanisms, which HBx caused

liver cancer (8). With the advantage of a hepatocarcinogenesis animal

model, we can elucidate the relationships among these pathways and

genes.

Our microarray study suggested that Bmp4, Bmp7, Edn1, Spib and

Src are critical in the 12 to 14 months transition. The proteins encoded

by Bmp4 and Bmp7 are members of the BMP family, part of the

transforming growth factor-beta superfamily. Overexpression of

Bmp4 (50) and Bmp7 (51) has been suggested to promote migration

and invasion in colon cancer. Endothelin (Edn1) is a 21 amino acid

peptide that exerts a wide range of biological activities, and activation

of the endothelin system contributes to hepatoma growth (52). Spib is

a member of a subfamily of Ets transcription factors (53) and may be

related to cancer (54), and Src is a kinase that may play a key role in

HCC progression (55) and others have already shown that HBx

activates Src (56). From the results, we found that upregulation of

Edn1 at the 12 months stage may trigger the expression of many

downstream genes. In addition, the upregulation of Bmp4, Bmp7, Spib

and Src at the 14 months stage may enhance and maintain the

expres-sion of those oncogenic genes. Overexpresexpres-sion of those common

regulators in hepatoma cell lines further enhances cell mobility. Our

study links these carcinogenetic factors to the HBx-induced HCC

mouse model, establishing a possible mechanism by which liver cells

become cancerous at the critical stage.

From our study, some genes critical for the transition of HCC were

suggested and pathways underlying HCC formation were identified

by genome-wide gene expression profiling. The relationships among

early, middle and late candidate disease genes and pathways shed light

on further functional analyses of HBx-induced HCC. We verified the

microarray data using Q-PCR in other transgenic mouse lines and

HCC patient samples. We also examined the function of

overexpress-ing candidate genes in cell lines and observed an increase in cell

mobility. Data presented here may help elucidate the molecular

mech-anisms involved in HBx-induced HCC in humans. However, we have

to keep in mind that HBx expression in the mouse models does not

necessarily reflect expression levels in the context of HBV replication

and consequently, may not directly mimic what would occur in an

authentic HBV infection.

Supplementary material

Supplementary Tables 1–9 and Figures 1–6 can be found at http://

carcin.oxfordjournals.org/

Funding

National Research Program for Genomic Medicine (NSC

99-3112-B-400-010, 98-3112-B-400-003, 97-3112-B-400-008 and

96-3112-B-400-011) to C.H.Y, National Science Council (NSC) (NSC

100-2118-M-001-003-MY2, NSC-99-3112-B-001-015) to G.S.S.,

and NSC (97-2311-B-007-004-MY3) grant to H.D.W. are

ac-knowledged. National Health Research Institutes to C.H.Y. TLCN

is supported by grants from NSC (NSC 94-3112-B-182-002,

NSC 97-3112-B-182-004) and National Health Research Institutes,

Taiwan.

Acknowledgements

Funding support from National Health Research Institute to C.H.Y is gratefully acknowledged. We would like to thank the TLCN for providing the HCC tissue samples and related clinical data (all are anonymous) for our research work. This network currently includes five major medical centers (National Taiwan University Hospital, Chang-Gung Memorial Hospital-Linko, Veteran General Hospital-Taichung, Chang-Gung Memorial Hospital-Kaohsiung and Veteran General Hospital-Kaohsiung).

Conflict of Interest Statement: None declared.

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Received February 22, 2011; revised September 14, 2011; accepted October 13, 2011