Epidermal growth factor-induced hepatocellular carcinoma - CiteSeerX

Oncogene (2005) 24, 1809–1819

& 2005 Nature Publishing Group All rights reserved 0950-9232/05 $30.00 www.nature.com/onc

ORIGINAL PAPERS

Epidermal growth factor-induced hepatocellular carcinoma: gene expression profiles in precursor lesions, early stage and solitary tumours Ju¨rgen Borlak*,1,4, Tatiana Meier1,4, Roman Halter1,4, Reinhard Spanel2 and Katharina Spanel-Borowski3 1 Department of Pharmacology and Molecular Medicine, Fraunhofer Institute of Toxicology and Experimental Medicine, NikolaiFuchsstr. 1, 30625 Hannover, Germany; 2Institute of Pathology, Viersen, Germany; 3Institute of Anatomy, University of Leipzig, Germany

Epidermal growth factor is an important mitogen for hepatocytes. Its overexpression promotes hepatocellular carcinogenesis. To identify the network of genes regulated through EGF, we investigated the liver transcriptome during various stages of hepatocarcinogenesis in EGF2B transgenic mice. Targeted overexpression of IgEGF induced distinct hepatocellular lesions and eventually solid tumours at the age of 6–8 months, as evidenced by histopathology. We used the murine MG U74Av2 oligonucleotide microarrays to identify transcript signatures in 12 tumours of small (n ¼ 5, pooled), medium (n ¼ 4) and large sizes (n ¼ 3), and compared the findings with three nontumorous transgenic livers and four control livers. Global gene expression analysis at successive stages of carcinogenesis revealed hallmarks linked to tumour size. A comparison of gene expression profiles of nontumorous transgenic liver versus control liver provided insight into the initial events predisposing liver cells to malignant transformation, and we found overexpression of c-fos, eps15, TGIF, IGFBP1, Alcam, ets-2 and repression of Gas-1 as distinct events. Further, when gene expression profiles of small manifested tumours were compared with nontumorous transgenic liver, additional changes were obvious and included overexpression of junB, Id-1, minopontin, villin, claudin-7, RR M2, p34cdc2, cyclinD1 and cyclinB1 among others. These genes are therefore strongly associated with tumour formation. Our study provided new information on the tumour stage-dependent network of EGF-regulated genes, and we identified candidate genes linked to tumorigenes and progression of disease. Oncogene (2005) 24, 1809–1819. doi:10.1038/sj.onc.1208196 Published online 24 January 2005 Keywords: HCC; EGF; transgenic mice; tumour stages; gene expression profiling Introduction It is estimated that about 350 000 new cases of hepatocellular carcinoma (HCC) arise per year (Schafer *Correspondence: J Borlak; E-mail: [email protected] 4 These authors contributed equally to this work Received 20 February 2004; revised 28 July 2004; accepted 9 August 2004; published online 24 January 2005

and Sorrell, 1999). The most prominent risk factors are chronic hepatitis B and C virus infection, Aflatoxin B1 exposure and alcohol-related cirrhosis. As of today, the precise molecular mechanism in the onset and progression of disease remains uncertain. Overexpression of liver mitogens may be an important mechanism of disease. EGF and TGFa are potent mitogens for hepatocytes (Wang et al., 1999; Rescan et al., 2001). Signalling of these mitogens is through binding to members of the EGF-receptor family. Their expression is unregulated in HCC and this supports autocrine growth stimulation of hepatoma cells (Yamaguchi et al., 1995; Chung et al., 2000). EGF also plays an important role in hepatocyte morphology (Rescan et al., 2001). Overexpression of EGF might be an important step towards development of liver cancer and is suspected to play a particular role in spontaneous liver tumour development (Ostrowski et al., 2000). There is suspicion that EGF plays a role in Helicobacter hepaticus-induced chronic hepatitis with progression to hepatocellular cancer (Ramljak et al., 1998). Previous investigations demonstrated targeted overexpression of a secretable form of EGF (IgEGF) to result in multiple highly malignant HCCs, with 100% fatalities around 7–8 months after birth (To¨njes et al., 1995). This transgenic mouse line therefore mimics effectively the consequence of altered EGF signalling via the EGF receptor. We used this mouse model to identify the network of EGF-regulated genes at various stages of tumour development and in solid tumours. Global gene expression analysis at successive stages of carcinogenesis holds promise for an identification of master genes for the onset and progression of disease. Thus, we aimed to identify candidate genes associated with early stages of tumorigenesis and with developed HCCs. Overall, this study aimed for a better understanding of the network of EGF-regulated genes in liver carcinogenesis.

Results Histopathology of liver tumours Macroscopically, all EGF-overexpressing animals developed tumours at the age of 6–9 months. A total of six

Early tumour stage in EGF-induced HCC J Borlak et al

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tumour-bearing EGF2B mice as well as tumour-free parenchyma and liver from wild-type animals were investigated. The HCCs ranged from less to highly differentiated. As a rule, small HCCs were adenoma-like tumours with well-developed hepatocytes, whereas larger HCC showed a lot of cellular dedifferentiation and enhanced nuclear atypia (Figure 1). There were precursor lesions in the tumour-free liver tissue as well. Basically all of the trabecular parenchyma showed nuclear atypia with enlarged nuclei, mainly increased numbers of large polyploid nuclei, anisocaryosis and some polymorphism, defined as large-cell dysplasia (LCD). This LCD seemed to merge into multicentric nodule formation, usually with small cell changes and various degrees of atypia. According to the actually proposed terminology of nodular hepatocellular lesions in human pathology (Hepatology 1995, International Working Party), these nodules can be defined as dysplastic foci (DF) and dysplastic nodules (DN). Transcript profiling Abundant expression of transgenic EGF in liver and tumours of transgenic mice was confirmed by RT–PCR, as depicted in Figure 2. As transcript profiling of juvenile tumours is of considerable value for an identification of priming factors in tumour development, we divided tumours into three groups according to their size. We investigated gene expression profiles in developing liver carcinomas and compared global expression profiles of wild-type animals with liver tumours from small to large size, as well as macroscopically nontumorous livers of tumour-bearing animals. In all, 4175

genes and ESTs were commonly expressed in all eight tumour samples and 4149 genes were expressed in n ¼ 4 normal liver samples, the expression of many genes being increased or decreased during the process of liver carcinogenesis (Table 1). To determine significant gene expression changes, we performed T-test analysis between control liver and sets of tumours, taking only changes into account, which were detected in all tumour samples or in all control livers for identification of overexpressed or repressed genes accordingly. Further, we performed ranking analyses (see Materials and methods) that enabled a more stringent comparison according to the consistency of gene expression changes (Table 1). Notably, gene expression profiles were significantly changed in transgenic nontumorous liver, but the number of deregulated genes was further increased in tumours. In all, 109 of the upregulated and 55 of the repressed genes show a signal intensity X70, a fold change X3 or p3, a P-value p0.05 and 100% concordance of expression changes in pairwise comparative analyses in at least one group of the tumours, when compared with normal liver. A total of 59 of the overexpressed and five of the repressed genes selected by their importance and their possible functions are listed in Table 2a and b. Some genes known to be important for cancer biology (e.g. cyclinD1 and PDGFa) with a lower FC are also included. For the pool of small tumours, T-test analysis could not be carried out. Instead, the inclusion criterion was 100% concordance of expression changes, when compared to n ¼ 4 normal liver samples. Table 2 allows a direct comparison of gene expression profiles of different tumour sizes, from EGF-overexpressing

Figure 1 Histology of tumours and precursor lesions in EGF2B transgenic mice. Normal liver tissue of nontransgenic controls with normal polyploidal variation of hepatocytic nuclear size (central vein: cv) (a). LCD in tumour-free parenchyma of EGF2B mice merging with initial perivenous DF (arrows) and DN (arrowheads): Small-cell nodular proliferations (b). Well-differentiated, adenoma-like small HCC: irregular, mainly bilayered trabeculae, slight polymorphism and lipid vacuolization (c). Multilayered trabeculae of larger HCC (d). Same magnifications. Staining: H (a, b); PAS (c); H and E (d) Oncogene


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Figure 2 (a) Structure of the transgene. albP, murine albumin promoter; Ig-S, Ig-signal sequence; I, Intron sequence; EGF, synthetic EGF; SVA, SV40 poly-A signal. (b) PCR analysis of Ig/EGF from tail biopsies to identify transgenic mice. Lane 1: nontransgenic mice; lanes 2–5: transgenic mice; M: molecular weight standard; lane 6: amplified fragment of the transgene was digested with EcoRI to obtain fragments of 210 and 107 bp. (c) RT–PCR analysis of the housekeeping gene (b-actin) and the transgene. Lanes 1–3: nontransgenic liver, lanes 4–6: macroscopic nontumorous liver of transgenic mice, lane 7: pool of small tumours; lanes 8–10: tumours of medium size; lanes 11 and 12: tumours of large size; lane 13: negative control (water). (d) RT–PCR of selected genes (lanes represent samples as described in (c))

Table 1 Number of genes and ESTs expressed in EGF-induced liver tumours and normal liver Genes or ESTs

EGF-transgenic liver (three samples)

Detected in all samples within a group Not detected in all samples within a group Upregulated in liver tumours according to T-test (P-valuep0.05); FCX2 Upregulated in liver tumours according to comparison ranking (100% concordance in comparative analyses); FCX2 Downregulated in liver tumours according to T-test (P-valuep0,05); FCp2 Downregulated in liver tumours according to comparison ranking (100% concordance in comparative analyses); FCp2 Uniquely expressed either in all tumours or normal liver, P-value in T-test p0.05, 100% concordance in comparative analyses; FCX2/p2

tumour-free transgenic liver with LCD to large manifested tumours. We found certain genes to be equally deregulated in displastic transgenic liver as in tumours (e.g. c-fos, eps-15, EGR-1, TGIF, IGFBP1, Alcam),

All tumours (eight samples)

Normal liver (four samples)

4494 6569 140 67

4175 5768 265 89

4149 6655 — —

52 23

130 59

— —

23

1

while others were dramatically deregulated at the onset of small HCC (e.g. p34cdc2, Id1, junB, minopontin, claudin7). Besides, we identified 23 genes that were uniquely expressed in all tumours, but not in controls Oncogene

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Table 2 Gene expression signatures in EGF-induced mouse liver tumours: (a) upregulated genes; (b) downregulated genes Gene

ACC

Gene description

Transgenic nontumorous liver

Tumours Small size (o2 mm) (Pool)

P-value (%)

FC

CD63 antigen G7e Fibrinogen-like protein 2 Growth promotion PDGFa

D16432 U69488 M16238 M29464

Lipoprotein lipase

P-value (%)

164.3 164.8

100 100

117.8 136.1

0.156 (75) 0.046

50.1 40.55

0.372 (66) 0.269

P-value (%)

FC

105.8 24.84

0.009 0.317

11.33

0.009

34.98

100

27.65

0.018

50.35

0.005

7.65 6.7

0.052 (83) 0.081

34.39 5.01

100 100

19.91 6.52

0.005 0

23.96 8.26

0.007 0.006

2.05

0.011 (41)

2.77

100

2.51

0.022 (87)

2.44

0 (83)

1.69

0.027 (41)

1.93

100

2.07

0.026 (68)

3.62

0.036

2.82

0.02

2.89

100

2.48

0.005 (50)

3.44

0.012

2.82

0

4.09

100

2.9

0.048

5.64

0.18

21.18

100

20.83

0.032

30.48

0.005

Regulation of cell growth

X89749

Inhibitor of TGFb signalling

2.79

0.107

2.56

100

2.6

0.011

3.62

0.067

J04103

Transcription factor, induced in HCC

3.94

0.102 (91)

3.44

100

3.61

0.002

4.25

0

M28845

Zinc-finger encoding gene, regulates tumour angiogenesis

0.103 (91)

9.06

100

6.65

0.007 (93)

8.26

0.021

L25274

Adhesion molecule, involved in tumour development

3.84

0.006

5.18

100

3.89

0.032

4.69

0.008

M64292 D83745

Negative control of cell growth Antiproliferative protein

6.46 7.17

0.111 (66) 0.093

5.64 8.48

100 100

5.18 10.15

AF068182

Central linker protein in B-cell activation

2.25

0

3.65

100

3.48

0.036

4.54

0.011

M26270

Fatty acid biosynthesis; upregulated in HCC Fatty acid degradation

L21768

Metabolism Stearoyl-CoA desaturase 2

FC

X81579

eps-15

Defense response TIS21 B-cell translocation gene 3 (ANA) BLNK

%a

V00727

M92420

Angiogenesis Early growth response (EGR-1) Adhesion Alcam

FC

Platelet-derived growth factor a, induced in HCC Transforming growth factor a, induced in HCC Substrate of the EGF-receptor, transforming capacity Oncogene, signal transduction

TGFa

FBJ osteosarcoma oncogene (c-fos) Insulin-like growth factor-binding protein I (IGFBP1) TGIF Transcription factor ets-2

Mucin-associated polypeptide Post-transcriptional regulator, overexpressed in HCC Tetraspanin, late endosome marker Viral envelope-like protein Unknown

Large size (10 mm)

M63335

18.8

10.9

0.186 (75)

0.093 (25) 0.001

5.92 22.42

0.002 (83) 0.074

12.13

0.062

33.4

100

34.91

0.062

28.62

0.01

27.02

0.033

22.25

100

20.03

0.019

23.78

0.05


(a) Upregulated in EGF-induced liver tumoursb Upregulated in transgenic liver and tumours Unknown function Trefoil factor 3 D38410 H19mRNA X58196

Medium size (5 mm)

Table 2 (Continued ) Gene

ACC

Gene description



FC Retinol-binding protein I Extracellular matrix Nidogen1 Miscellaneous Phospholipid scramblase 1 (TRA1)

%a

FC

13.29

100

10.03

Vitamin A, E binding

7.87

0.005

L17324

Basement membrane component, cell–matrix interaction, cell adhesion

1.95

0.002 (100)

3.05

100

2.26

D78354

Potential role in growth factor signalling pathways; associated with leukomogenesis

3.61

0.083

4.87

100

4.86

M31885 U20735 AF016294

Cell cycle control Cell cycle control Mitosis-specific phosphorylation of cytoskeletal protein Tumour cell proliferation marker Helix–loop–helix protein; inactivates p16/pRB pathway in prostate cancer Transcription factor, proto-oncogene Member of ETS family, involved in cancer

P-value (%) 0.001

Large size (10 mm) FC

P-value (%)

9.99

0.009

3.66

0.018 (100)

0.003

5.65

0.016

0 (100)

1.19

Absent (100) Absent

2.85 2.79 5.74

100 100 100

1.92 2.88 4.82

0.007 (68) 0.019 (87) 0.026 (0)

3.07 2.68 6.77

0.036 0.119 (83) 0.05 (66)

1.28

0.004 (66)

3.88

100

2.64

0.008

3.55

0.074 (83)

Absent

4.41

100

1.57

0.020 (12)

2.98

0.013 (66)

Absent

3.38

100

1.97

0.232 (0)

2.77

0.091 (58)

0.202 (16)

3.19

100

2.88

0.011 (25)

2.22

0.047 (25)

Ets transcription factor (ELF3) Signalling Cell adhesion kinase Cytoskeleton villin

1.84

L57509

CAK receptor kinase

Absent

7.31

100

5.03

0.006 (25)

9.2

0.013

M98454

Matrix protein in microtubuli; upregulated HCC

Absent

8.75

100

1.45

0.166 (25)

1.87

0.178 (66)

Angiogenesis Calpactin I (annexin II)

M14044

0.019 (75)

7.86

100

7.6

0.018

19.17

0.07

Lymphotoxin b

U16985

Calcium-dependent phospholipid binding; upregulated in proliferating hepatocytes; role in angiogenesis TNF family cytokine, lymph node development; may initiate angiogenesis

Absent

23.58

100

5.73

0.01 (75)

13.19

0.276

1.92

Adhesion claudin-7

AF087825

Tight junction adhesion protein, activates processing of pro-matrix metalloproteinase-2

Absent

4.69

100

Cell death Cell death factor CIDE-A

AF041376

DNA fragmentation, activation of apoptosis

Absent

5.92

100

Absent

3.94

0.068 (50)


junB

FC

X60367

Overexpressed at the onset of small tumours Cell cycle promotion CyclinB1 X64713 CyclinD1 AI849928 Cell division cycle control M38724 protein 2a (p34 cdc2) Ki67 X82786 Transcription factors Inhibitor of DNA binding 1 (Id-1)

P-value (%)

Medium size (5 mm)

Absent

4.48

0.225 (33)

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Table 2 Gene

ACC

Gene description

(Continued )



FC Invasion, metastasis Minopontin (osteopontin)

Proteolysis, peptidolysis CarboxypeptidaseE Metabolism Nonallelic mRNA for pancreatic a-amylase isoenzyme (pCEPa12) Transport Rab3D Solute carrier family 7 Immune response Toll-like receptor 6 (TLR6)

Serpinb6

FC

P-value (%)

P-value (%)

FC

1.26

0.422 (0)

18.22

100

2.01

0.593 (25)

3.39

0.067

1.35

0.046 (0)

4.49

100

2.8

0.024 (75)

2.06

0.001 (8)

Absent

10.67

100

17.29

0.041

47.42

0.177

X61232

Abundantly expressed in hepatoma and HCC

0.6

0.253 (58)

3.71

100

5.02

0.195

52.71

0.162

X02578

Overexpressed in lung cancers

1.67

0.150 (60)

63.51

100

47.27

0.287 (75)

85.87

0.14

M89777

Small GTPase, regulatory role in vesicular transport Cationic amino-acid transporter

Absent

14.73

100

8.16

0.002

13.87

0.056

Absent

6.05

100

7.03

0.001

5.13

0.001 (66)

0.013 (81)

3.67

0.015

59.61

0.082

M14223 X63782

AJ012754 AB02088

Y17808

Transcription factor LRG-21 U19118 Angiogenesis, invasion, metastasis RhoC X80638 Plasminogen activator inhibitor-1 (PAI-1)

%a

Secreted adhesive glycoprotein, upregulated in HCC DNA synthesis and repair, role in cancer/metastases GPI-anchored protein, correlates with malignancy of mouse tumours

Miscellaneous Endothelial monocyte U41341 activated polypeptideI (EMAP) Other genes upregulated in tumours Signalling Ect2 oncogene L11316 A6 related protein

FC

Large size (10 mm)

M33960 U25844

Activation of Nf-kB and c-Jun N-terminal kinase (JNK), immune response

2.22

0.002 (58)

7.19

100

3.75

Tumour-derived cytokine

3.45

0.048 (33)

28.63

100

17.84

Signal transduction, Rho-specific exchange factor Mouse homolog of human protein kinase

2.19

0.088 (58)

3.96

100

2.83

0.01 (93)

2.4

0.144 (50)

6.42

100

6.98

0.013

Transcription factor

3.1

0.179 (33)

4.65

100

4.05

0.119

5.49

0.015

Small GTPase, involved in angiogenesis and metastasis Serine protease inhibitor involved in angiogenesis, invasion, metastasis Serine protease inhibitor 3, regulation of tumour progression, inflammation and cell death

2.17

0.026 (66)

4.11

100

3.11

0.01

7.45

0

4.43

0.187 (66)

1.21

0

3.35

0.273 (25)

1.29

0.077 (41)

1.81

100

2.34

0.082 (81)

0.04

3.55 14

12 8.06

0.088 (83) 0.013

0.01 0.007


Ribonucleotide reductase M2 subunit (RR M2) Lymphocyte antigen 6 complex (Ly6d)

X13986

P-value (%)

Medium size (5 mm)

Table 2 (Continued ) Gene

ACC

Gene description



FC

P-value (%)

FC

%a

Medium size (5 mm) FC

P-value (%)

Large size (10 mm) FC

P-value (%)

M15832

Basement membrane-associated protein, cell adhesion, upregulated in human HCC

1.66

0.083 (58)

3.27

100

3.94

0.006 (100)

3.81

0.204 (100)

Cytoskeleton Cytokeratin endoA

X15662

2.01

0.203 (66)

3.14

100

2.4

0 (100)

4.48

0.081 (100)

Cytoceratin endoB

M22832

Intermediate filament protein, specific to carcinoma Intermediate filament protein, specific to carcinoma

2.39

0.098 (83)

3.83

100

2.73

0 (100)

4.74

0.041 (100)

Wound healing Downregulated by retinoic acidinduced growth inhibition in murine teratocarcinoma cells Role in facilitating transport of CO2, regulated by microenvironmental hypoxia Mitochondrial long-chain acyl-CoA thioesterase (eps-15 partner) Binding domain for proline-rich sequences of various structural, regulatory and signalling proteins Cold shock protein

3.94 1.46

0.169 (33) 0.031

8.43 9.03

100 100

7.81 4.45

0 0

16.5 14.42

0.032 0.035

2.22

0.013 (41)

5.26

100

8.0

0.122 (81)

22.79

0.226 (66)

1.36

0.134 (16)

2.05

75

1.66

0.003 (62)

3.4

0.001

1.49

0.034

4.5

100

3.68

0 (57)

5.18

0.036

2.24

0.009 (75)

5.36

100

3.47

0.029 (93)

7.36

0.109

liver tumoursc Y12657 RA oxidation X65128 Cell proliferation inhibitor

3.3 2.42

0.003 (91) 0.005 (75)

5.43 3.98

100 100

4.23 4.81

5.05 6.69

0.004 0.002

X63349

Melanine biosynthesis

2.77

0.059 (58)

13.27

75

0.002 (93)

24.85

0.018

M77497 AF02604

Naphthalene detoxification Sulphotransferase-related protein

1.36 1.7

0.285 (8) 0.089 (50)

10.51 16.27

100 100

0.006 0.01

12.79 16.27

0.006 0.009

Miscellaneous AE binding protein Reduced expression 3 (REX-3)

AF053943 AF05134

Carbonic anhydrase 2

M25944

MT-ACT48

AJ238894

ww domain-binding 5

U92454

rbm3

AB016424

(b) Downregulated in EGF-induced Cyt. P450 retinoic acid Growth arrest-specific 1 (GAS-1) Tyrosinase-related protein-2 (tyrp-2) Cytochrome P450 2f2 SULT-X1

11.3 7.24 8.67

0.001 0.002


Extracellular matrix Procollagen type IV a 1

ACC: accession number; FC: fold change; P-value: P-value in T-test; %: Concordance (%) of change calls in the pairwise comparisons (each tumour compared to each normal liver sample) by which genes were up- or downregulated. If not indicated, concordance ¼ 100%. aInstead of P-value for the pool of small tumours, the concordance (%) is shown, because for a pool no T-test analyses could be carried out. bThe genes had in at least one group of the tumours an FCX3, a P-value in T-test o0.05, a signal intensity >70 and were upregulated in 100% of pairwise analyses, compared with normal liver of control mice. Grey-coloured genes were absent in the control tissues. Grey-coloured rows indicate genes expressed in all tumours and not in controls. cThe genes had in at least one group of tumours an FC o3, a P-value in T-test o0.05, were downregulated in pairwise analyses when compared to normal liver of control mice and a signal intensity >70 in control liver. Grey-coloured rows are genes, which were expressed in all normal tissues but not in tumours.

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(Table 1), and these included Ets transcription factor, calpactin, stearoyl-CoA desaturase 2 and Rab3D among others (Table 2). In contrast, Gas1 and tyrosinaserelated protein-2 were detected in all normal liver samples, but not in tumours (Tables 1, 2). Notably, among genes which were upregulated in tumours (Table 2a), we found several genes with an inferred role in tumour development such as growth factors TGFa, some components of EGF/ TGFa-mediated signalling pathway (c-fos, eps-15, MTAct48), EGR-1, cell division cycle control protein 2a, proto-oncogene junB, IGFBP1, cyclinB1, cyclinD1 and Id-1, the latter two interfering with the Rb pathway. Additionally, genes that are associated in human cancers with metastasis and angiogenesis like rhoC, PAI-1 and calpactin 1 (annexin II) were upregulated. Further, we observed overexpression of some genes coding for components of the cytoskeletal network (villin, cytokeratin endoA and B) and basal membrane (collagen IVa1, nidogen), and proteins that are involved in survival factor signalling pathway like IGFBP1 and TLR6. The most highly overexpressed genes in all tumours included H19 mRNA, trefoil factor 3, stearoyl-CoA desaturase 2, lipoprotein lipase, nonallelic mRNA for pancreatic a-amylase isoenzyme, CD63 antigen, G7e – their contribution in carcinogenesis may be of great importance, but requires further study. The expression of certain genes was obviously enhanced as part of a defence response to the free development, for example, cell death factor CIDE-A, TIS21 and ANA (BTG3). In strong contrast, most of the repressed genes code for proteins important for liver function, including drug binding, detoxification and metabolism (see Table 2b). We thus provide evidence for loss of metabolic competence in liver tumour tissue. The expression of a selected number of genes was additionally analysed by semiquantitative RT–PCR (Figure 2d). There was good agreement between microarrays and RT–PCR experiments, for example, no expression of G7e and Trefoil factor 3 in controls but strong expression of TGIF and LRG-21 in large tumours and strong expression of Trefoil factor 3 and G7e in small tumours. In the case of calpactin, the level of induction determined by RT–PCR and the microarray differed, though an identical trend was observed, for example, induction. Presumably, the different methods of gene expression analysis (see Materials and methods) and the algorithm applied for data analysis of microarray experiments produced different estimate of induction levels.

Discussion Liver pathology Morphological phenotyping enabled new insights into the process of hepatocellular carcinogenesis. Particularly, the onset and development of malignant tumours were followed. Notably, no benign tumours were found. We assume LCD to be the precursor lesion being at the Oncogene

edge of malignant change. No inflammatory process was observed and no cirrhotic conversion of liver tissue was noted. This contrasts with microscopic lesions frequently seen in humans, where cirrhotically altered organs are the main precursor of carcinogenesis (Kubicka et al., 2000). An important finding of our study was the 100% incidence of malignant tumour within 6–8 months after birth, and we suggest the following sequence of events: diffuse LCD merges into multiple DF and DN, with local growth towards HCC. Transcript profiling The transgenic mouse line EGF2B develops HCCs (To¨njes et al., 1995) as a consequence of overexpression of a secretable form of EGF, which is known to be a strong mitogen for liver cells. There is cumulative evidence for EGF or TGFa overexpression to be necessary, but not sufficient in inducing carcinogenesis in mice (Sandgren et al., 1993; Wu et al., 1994; To¨njes et al., 1995). As shown in our study and by others, undue exposure to EGF predisposes liver tissue to cancer. Large-scale expression analysis enabled initial changes to be studied. We compared the expression profiles of tumour-free liver of transgenic with normal liver of control mice. In transgenic displastic liver, certain genes were upregulated at the same level as in tumours. These genes include eps-15, a substrate of EGF-R with transforming capacity (Alvarez et al., 1995), and c-fos, a transcription factor of the EGF signalling pathway known to be induced by EGF and other growth factors (Dey et al., 1991). Therefore, transcriptional activation of the EGF/TGFa signalling pathway in the liver and tumours of EGF2B transgenic mice is a prominent feature. Similarly, the transcription factor EGR-1, which is co-regulated with c-fos (Chavrier et al., 1989), was also overexpressed. Recently, EGR-1 was shown to play an important role in tumour angiogenesis and growth (Fahmy et al., 2003). Remarkably, we observed a strong upregulation of insulin-like growth factor-binding protein 1 (IGFBP1) (Table 2a), a hepatocyte-derived and secreted protein, which is required for liver regeneration. The recent report of Leu et al. (2003) provided evidence for IGFBP1 to function as a critical hepatic survival factor in the liver by reducing the level of proapoptotic signals. Therefore, overexpression of IGFBP1 may lead to enhanced survival of tumorous liver cells. Further, the role of BLNK (Table 2a), which is involved in activation of nuclear factor NF-kB (Tan et al., 2001) and Toll-like receptor, which activates both NF-kB and c-jun N-terminal kinase (JNK) (Takeuchi et al., 1999), is difficult to comprehend, because these genes are mainly induced in immune-competent cells after stimulation. On the other hand, their expression in the liver would indicate an imbalance of the IGF/ IGFBP system of EGF2B transgenic mice and support the survival of tumorous liver cells. As no infiltration of the tumour tissue by immune cells was observed, enhanced expression of these genes may support a function beyond immune-competent cells. There is


1817

a clear need to clarify the role of these genes in HCC formation. Further, elevated expression level of TGIF, an inhibitor of antigrowth factor TGFb-responsive transcription (Melhuish et al., 2001) and transcription factor ets-2 among others (Table 2a), can also contribute to the initial transformation of hepatocytes in EGFoverexpressing mice. Overexpression of an adhesion molecule Alcam, a member of the immunoglobulin superfamily, may contribute to the invasive capabilities of transformed liver cells (Choi et al., 2000). Remarkably, two genes involved in lipid metabolism, for example, lipoprotein lipase and stearoyl-CoA desaturase-2, were uniquely expressed or strongly induced in liver and tumours of transgenic mice. This suggests that important functions of the coded genes in lipid and fatty acid metabolism of tumour cells most probably contribute to cell membrane synthesis. A comparison of the expression profiles of EGFoverexpressing tumour-free transgenic liver with those observed in manifested small liver tumours allows an identification of candidate genes additionally required for malignant transformation. We observed protooncogene junB, cell division cycle control protein p34cdc2, cyclinD1, cyclinB1, Id-1, minopontin, villin, claudin-7, ribonucleotide reductase (RR M2), Ly6d and cell adhesion kinase, which were not changed in transgenic liver (mostly not detected both in control and transgenic liver), but were dramatically induced in the pool of small tumours (Table 2a). CyclinD1 forms a complex with cdk4 to inactivate Rb by phosphorylation and Id-1 was shown to inactivate the p16/pRB pathway by preventing the deactivation of cyclin/cdk complexes in human prostate cancer (Ouyang et al., 2002). Thus, the overexpression of cyclinD1 and Id1 in liver cells can interfere with pRb pathway, leading to exaggerated cell division signalling. We observed overexpression of minopontin (osteopontin), RRM2 and Ly6d in small tumours of EGF2B mice. Overexpression of the latter genes was observed in human cancers and correlated with invasiveness and metastatic potential (Chen et al., 2000; Witz, 2000; Gotoh et al., 2002). Many regulated genes showed permanent increase or decrease in their expression level during carcinogenesis. Importantly, we failed to detect Gas-1 in all tumour samples, while its expression was evident in all controls and a low expression in two of three nontumorous transgenic livers (see Table 2a). This protein is of importance in growth suppression and it was suggested that pRb and/or p53 play an active role in mediating the growth-suppressor effect of Gas-1 (Del Sal et al., 1994, 1995; Evdokiou and Cowled, 1998). It appears that loss of growth control through GAS-1 may be a necessary event in the multi-step neoplastic transformation. Additionally, we observed a significant increase in the transcript level of the small GTPase rhoC (Table 2a), and of Ect2 (Table 2a), the guanine nucleotide exchange factor for Rho GTPases (Tatsumoto et al., 1999), which plays a critical role in Rho activation (Kimura et al.,

2000). RhoC is involved in controlling cell motility and focal adhesion, and was recently demonstrated to be associated with vascular invasion in human HCC (Okabe et al., 2001) and may play a role in metastasis of human melanoma (Clark et al., 2000). Transcript profiling of large-size tumours evidenced induction of PAI-1, serpin b6, calpactin (annexin II), carboxypeptidase E and EMAP. Their specific role in tumour progression still needs to be delineated. It is apparent that the EGF-transgenic mouse model is valuable for the study of HCC. Indeed, the tumours in these animals share known features with those previously observed in humans following EGF induction or by malignant transformation, and our analyses revealed novel candidate genes associated with tumorigenesis. This included Rab 3D, cell adhesion kinase, trefoil factor 3, A6-related protein, LRG-21, cold shock protein cbm-3, AE-binding protein, ww domain-binding protein, Tra1, fibrinogen-like protein and tyrosinaserelated protein-2, but their specific role in liver carcinogenesis needs to be elucidated. In conclusion, we report tumour size-dependent gene expression in EGF-induced HCCs. We observed enhanced expression of villin, cell death factor CIDE-A, claudin 7 and junB in specifically small tumours. We further observed induction of autocrine growth with increased expression of TGFa, PDGFa and eps-15, the latter being a substrate for the EGF receptor with transforming capacity. In all tumours, induction of c-fos and egr-1 was significant, as was the induction of the survival factor IGFBP1, which provided tumours with an important advantage. In large tumours, RhoC activation was linked to vascular invasion. Finally, loss of sensitivity to antigrowth signals could be traced back to induction of TGIF, Id-1 and cyclinD1 to interfere with the pRb control of cell division, whereas the repression of the tumour suppressor gene Gas-1 allowed for proliferation invasion and metastatic growth. In future, promoter analyses of deregulated genes is needed to identify the molecular rules of promoter activation and the transcription factors acting in concert in malignancies of the liver. Most certainly, the EGF transgenic mouse model contributes towards a molecular understanding of liver carcinogenesis

Materials and methods Maintenance of the transgenic mouse line The EGF2B transgenic line was described earlier by To¨njes et al. (1995). Transgenic mice were maintained as hemizygotes in the CD2F1-(DBA/2 Balb/c) background. PCR was carried out with Platinum PCRSuperMix (InVitrogen). Annealing temperature and the number of cycles are indicated in brackets after each primer pair. The transgene was verified by PCR of DNA extracted from tail biopsies (Hogan et al., 1994) and the following forward primer (fp) and reverse primer (rp) pair was used for a transgene-specific amplification: forward primer: 50 -CTAGGCCAAGGGCCTTGGGGGCTC TTGCAG-30 ; reverse primer: 50 -CATGCGTATTTGTCCAG AGCTTCGATGTA-30 (611C, 32 cycles). Oncogene


1818 Gene expression studies by RT–PCR RT–PCR was employed to confirm expression of the transgene and some selected genes in controls, tumours and nontumorous liver of transgenic mice. The primer design was carried out with the MacVectort 6.5.3 software and cross-reaction of primers with other genes was excluded by comparison of the sequence of interest with a data bank (Blast 2.0 US National Centre for Biotechnology information). We also used UCSC genome bioinformatics to design intron-spanning primers. Total RNA was isolated with the Qiagen RNA purification kit according to the manufacturer’s instructions. Reverse transcription was carried out using Omniscript (Qiagen), Oligo-dT primers (InVitrogen) and RNasin (Promega), followed by PCR amplification (see above) with the following primer pairs: EGF, fp: GCTGTGACGGTCCTTACAATG; rp: CAGTTC CCACCACTTCAGGTC (611, 29 cycles). Calpactin, fp: GAG CATCAAGAAAGAGGTCAAAGG; rp: TTCAGTCATCC CCACCACACAG (651C, 28 cycles). LRG21, fp: AGATGAG AGGAAAAGGAGGCGG; rp: GGGTGGAAAAGGAGGA TTCAGTAAG (651C, 28 cycles). G7e, fp: GGTCTTTCACA AGCAGTGCCTG; rp: AAACCAAGTTCCAATGGGGG (571C, 32 cycles). TFF3, fp: GCAAATGTCAGAGTGGACT GTGG; rp: GGCTGTGAGGTCTTTATTCTTCAGG (621C, 28 cycles). TGIF, fp: AACGCCTATCCCTCAGAGCAAG; rp: GTCCAACTACGCAGGAATGAAATG (651C, 30 cycles). b-Actin was used as a housekeeping gene, because its expression was found to be unchanged in controls (nontransgenic), transgenic (nontumorous) and tumours of EGF2B mice (microarray analyses) The following primer pair was used: fp: GGCATTGTTACCAACTGGGACG; rp: CTCTTTGATGT CACGCACGATTTC (651C, 25 cycles). PCR reaction products were separated on 1% agarose gels stained with ethidium bromide and photographed on a transilluminator (Kodak 1544 CF). A semiquantitative measurement was carried out using Kodak 1D software (v.3.5.3) Histology Tumour tissue and tumour-free tissue of transgenic animals, as well as liver from control animals, were fixed in 4% formaldehyde in PBS and embedded in paraffin by standard operating procedures. Paraffin blocks were sectioned into 3– 5 mm thick slices and stained with haematoxylin and eosin (H and E), haematoxylin only (H) and PAS for light microscopic evaluation. Sample collection and preparation Mice were anaesthesized by an overdose of CO2 at the age of 6.5–9 months. The thorax was opened by standard surgical procedures and the liver was explanted and rinsed with PBS. The tumours were inspected macroscopically and separated from the liver. One group consisted of four analysed tumours of about 5 mm in size (medium size) derived from n ¼ 4 animals, and a second group of three tumours of about 10– 15 mm (large size) were derived from n ¼ 3 animals. Five tumours from n ¼ 3 animals were about 1 mm (small size) and were pooled to improve the yield in RNA. Tumour-free tissue was taken from the liver of (n ¼ 3) tumour-bearing transgenic mice. Healthy liver from n ¼ 4 non transgenic CD2F1 mice of about the same age were used as controls. Upon anatomical preparation tissue was frozen immediately in liquid nitrogen. RNA isolation and production of copy RNA The cRNA samples were prepared following the Affymetrix Gene Chips Expression Analysis Technical Manual (Santa Oncogene

Clara, CA, USA). Briefly, total RNA was isolated from frozen tissues using QIAGEN’s RNeasy total RNA isolation procedure. A second cleanup of isolated RNA was performed using the same RNA isolation kit. In all, 10 mg of total RNA was used for the synthesis of double-stranded cDNA with Superscript II RT and other reagents from Invitrogen Life Technologies. HPLC-purified T7-(dT)24 (GenSet SA) was used as a primer. After cleanup, double-stranded cDNA was used for the synthesis of biotin-labelled cRNA (Enzos BioArray High Yield RNA Transcript Labeling Kit, Affymetrix). cRNA purified with RNeasy spin columns from Qiagen was cleaved into fragments of 35–200 bases by metalinduced hydrolysis. Array hybridization and scanning A measure of 10 mg of biotinylated fragmented cRNA was hybridized onto the Murine Genome U74Av2 Array (MG-U74Av2). The array consists of 12 488 probe sets, that represent RefSeq annotated sequences (B6000) in the Mouse UniGene database, as well as B6000 EST clones. The hybridized, washed and coloured arrays were scanned using the Agilent GeneArrays Scanner. Scanned image files were visually inspected for artifacts and then analysed, each image being scaled to an all probe set intensity of 150 for comparison between chips. The Affymetrixs Microarray Suite (version 5.0) was used to control the fluidics station and the scanner, to capture probe array data and to analyse hybridization intensity data. Default parameters provided in the Affymetrix data analysis software package were applied in running of analyses. Data analysis The hybridization values for each gene probe presented on the array with a set of 16 perfect and mismatch oligonucleotide pairs were calculated within Affymetrixs Microarray Suite 5.0 Software, using the manufacturer’s statistical algorithm. The results were reported as numeric expression values – signal intensities and absolute information – detection calls ‘Present’ or ‘Absent’ produced by two independent algorithms. The results of a single comparison analysis between two different arrays were reported for each gene as signal logarithm ratio (log2ratio) and a change call ‘Increase’ or ‘Decrease’. Multiple data from replicate samples were evaluated and compared using statistical analyses with the Affymetrixs Data Mining Tool 3.0 (DMT). The average and standard deviation statistics within Affymetrixs DMT was used to summarize the expression level (the signal values) for each transcript across the replicates. The unpaired one-sided T-test converting P-value to a two-sided P-value was used to determine the direction and significance of change in a transcript’s expression level between sets of tumours (except for the pool of small tumours), transgenic liver and normal livers, with the P-value cutoff determined as 0.05. Besides, only those genes that were detected (had call ‘Present’) in all samples of a tumour set or transgenic liver for the upregulated genes and in all control livers for the downregulated genes were taken into consideration as differentially expressed. Fold-change values were calculated as the ratio of the average expression levels for each gene between two tissue sets. Comparison ranking analysis was additionally employed to study the concordance of gene expression changes in pairwise comparisons of tumour samples with control livers. The results are shown as % of ‘Increase’ or ‘Decrease’ calls in individual comparisons, for example, 16 analyses (four tumours versus four controls) for


1819 tumours of median size, 12 (three tumours versus four controls) for tumours of large size and 12 analyses for transgenic liver. The small tumours (B1 mm) were pooled and compared with the four individual controls, resulting in four comparisons.

Acknowledgements The excellent technical assistance of Mrs Edith Aretz, Ms Ines Noack and Mr Albert Rast is gratefully acknowledged. We thank the Lower Saxony Ministry of Culture and Sciences and the Volkswagen foundation for providing a grant to J Borlak.

References Alvarez CV, Shon KJ, Miloso M and Beguinot L. (1995). J. Biol. Chem., 270, 16271–16276. Chavrier P, Janssen-Timmen U, Mattei MG, Zerial M, Bravo R and Charnay P. (1989). Mol. Cell. Biol., 9, 787–797. Chen S, Zhou B, He F and Yen Y. (2000). Antisense Nucleic Acid Drug Dev., 10, 111–116. Choi S, Kobayashi M, Wang J, Habelhah H, Okada F, Hamada J, Moriuchi T, Totsuka Y and Hosokawa M. (2000). Clin. Exp. Metastasis, 18, 45–50. Chung YH, Kim JA, Song BC, Lee GC, Koh MS, Lee YS, Lee SG and Suh DJ. (2000). Cancer, 89, 977–982. Clark EA, Golub TR, Lander ES and Hynes RO. (2000). Nature, 406, 532–535. Del Sal G, Collavin L, Ruaro ME, Edomi P, Saccone S, Valle GD and Schneider C. (1994). Proc. Natl. Acad. Sci. USA, 91, 1848–1852. Del Sal G, Ruaro EM, Utrera R, Cole CN, Levine AJ and Schneider C. (1995). Mol. Cell. Biol., 15, 7152–7160. Dey A, Nebert DW and Ozato K. (1991). DNA Cell Biol., 10, 537–544. Evdokiou A and Cowled PA. (1998). Exp. Cell Res., 240, 359–367. Gotoh M, Sakamoto M, Kanetaka K, Chuuma M and Hirohashi S. (2002). Pathol. Int., 52, 19–24. Hogan B, Beddingtin R, Costantini F and Lacy E. (1994). Manipulating the Mouse Embryo: A Laboratory Manual. Cold Spring Harbor Laboratory: Cold Spring Harbor, NY. Fahmy RG, Dass CR, Sun LQ, Chesterman CN and Khachigian LM. (2003). Nat. Med., 9, 1026–1032. Kimura K, Tsuji T, Takada Y, Miki T and Narumiya S. (2000). J. Biol. Chem., 275, 17233–17236. Kubicka S, Rudolph KL, Hanke M, Tietze MK, Tillmann HL, Trautwein C and Manns M. (2000). Liver, 20, 312–318. Leu JI, Grissey MA and Taub R. (2003). J. Clin. Invest., 111, 129–139.

Melhuish TA, Gallo CM and Wotton D. (2001). J. Biol. Chem., 276, 32109–32114. Okabe H, Satoh S, Kato T, Kitahara O, Yanagawa R, Yamaoka Y, Tsunoda T, Furukawa Y and Nakamura Y. (2001). Cancer Res., 61, 2129–2137. Ostrowski J, Woszczynski M, Kowalczyk P, Wocial T, Hennig E, Trzeciak L, Janik P and Bomsztyk K. (2000). Br. J. Cancer, 82, 1041–1050. Ouyang XS, Wang X, Ling MT, Wong HL, Tsao SW and Wong YC. (2002). Carcinogenesis, 23, 721–725. Ramljak D, Jones AB, Diwan BA, Perantoni AO, Hochadel JF and Anderson LM. (1998). Cancer Res., 58, 3590–3597. Rescan C, Coutant A, Talarmin H, Theret N, Glaise D, Guguen-Guillouzo C and Baffet G. (2001). Mol. Biol. Cell., 12, 725–738. Sandgren EP, Luetteke NC, Qiu TH, Palmiter RD, Brinster RL and Lee DC. (1993). Mol. Cell. Biol., 13, 320–330. Schafer DF and Sorrell MF. (1999). Lancet, 35, 1253–1257. Takeuchi O, Kawai T, Sanjo H, Copeland NG, Gilbert DJ, Jenkins NA, Takeda K and Akira S. (1999). Gene, 231, 59–65. Tan JE, Wong SC, Gan SK, Xu S and Lam KP. (2001). J. Biol. Chem., 276, 20055–20063. Tatsumoto T, Xie X, Blumenthal R, Okamoto I and Miki T. (1999). J. Cell. Biol., 147, 921–928. To¨njes RR, Lohler J, Oı´ Sullivan JF, Kay GF, Schmidt GH, Dalemans W, Pavirani A and Paul D. (1995). Oncogene, 10, 755–758. Wang Y, Ripperger J, Fey GH, Samols D, Kordula T, Wetzler M, Van Etten RA and Baumann H. (1999). Hepatology, 30, 682–697. Yamaguchi K, Carr BI and Nalesnik MA. (1995). J. Surg. Oncol., 58, 240–245. Witz IP. (2000). J. Cell. Biochem. Suppl., 34, 61–66. Wu JC, Merlino G, Cveklova K, Mosinger Jr B and Fausto N. (1994). Cancer Res., 54, 5964–5973.

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