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. 2004 Jul 28;130(11):636–644. doi: 10.1007/s00432-004-0586-3

Alterations in gene expression associated with the overexpression of a splice variant of DNA methyltransferase 3b, DNMT3b4, during human hepatocarcinogenesis

Yae Kanai 1,✉, Yoshimasa Saito 1,2, Saori Ushijima 1, Setsuo Hirohashi 1
PMCID: PMC12161791  PMID: 15490234

Abstract

Purpose

Overexpression of a splice variant of DNA methyltransferase 3b, DNMT3b4, correlates significantly with DNA hypomethylation in pericentromeric satellite regions, which is known to result in centromeric decondensation and enhanced chromosomal recombination in precancerous conditions and hepatocellular carcinomas (HCCs). We aimed to elucidate further the significance of DNMT3b4 during human hepatocarcinogenesis.

Methods

DNMT3b4-transfected human epithelial 293 cells were characterized using growth rate measurements, gene expression microarray, and quantitative reverse transcription-polymerase chain reaction (RT-PCR) analyses. RT-PCR was also performed on eight normal liver specimens, 45 noncancerous liver specimens showing chronic hepatitis or cirrhosis, which are considered to be precancerous conditions, and 56 HCCs.

Results

The growth rate of the DNMT3b4 transfectants was about double that of mock-transfectants. Induction of signal transducer and activator of transcription 1 (STAT1), an effector of interferon signaling, and of a set of downstream genes implicated in such signaling, was observed in the DNMT3b4 transfectants. There was significant correlation between the mRNA expression levels of DNMT3b4 and STAT1 in HCCs. mRNA expression levels of STAT1 and the three downstream genes examined were all significantly elevated in the chronic hepatitis and cirrhosis specimens compared with the normal liver specimens. Among the HCCs, the mRNA expression levels of STAT1 and the downstream genes were higher in tumors without portal vein involvement than in more malignant HCCs with portal vein involvement. Significant correlations between the mRNA expression levels of STAT1 and each of the downstream genes were observed in the tissue samples.

Conclusions

Overexpression of DNMT3b4 is involved in human hepatocarcinogenesis, even at the precancerous stages, not only by inducing chromosomal instability but also by affecting the expression of specific genes.

Keywords: DNA methylation, DNMT3b4, STAT1, Interferon inducible protein, Hepatocellular carcinoma

Introduction

DNA methylation is important in gene silencing, chromatin remodeling, and genome stability. Accordingly, aberrant DNA methylation is one of the most consistent epigenetic changes in human cancers (Jones and Baylin 2002). Generally, the overall level of DNA methylation is lower in cancer cells than in normal cells, although a number of tumor suppressor genes are silenced by DNA methylation of the CpG islands around the promoter regions in cancer cells (Jones and Baylin 2002). Furthermore, evidence indicating that aberrant DNA methylation is involved even in the early, precancerous stages of human carcinogenesis has now accumulated (Kanai et al. 1996; Kanai et al. 1997; Kanai et al. 1998; Kanai et al. 1999; Kanai et al. 2000; Eguchi et al. 1997; Kondo et al. 2000; Saito et al. 2001).

The major and best-known DNA methyltransferase (DNMT) is DNMT1, and we have reported increased expression of this enzyme in various precancerous conditions and cancers (Sun et al. 1997; Saito et al. 2001; Saito et al. 2003; Nakagawa et al. 2003). In colorectal and stomach cancers, increased DNMT1 expression correlates significantly with the CpG island methylator phenotype (Kanai et al. 2001; Etoh et al. 2004), which is defined as frequent hypermethylation of CpG islands that are not methylated in normal tissues (Toyota et al. 1999).

Another DNMT, DNMT3b, is required for the de novo DNA methylation of pericentromeric satellite regions that occurs during normal development in the mouse (Okano et al. 1999). DNA hypomethylation in these regions is known to result in centromeric decondensation and enhanced chromosomal recombination (Kokalj-Vokac et al. 1993; Jeanpierre et al. 1993). We have previously shown that overexpression of a splice variant of DNMT3b, DNMT3b4, which lacks the conserved methyltransferase motifs IX and X, correlates significantly with DNA hypomethylation of pericentromeric satellite regions in precancerous conditions and hepatocellular carcinomas (HCCs) (Saito et al. 2002). During this earlier study, transfection of human epithelial 293 cells with DNMT3b4 cDNA induced DNA demethylation of satellite 2. Our findings led us to conclude that overexpression of DNMT3b4 (which may lack DNMT activity and compete with DNMT3b3, the major splice variant in normal liver tissues, for targeting to pericentromeric satellite regions) results in DNA hypomethylation in these regions even in precancerous conditions, and plays a critical role in human hepatocarcinogenesis by inducing chromosomal instability.

During the present study, we examined the characteristics of DNMT3b4-transfectants in more detail, in order to gain further insight into the significance of DNMT3b4 during human hepatocarcinogenesis. We found that the growth rate of DNMT3b4 transfectants approximately doubled compared with that of mock-transfectants soon after the introduction of DNMT3b4, when allelic imbalance may not yet have accumulated. We assumed that this change was caused by altered gene expression. Gene expression profiling was then carried out on the DNMT3b4 transfectants by microarray analysis. Alterations in the expression of a set of specific genes were confirmed in the DNMT3b4 transfectants as well as in liver tissue samples showing precancerous changes and established HCCs by quantitative reverse transcription (RT)-polymerase chain reaction (PCR) analysis.

Materials and methods

Characterization of cell growth in DNMT3b4 transfectants

During our previous study (Saito et al. 2002), full-length human DNMT3b4 cDNA was transfected into human embryonic kidney cells that had been transformed using adenovirus type 5 DNA (293 cells) (Graham et al. 1977), and myc-tagged DNMT3b4 expression was confirmed by Western blotting. These DNMT3b4 transfectants (clones 3b4–4 and 3b4–5), and their corresponding mock-transfectants (clones mock-1 and mock-2), were used in the present study. About 50 days after transfection, 3×105 cells were plated into 6-well microtiter plates. Two wells were trypsin-treated at each time point (3 days, 6 days, 8 days, 10 days, and 12 days) and the cells were counted in a Cell Counter (Beckman Coulter, Calif., USA).

Gene expression microarray analysis of DNMT3b4 transfectants

Total RNA was isolated from each transfectant using Trizol reagent (Invitrogen, Carlsbad, Calif., USA). Contaminant DNA was removed by digestion with RNase-free DNase (Promega, Southampton, UK) in the presence of RNasin RNase inhibitor (Promega). cRNA was prepared from the total RNA using the Super Script Choice System (Invitrogen) and the BioArray High Yield RNA Transcript Labeling kit (Enzo Diagnostics, Farmingdale, N.Y., USA). Hybridization of each cRNA to human U95Av2 oligonucleotide probe arrays (corresponding to 12,686 human genes and expressed sequence tags; Affymetris, Santa Clara, Calif., USA) and detection of the resulting signals were performed as instructed by the manufacturer. Analysis of the full data was performed using Affymetrix Microarray Suite 4.0 software. For this analysis, the hybridization intensity data were normalized to 1,000 total signal intensities for each array. The data were further filtered and analyzed using GeneSpring Software (version 4.2.1; Silicon Genetics, San Carlos, Calif., USA).

Patients and tissue specimens

Fifty-six primary HCCs and corresponding noncancerous liver tissue specimens were obtained from surgically resected materials of 45 patients who were treated at the National Cancer Center Hospital, Tokyo, Japan. The patients comprised 40 men and 5 women with a mean (±SD) age of 60±12 years (range 23–79). Of these, 11 were positive for hepatitis B virus surface antigen, 27 were positive for anti-hepatitis C virus antibody, six were positive for both hepatitis B virus surface antigen and anti-hepatitis C virus antibody, and one was negative for both. Histological examination of the noncancerous liver tissue specimens from the HCC patients revealed findings compatible with chronic hepatitis in 21 and cirrhosis in 24. Of the 56 HCCs, six were classified as well differentiated, 28 as moderately differentiated and 22 as poorly differentiated. Twenty-one tumors revealed involvement of the portal vein. For comparison, normal liver tissue specimens showing no remarkable histological findings were obtained from eight hepatitis B virus surface antigen- and anti-hepatitis C virus antibody-negative patients who underwent partial hepatectomy for liver metastasis of primary colon cancer.

Quantitative RT–PCR in DNMT3b4 transfectants and tissue specimens

First-strand cDNA was prepared from the total RNA of each transfectant and tissue sample using a random hexadeoxynucleotide primer and SuperScript RNase H- reverse transcriptase (GIBCO-BRL, Gaithersburg, Md., USA). Specific primer sets (Table 1) were designed using Primer Express software (Applied Biosystems, Foster City, Calif., USA). To test the quality of the extracted RNA and to standardize the amount of RNA applied, glyceraldehydephosphate dehydrogenase (GAPDH) mRNA was amplified using 5’-GAAGGTGAAGGTCGGAGTC-3’ (sense) and 5’-GAAGATGGTGATGGGATTTC-3’ (antisense) primers, resulting in a 226-bp product (Applied Biosystems). The PCR reactions were performed using the SYBR Green PCR Core Reagents kit (Applied Biosystems). Real-time detection of the emission intensity of SYBR Green bound to double-stranded DNA was performed using the GeneAmp 5700 Sequence Detection System (Applied Biosystems). cDNAs derived from the HCC cell line Alexander (Alexander et al. 1976) or the stomach cancer cell line MKN1 (Aizawa et al. 1989) were used as calibration samples. Relative quantification values were obtained from the threshold cycle (the cycle during which enhanced signal intensity associated with an exponential increase in PCR products began to be detected) using Applied Biosystems analysis software according to the manufacturer’s manuals. The quantitative PCR analyses were performed in triplicate for each sample-primer set, and the mean of the three experiments was used as the relative quantification value. After 40 PCR cycles, the reaction products were separated electrophoretically on a 3% agarose gel and stained with ethidium bromide to confirm that only specific products had been obtained during each amplification. The mRNA levels for each target gene were expressed as the ratio relative to GAPDH mRNA for each sample.

Table 1.

The specific primer sets used for quantitative reverse transcription-polymerase chain reaction analysis in DNMT3b4 transfectants and tissue specimens

Target gene Primer sets PCR product
(GenBank accession number) (bp)
Signal transducer and activator of transcription 1 (M97935) 5’-TATGGGACCGCACCTTCA-3’(sense) 121
5’-AACTGGACCCCTGTCTTCAAG-3’(antisense)
Interferon-stimulated gene 12 (X67325) 5’-GGCAGCCTTGTGGGTACTC-3’(sense) 121
5’-GGAGCTAGTAGAACCTCGCAATG-3’(antisense)
Interferon-inducible protein 9–27 (J04164) 5’-GTCCCTGTTCAACACCCTCTT-3’(sense) 149
5’-CCCAGATGTTCAGGCACTTG-3’(antisense)
Interferon-inducible 56 Kd protein (M24594) 5’-GAGGAGCCTGGCTAAGCAA-3’(sense) 151
5’-GCATTTCATCGTCATCAATGG-3’(antisense)
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Statistical methods

Correlations between the mRNA expression levels of DNMT3b4 and signal transducer and activator of transcription 1 (STAT1), the mRNA expression levels of STAT1 and each downstream gene and the mRNA levels of these specific genes and the clinicopathological characteristics of the tissue samples were analyzed using the Kruskal-Wallis test or Mann Whitney U-test. Differences with P-values of less than 0.05 were considered significant.

Results

Effect of DNMT3b4 transfection on cell growth in human epithelial 293 cells

The growth curves for the DNMT3b4 transfectants (clones 3b4–4 and 3b4–5) and mock-transfectants (clones mock-1 and mock-2) are presented in Fig. 1. By about 50 days after transfection, the growth rate of the DNMT3b4 transfectants was about double that of the mock-transfectants.

Fig. 1.

Fig. 1

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Growth curves for the DNMT3b4 transfectants (clones 3b4–4 and 3b4–5) and mock-transfectants (clones mock-1 and mock-2). Full-length human DNMT3b4 cDNA was transfected into 293 cells during our previous study (Saito et al. 2002). About 50 days after transfection, 3×105 cells were plated into 6-well microtiter plates. Two wells were trypsin-treated at each time point (3 days, 6 days, 8 days, 10 days, and 12 days) and the cells were counted. Open circles mock-1, open squares mock-2, solid circles 3b4–4, solid squares 3b4–5

mRNA expression levels of STAT1 and other genes implicated in interferon signaling in DNMT3b4 transfectants

Genes that were upregulated at least three times in both clones 3b4–4 and 3b4–5 compared with clones mock-1 and mock-2 during the microarray analysis are listed in Table 2. Eight of the 12 listed genes are implicated in interferon signaling. However, genes that encoded interferons themselves were not listed.

Table 2.

Genes that were upregulated at least three times in both clones 3b4–4 and 3b4–5 compared with clones mock-1 and mock-2 during the microarray analysis

GenBank accession number Genes
M97935 Signal transducer and activator of transcription 1
X67325 Interferon-stimulated gene 12
J04164 Interferon-inducible protein 9–27
M24594 Interferon-inducible 56 Kd protein
U53831 Interferon regulatory factor 7B
M87503 Interferon-stimulated transcription factor 3
U22970 Interferon-inducible peptide 6–16
M13755 Interferon-induced 17-kDa/15-kDa protein
U18760 Nuclear factor I
U71088 MAP kinase kinase MEK5c
AJ005168 Ketohexokinase
D28137 Bone marrow stromal antigen 2
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We then focused on STAT1, which acts as an effector of interferon signaling. The mRNA expression characteristics of this transcription factor were confirmed more accurately in clones 3b4–4, 3b4–5, mock-1, and mock-2 by quantitative RT-PCR using the specific primer set shown in Table 1. The results confirmed that STAT-1 was overexpressed in both clones 3b4–4 and 3b4–5 compared with clones mock-1 and mock-2 (Fig. 2).

Fig. 2.

Fig. 2

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Results of the quantitative RT-PCR analysis of STAT-1 in DNMT3b4 transfectants (clones 3b4–4 and 3b4–5) and mock-transfectants (clones mock-1 and mock-2). Full-length human DNMT3b4 cDNA was transfected into 293 cells during our previous study (Saito et al. 2002). All examinations were performed using the GeneAmp 5700 Sequence Detection System (Applied Biosystems) as described in the Materials and Methods section. cDNAs derived from the HCC cell line Alexander were used as calibration samples. The mRNA levels for each clone are expressed as a ratio relative to GAPDH mRNA

The overexpression of STAT-1 may in turn induce a set of downstream genes implicated in interferon signaling. The mRNA expression levels of three such downstream genes identified by the microarray analysis [interferon-stimulated gene 12 (GenBank accession number: X67325), interferon-inducible protein 9–27 (J04164) and interferon-inducible 56 kD protein (M24594)] were therefore also examined by quantitative RT-PCR. These analyses showed that the mRNA for interferon-stimulated gene 12 was overexpressed 6.1-fold in clone 3b4–5 compared with clone mock-1, while the mRNAs for interferon-inducible protein 9–27 and interferon-inducible 56 kD protein were overexpressed 1.9-fold and 23-fold, respectively.

mRNA expression levels of STAT1 and other genes implicated in interferon signaling in tissue specimens

The mRNA expression levels of STAT1, interferon-stimulated gene 12, interferon-inducible protein 9–27, and interferon-inducible 56 kD protein were examined in normal liver tissue specimens, noncancerous liver tissue specimens showing chronic hepatitis, noncancerous liver tissue specimens showing cirrhosis, and HCCs by quantitative RT-PCR. Compared with the normal liver specimens, the mRNA expression levels of STAT1 and the downstream genes were all significantly elevated in the specimens showing chronic hepatitis or cirrhosis (Fig. 3). The mRNA expression level of STAT1 was also significantly higher in HCCs without portal vein involvement than in HCCs with portal vein involvement (Fig. 3B). Similarly, the mRNA expression levels of interferon-stimulated gene 12, interferon-inducible protein 9–27 and interferon-inducible 56 kD protein were significantly higher in well-differentiated HCCs than in moderately or poorly differentiated HCCs and in HCCs without portal vein involvement than in HCCs with such involvement (Figs. 3C, 3D, and 3E).

Fig. 3A–E.

Fig. 3A–E

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Results of quantitative RT-PCR analyses of A DNMT3b4, B STAT1, C interferon-stimulated gene 12 (GenBank accession number: X67325), D interferon-inducible protein 9-27 (J04164), and E interferon-inducible 56 kD protein (M24594) in normal liver tissue specimens (N), noncancerous liver tissue specimens showing chronic hepatitis (CH), noncancerous liver tissue specimens showing cirrhosis (LC) and HCCs. (W well-differentiated HCCs, M moderately differentiated HCCs, P poorly differentiated HCCs, − portal vein involvement-negative, + portal vein involvement-positive). For eight normal liver specimens, 45 noncancerous liver specimens showing chronic hepatitis or cirrhosis, and 50 HCCs included in this study, expression levels of DNMT3b4 mRNA had already been evaluated by quantitative RT–PCR and reported in our previous study (Saito et al. 2002). In panel A, correlation between the previously reported DNMT3b4 mRNA expression levels, and clinicopathological parameters of tissue samples was reexamined for comparison with panels B–E. As we have already reported (Saito et al. 2002), DNMT3b4 expression was especially elevated in precancerous liver tissues with marked DNA hypomethylation of pericentromeric satellite regions. Therefore, the difference in the average levels of DNMT3b4 expression between all CH and LC with and without DNA hypomethylation in these regions and N did not reach a statistically significant level (A), although DNMT3b4 showed a generally similar pattern of expression alteration to that of STAT1 and other genes implicated in interferon signaling shown in panels B–E. Other examinations were performed using the GeneAmp 5700 Sequence Detection System (Applied Biosystems) as described in the Materials and Methods section. cDNAs derived from the HCC cell line Alexander or the stomach cancer cell line MKN1 were used as calibration samples. The mRNA levels for each sample are expressed as the ratio relative to GAPDH mRNA

Correlations among the mRNA expression levels of DNMT3b4, STAT1 and other genes implicated in interferon signaling in tissue specimens

The DNMT3b4 mRNA expression levels for 50 of the 56 HCCs included in the present study had already been evaluated by quantitative RT–PCR and reported in our previous paper (Saito et al. 2002). There was a significant correlation between these previously examined DNMT3b4 mRNA expression levels and the mRNA expression levels of STAT-1 measured in the same 50 HCCs during the present study (P=0.0482, Fig. 4A). Significant correlations between the mRNA expression levels of STAT1 on the one hand, and those of interferon-stimulated gene 12 (P<0.0001), interferon-inducible protein 9–27 (P=0.0059), and interferon-inducible 56 kD protein (P=0.0043) on the other, were observed among normal liver specimens, chronic hepatitis specimens, cirrhosis specimens, and HCCs (Fig. 4B).

Fig. 4A,B.

Fig. 4A,B

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A Correlation between the mRNA expression levels of DNMT3b4 and STAT1 in HCCs and B correlations between the mRNA expression levels of STAT1 and each of the downstream genes [interferon-stimulated gene 12 (GenBank accession number: X67325), interferon-inducible protein 9–27 (J04164) and interferon-inducible 56 kD protein (M24594)] in normal liver specimens, chronic hepatitis specimens, cirrhosis specimens, and HCCs. For 50 HCCs presented in panel A, DNMT3b4 mRNA expression levels had already been evaluated by quantitative RT–PCR and reported in our previous study (Saito et al. 2002). Other examinations were performed using the GeneAmp 5700 Sequence Detection System (Applied Biosystems) as described in the Materials and Methods section. cDNAs derived from the HCC cell line Alexander or the stomach cancer cell line MKN1 were used as calibration samples. The mRNA levels for each sample are expressed as the ratio relative to GAPDH mRNA

Discussion

Over recent years, we have carefully examined alterations in the DNA methylation status of the pericentromeric satellite regions and multiple CpG islands in noncancerous liver tissues showing signs of chronic hepatitis or cirrhosis and in HCCs (Kanai et al. 1996; Kanai et al. 1997; Kanai et al. 1999; Kanai et al. 2000; Kondo et al. 2000; Saito et al. 2001). DNA hypomethylation of the pericentromeric satellite regions was detected even in precancerous conditions, and appears to be one of the earliest epigenetic changes during human hepatocarcinogenesis (Saito et al. 2001). These satellite regions are located in the pericentromeric heterochromatin DNA, and their hypomethylation is known to result in centromeric decondensation and enhanced chromosomal recombination (Kokalj-Vokac et al. 1993; Jeanpierre et al. 1993). Accordingly, frequent increases in chromosome 1q copies with a pericentromeric breakpoint have been reported in HCCs showing DNA hypomethylation on satellite 2 (Wong et al. 2001). DNA hypomethylation in pericentromeric satellite regions may therefore induce chromosomal instability during hepatocarcinogenesis, even at the precancerous stage.

DNMT3b is required for the de novo DNA methylation of the pericentromeric satellite regions seen during normal development in the mouse (Okano et al. 1999). However, we detected no somatic mutations in any of the coding exons of the DNMT3b gene in HCCs (Saito et al. 2002). We therefore focused on the expression of four splice variants in the C-terminal catalytic domain of DNMT3b. DNMT3b3 retains the conserved methyltransferase motifs I, IV, VI, IX, and X, whereas DNMT3b4 lacks the conserved methyltransferase motifs IX and X (Robertson et al. 1999). We found that the major variant in normal liver tissues was DNMT3b3; only traces of DNMT3b4 were expressed in these tissues (Saito et al. 2002). We also showed that both overexpression of DNMT3b4 and elevation of the ratio of DNMT3b4 mRNA to DNMT3b3 mRNA correlated significantly with the degree of DNA hypomethylation of the pericentromeric satellite regions in precancerous conditions and HCCs (Saito et al. 2002). DNMT3b4 may thus compete with DNMT3b3 for targeting to pericentromeric satellite regions. We then introduced DNMT3b4 cDNA into human epithelial 293 cells and confirmed the induction of DNA demethylation on satellite 2 (Saito et al. 2002). We concluded that overexpression of DNMT3b4 induces DNA demethylation of the pericentromeric satellite regions even in precancerous conditions, and that it plays a critical role in the development of HCC through chromosomal instability.

In order to gain further insight into the significance of DNMT3b4 during human hepatocarcinogenesis, the characteristics of DNMT3b4 transfectants were examined more fully during the present study. We found that the growth rate of DNMT3b4 transfectants was approximately doubled compared with that of mock-transfectants soon after the introduction of DNMT3b4, when allelic imbalances may not yet have accumulated. We assumed that this change in growth was caused by alterations in the expression of genes that are regulated directly or indirectly by DNMT3b activity. Gene expression profiling was therefore examined in the DNMT3b4 transfectants. Eight of the 12 genes that were upregulated in the DNMT3b4 transfectants but not the mock-transfectants were implicated in interferon signaling. However, genes that encoded interferons themselves were not listed. We then made the further assumption that STAT1, a transcription factor that acts as an effector, may induce a set of downstream genes implicated in interferon signaling. Karpf et al. (Karpf et al. 1999) reported that the inhibition of DNA methylation in cultured human cancer cells by 5-aza-2’-deoxycytidine induced a set of genes implicated in interferon signaling primarily via the overexpression of STAT 1, 2, and 3. In the present study, we obtained similar results by inducing DNMT3b4 expression. Until recently, DNMT1 and DNMT3b were regarded as performing different functions. DNMT1 generally acts as a “maintenance” DNMT; it shows a preference for hemimethylated rather than unmethylated substrates in vitro, and targets replication foci by binding to proliferating cell nuclear antigen (Chuang et al. 1997). In contrast, it is the de novo DNA methyltransferase activity of DNMT3b during development that has so far been emphasized (Okano et al. 1999). However, some recent studies have proposed that all active DNMTs, DNMT1 and members of the DNMT3 family, probably possess both de novo and maintenance DNMT activity in vivo, regardless of their preference for hemimethylated or unmethylated substrates in vitro (Vertino et al. 1996; Rhee et al. 2002). Therefore, it is likely that DNMT3b acts to maintain the DNA methylation status of specific genes in both somatic and cancer cells. This may explain why inhibiting DNMT3b activity by inducing DNMT3b4 produced a similar result to the general inhibition of DNA methylation obtained with 5-aza-2’-deoxycytidine in cancer cells.

Although we examined DNA methylation status at multiple CpG sites in the 5’ region of the STAT1 gene by bisulfite modification followed by cloning and sequencing, this region was not methylated in either DNMT3b4 transfectants or mock-transfectants (data not shown). DNA methylation status in the 5’ region of the STAT1 gene was not examined in 5-aza-2’-deoxycytidine-treated cancer cells (Karpf et al. 1999). Moreover, the active promoter for the STAT1 gene has never been strictly identified. Therefore, further study should be done to determine whether STAT1 expression is directly regulated by DNA methylation around the promoter region. The possibility that transcription factors affecting STAT1 expression are epigenetically regulated cannot be excluded.

It is known that inappropriate activation of STAT proteins (particularly STAT3 and STAT5) occurs very frequently in a wide variety of human cancers. On the other hand, although increased expression of STAT1 has been reported to occur in various cancers, its role in promoting apoptosis has also been emphasized (Calo et al. 2003). Moreover, little is known about the functions of the downstream interferon-inducible proteins investigated during this study, although interferon-stimulated gene 12 has been reported to be expressed in human breast cancer cell lines and tissues (Rasmussen et al. 1993). Therefore, it is important to determine whether the overexpression of STAT1 and downstream interferon-inducible proteins simply results in a growth advantage for DNMT3b4 transfectants. Our current results clearly showed a significant correlation between the expression levels of DNMT3b4 and STAT1, and significant correlations between the expression levels of STAT1 and each of the interferon-inducible proteins, in human tissue samples during various stages of hepatocarcinogenesis. Expression levels of STAT1 and the downstream interferon-inducible proteins were all elevated at the chronic hepatitis and cirrhosis stages, when persistent inflammation and cell regeneration occur. In established HCCs, in which persistent inflammation is no longer present, overexpression of these proteins was highest in well-differentiated tumors, suggesting that the overexpression actually has oncogenic significance, especially during the early stage of hepatocarcinogenesis. Thus, the same alterations in gene expression as those induced in human epithelial cells by DNMT3b4 transfection occurred during human carcinogenesis. Expression of STAT1 and the downstream interferon-inducible proteins was diminished during progression to more malignant stages when proteins other than those required during the early stage may be needed for acquisition of more malignant phenotypes. Taking all our results together, we conclude that overexpression of DNMT3b4 is involved in human hepatocarcinogenesis, even at the precancerous stages, not only by inducing chromosomal instability but also by affecting the expression of specific genes.

Acknowledgements

This study was supported by a Grant-in-Aid for the Second Term Comprehensive 10-Year Strategy for Cancer Control and a Grant-in-Aid for Cancer Research from the Ministry of Health, Labour and Welfare of Japan. Y. Saito is a recipient of a Research Resident Fellowship from the Foundation for Promotion of Cancer Research in Japan.

Abbreviations

DNMT

DNA methyltransferase

HCC

Hepatocellular carcinoma

RT

Reverse transcription

PCR

Polymerase chain reaction

STAT

Signal transducer and activator of transcription

GAPDH

Glyceraldehydephosphate dehydrogenase

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