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[Cancer Research 62, 3939-3944, July 15, 2002]
© 2002 American Association for Cancer Research

Advances in Brief

Comparison of Gene Expression Profiles between Hepatitis B Virus- and Hepatitis C Virus-infected Hepatocellular Carcinoma by Oligonucleotide Microarray Data on the Basis of a Supervised Learning Method1

Norio Iizuka, Masaaki Oka2, Hisafumi Yamada-Okabe, Naohide Mori, Takao Tamesa, Toshimasa Okada, Norikazu Takemoto, Akira Tangoku, Kenji Hamada, Hironobu Nakayama, Takanobu Miyamoto, Shunji Uchimura and Yoshihiko Hamamoto

Departments of Surgery II [N. I., M. O., N. M., T. T., T. O., N. T., A. T.] and Bioregulatory Function [N. I.], Yamaguchi University School of Medicine, Yamaguchi 755-8505; Department of Computer Science and Systems Engineering, Faculty of Engineering, Yamaguchi University, Yamaguchi 755-8611 [T. M., S. U., Y. H.]; and Department of Oncology, Nippon Roche Research Center, Kanagawa 247-8530 [H. Y-O., K. H., H. N.], Japan


   ABSTRACT
Top
 ABSTRACT
Introduction
 Materials and Methods
 Results and Discussion
 REFERENCES
 
Gene expression profiles of hepatocellular carcinomas (HCCs) associated withhepatitis B virus (HBV) and hepatitis C virus (HCV) were analyzed and compared. Oligonucleotide microarrays containing >6000 genes and subsequent gene selection by a supervised learning method yielded 83 genes for which expression differed between the two types of HCCs. Expression levels of 31 of these 83 genes were increased in HBV-associated HCCs, and expression levels of the remaining 52 genes were increased in HCV-associated HCCs. The 31 genes up-regulated in HBV-associated HCC included imprinted genes (H19 and IGF2) and genes relating to signal transduction, transcription, and metastasis. The 52 genes up-regulated in HCV-associated HCC included a number of genes responsible for detoxification and immune response. These results suggest that HBV and HCV cause hepatocarcinogenesis by different mechanisms and provide novel tools for diagnosis and treatment of HBV- and HCV-associated HCCs.


   Introduction
Top
 ABSTRACT
 Introduction
Materials and Methods
 Results and Discussion
 REFERENCES
 
HCC3 is one of the most common fatal cancers worldwide (1) . The most clearly established risk factor for HCC is chronic infection with HBV or HCV (2) . More than 350 million people worldwide are known to be chronic carriers of HBV (3) . It is reported that the incidence of HCC is increasing in many countries in parallel to an increase in chronic HCV infection (1 , 2) . Therefore, clarification of the genetic portraits of hepatocarcinogenesis caused by HBV or HCV infection might provide clues toward effecting a decrease in the incidence of HCC and establishing effective treatments for each type of HCC.

Recent development of DNA microarray technology, a type of high-throughput analysis for gene expression, has opened a new era in medical sciences (4, 5, 6) . Supervised learning and unsupervised learning methods have been introduced into gene expression analysis of DNA microarray data (7 , 8) . Using hierarchical cluster analysis, an unsupervised learning method, Honda et al. (9) showed different gene expression profiles in the hepatic lesions of chronic hepatitis associated with HBV and HCV and suggested that the molecular mechanisms responsible for the pathogenesis of HCC differ between HBV and HCV infections. Several studies compared gene expression between nontumorous liver and HCC and revealed gene expression patterns that are rather specific to HCC (10, 11, 12, 13, 14) . However, there is only one study that compared gene expression patterns between HCC with HBV infection (B-type HCC) and HCC with HCV infection (C-type HCC; 14 ), and only a limited number of specimens were analyzed. Therefore, additional studies are needed to understand molecular mechanisms involved in the development and progression of virus-induced HCCs. In this study, we investigated gene expression patterns of 45 HCC samples using high-density oligonucleotide microarrays and the supervised learning method to gain additional insight into hepatocarcinogenesis or cancer progression related to HBV or HCV infection. The results of this study provide additional markers and molecular targets for the diagnosis and treatment of B- and C-type HCCs.


   Materials and Methods
Top
 ABSTRACT
 Introduction
 Materials and Methods
Results and Discussion
 REFERENCES
 
Tumor Samples.
Surgical specimens were obtained from 45 patients who underwent surgical treatment for HCC at Yamaguchi University Hospital between May 1997 and August 2000. Written informed consent was obtained from all patients before surgery. The study protocol was approved by the Institutional Review Board for Human Use at the Yamaguchi University School of Medicine. Histopathological diagnosis of HCC was made after surgery in each case. The clinicopathological characteristics of the 45 patients based on the International Union against Cancer TNM classification (15) are shown in Table 1Citation . Of the 45 patients, 14 were positive for serum HBs Ag and 31 were positive for HCV Ab; none were positive for both HBs Ag and HCV Ab. Thus, the patients were classified into two groups, those positive for HBs Ag (B-type HCC, n = 14) and those positive for HCV Ab (C-type HCC, n = 31).


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  		Table 1 Patient characteristics per study group

 
Control Liver Samples.
Six nontumorous liver samples were obtained from patients who underwent hepatic resection for benign liver tumor or metastatic liver tumor, which derive from gastrointestinal cancer. Liver function for these 6 patients was shown to be normal, and the liver was shown to histopathologically be normal. All 6 patients were seronegative for both HBs Ag and HCV Ab.

Samples and RNA Extraction.
Total RNA was extracted with Sepasol-RNAI (Nacalai Tesque, Tokyo, Japan) and purified with the RNeasy Mini kit (Qiagen, Tokyo, Japan) according to the manufacturer’s instructions. Quality of the total RNA was judged from the ratio between 28S and 18S RNase after agarose gel electrophoresis.

cDNA Synthesis and in Vitro Translation for Labeled cRNA Probe.
cDNA was synthesized with the reverse SuperScript Choice System (Invitrogen Life Technologies, Carlsbad, CA) according to the manufacturer’s instructions. cRNA was synthesized from the cDNA template by use of the MEGAscript T7 kit (Ambion, Austin, TX) according to the manufacturer’s instructions. Mononucleotides and short oligonucleotides were removed by column chromatography on a CHROMA SPIN +STE-100 column (Clontech, Palo Alto, CA).

Gene Expression Analysis by Means of High-density Oligonucleotide Arrays.
Gene expression patterns were examined by high-density oligonucleotide arrays (HuGeneFL Array; Affymetrix, Santa Clara, CA). After the cRNA was fragmented at 95°C for 35 min, hybridization was performed in 200 µl of buffer containing 0.1 M 2-(N-morpholino)ethanesulfonic acid (pH 6.7), 1 M NaCl, 0.01% Triton X-100, 20 µg of herring sperm DNA, 100 µg of acetylated BSA, 10 µg of the fragmented cRNA, and biotinylated control oligonucleotides at 45°C for 12 h. To increase hybridization signals, the washed chips were further hybridized with biotinylated antistreptavidin Ab and stained with streptavidin R-phycoerythrin (Molecular Probes, Eugene, OR) as described in the instruction manual (Affymetrix). The intensity of each pixel was detected by laser scanner (Affymetrix), and expression levels of each cDNA and reliability (Present/Absent call) were calculated with software (Affymetrix GeneChip version 3.3 and Affymetrix Microarray Suite version 4.0).

Procedure for Gene Selection.
To filter genes out of the ~6000, we first investigated all genes for which mean average differences were >2-fold between B- and C-type HCCs. Of the filtered genes, we selected D genes that had average expression levels of >20 (arbitrary units by Affymetrix) in both types of HCCs.

We used the Fisher ratio to evaluate separability between B- and C-type HCCs. The Fisher ratio for gene i is given by

where µBT(i) and {varsigma}2BT(i) are the sample mean and sample variance, respectively, of the expression levels of gene i for the samples in B-type HCC (BT). P(BT) is the priori probability of BT. As a final step, we ranked D selected genes by the Fisher ratio as F(i1) > F(i2) > ··· > F(iD).

To investigate how many genes should be considered, we performed the random permutation test according to the method by Luo et al. (16) . In the test, samples labels were randomly permuted among the two types of HCCs, and the Fisher ratio for each gene was again computed. This random permutation of sample labels was repeated 1000 times. The Fisher ratios generated from the actual data were then assigned Ps based on the distribution of the Fisher ratios from randomized data.

Reverse Transcriptase PCR Analysis for IGF-2 Gene.
To validate our microarray results and to further clarify a difference in expression pattern for IGF-2,4 we carried out semiquantitative reverse transcriptase PCR using RNA stock of 9 B-type and 12 C-type HCC samples that were subjected to microarray study. Reverse transcriptase step was performed as described previously (17) . Five µl of cDNA solution (equivalent to the cDNA from 100 ng of initial RNA) were amplified in 45 µl of PCR mixture (17) containing 25 pmol of each primer for IGF-2 and ß-actin genes. PCR was performed for 26 cycles for IGF-2 and 24 cycles for ß-actin. Each cycle consisted of denaturation at 94°C for 1 min, annealing at 60°C for 45 s, and elongation at 72°C for 2 min. The primers used in this study were as follows: IGF-2, 5'-ctggtggacaccctccagttc-3' (sense) and 5'-gcccacggggtatctggggaa-3' (antisense); and ß-actin, 5'-CCAGAGCAAGAGAGGTAT-3' (sense) and 5'-CTGTGGTGGTGAAGCTGTAG-3' (antisense). The expected sizes were 235 and 436 bp for IGF-2 and ß-actin genes, respectively. PCR products were separated by electrophoresis on 1.5% agarose gels and visualized under UV light after ethidium bromide staining. We determined the mean band densities using NIH Image 1.62 software, and we calculated levels of IGF-2 relative to ß-actin gene.

Statistical Analysis.
Clinicopathological factors pertaining to B- and C-type HCCs were compared, and differences were analyzed by {chi}2 test, Fisher’s exact test, Student’s t test, or Mann-Whitney’s U test (Table 1)Citation . P < 0.05 was accepted for statistical significance. Pearson’s correlation coefficient (r) was calculated to examine the relation between microarray and reverse transcriptase PCR results. r2 > 0.16 and P < 0.05 were considered significant. Calculations were done with Statview 5.0 (Abacus Concepts, Berkeley, CA) on a Macintosh computer (Apple Computers, Inc., Cupertino, CA).


   Results and Discussion
Top
 ABSTRACT
 Introduction
 Materials and Methods
 Results and Discussion
REFERENCES
 
Clinicopathological Characteristics Pertaining to B- and C-type HCCs.
The clinicopathological characteristics of the 14 patients with B-type HCC and the 31 patients with C-type HCC are shown in Table 1Citation . Patients with B-type HCC were significantly younger than those with C-type HCC (P < 0.0001 by Mann-Whitney t test). There were no significant differences in other factors between the two types of HCCs.

Selection of the Top 83 Genes Linked to B- or C-type HCC.
Many studies have successfully identified gene subsets (i.e., gene clusters) linked to various states of many diseases by unsupervised learning such as hierarchical clustering (5 , 7, 8, 9) . However, one cannot effectively use information on the category label of sample data by unsupervised learning (18) . Application of the supervised learning method by the Fisher ratio to the analysis of DNA microarray data makes identification of disease-related genes easier and more precise (19) . We therefore used the Fisher ratio to select appropriate genes for this study. Of an approximate 6000 genes, we first identified 169 for which expression differed between B- and C-type HCCs. We then ranked these 169 genes in the order of decreasing magnitude of the Fisher ratio. Next, we performed the random permutation test to assess the statistical significance of the Fisher ratios. From the distribution of the Fisher ratios based on the randomized data, 83 genes with the Fisher ratio > 0.4 were determined to be statistically significant (P < 0.05) in expression between B- and C-type HCCs. Therefore, we selected the top 83 genes of the 169 (Fig. 1Citation ; Table 2Citation ).



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  		Fig. 1. Gene expression profiles linked to HBV- and HCV-associated HCCs. Color displays of the expressions of (a) 31 genes up-regulated in HBV-associated HCC and (b) 52 genes up-regulated in HCV-associated HCC. Each gene was ranked in decreasing order of the Fisher ratio (see the "Materials and Methods") and was listed as an accession number. Accession numbers of each gene were obtained from PubMed or the Institute for Genomic Research databases.5 ,6 Normal liver samples obtained from nontumorous livers.

 

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  		Table 2 The 83 genes for which expression levels differed between HBV- and HCV-associated HCCs

 
Molecular Feature of B- and C-type HCCs.
Among the top 83 genes selected, 31 were up-regulated in B-type HCC in comparison to C-type HCC. These 31 genes included imprinted genes (H19 and IGF2), genes linked to signal transduction or transcription (MAP2K4, MAP2K5, SF1, SIAHBP1, and MYOG), and metastasis-related genes (i.e., MMP9 and VEGF). Fifty-two genes were up-regulated in C-type HCC in comparison to B-type HCC and these included detoxification-related genes (i.e., MT1E, MT1H, ADH1B, ADH4, CYP2A7, and CYPIIE) and many immune response-related genes (Fig. 1Citation ; Table 2Citation ).

Genes with the Larger Fisher Ratio.
The Fisher ratio measures the difference between two means normalized by the average variance. Thus, the Fisher ratio represents the ability of a gene to discriminate the two types of HCCs. Among the top 83 genes, ACP yielded the largest Fisher ratio, and RPL39L and TACSTD1 yielded the second and third largest Fisher ratio, respectively (Table 2)Citation . All 3 genes were up-regulated in B-type HCC in comparison to C-type HCC. ACP is known to play a role in the differentiation of normal human monocytes to macrophages (20) but its role in the development of HCC remains unclear.Using microarray, Xu et al. found that many ribosome-related genes such as RPL family genes were up-regulated in HCC, suggesting the activation of protein translation in HCC (11) .TACSTD1, which was identified as a gastrointestinal cancer Ag, plays an important role in cellular adhesion and its overexpression has been reported in certain other types of cancers (21) . Consistent with these reports, RPL39L and TACSTD1 mRNA levels were also higher in our B-type HCC than in our nontumorous liver tissue. Thus, it seems that the 3 genes are potential molecular targets for the treatment of B-type HCC rather than C-type HCC.

Imprinted Genes.
We found that imprinted genes H19 and IGF2, which are located close together on chromosome 11p15.5, were up-regulated in B-type HCC in comparison to both C-type HCC and nontumorous liver. There was a positive correlation in gene expression levels between H19 and IGF2 (r = 0.517, P < 0.0001; data not shown). Although these genes are reported to be coordinately up-regulated in HCC (22) , this is the first study to show up-regulation of these genes specifically in B-type HCC. H19 is an untranslated gene, and the biological function remains unclear. IGF2 is known to be an autocrine growth factor in many malignant tumors (22) . Expression levels of the IGF2 mRNA were further confirmed by our semiquantitative reverse transcriptase PCR; the result of the DNA microarray was reproduced even by reverse transcriptase PCR (P = 0.0075, r = 0.558; Fig. 2a and bCitation ). This result suggests for the first time that up-regulation of the IGF-2 pathway may play an important role in the pathogenesis of B-type HCC but not C-type HCC.



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  		Fig. 2. Validation of expression pattern of IGF2. a, representative reverse transcriptase PCR result of IGF2. Lanes 1–4 are samples obtained from HBV-associated HCC (HBV61T, HBV48T, HBV30T, and HBV14T, respectively), and Lanes 5–8 are samples obtained from HCV-associated HCC (HCV21T, HCV29T, HCV20T, and HCV45T, respectively). b, validation of microarray data for IGF2 by semiquantitative reverse transcriptase PCR. The PCR products for IGF2 were semiquantitatively analyzed with the use of NIH Image 1.62 and calculated as levels relative to ß-actin. The reverse transcriptase PCR data correlated with the microarray data (P = 0.0075 and r = 0.558).

 
Detoxification-related Genes.
The expression levels of many detoxification-related genes were increased in C-type HCC in comparison to B-type HCC, although nontumorous liver contained higher levels of the mRNA for these genes. The result for CYPIIE was quite consistent with that obtained by Okabe et al. (14) . ADH and CYP family genes are also reported to be down-regulated in HCCs in comparison to nontumorous liver tissue (12, 13, 14) . Thus, it is likely that blockade of the detoxification system is a common pathway during carcinogenesis and/or progression of B-type and C-type HCCs. Moreover, markedly reduced levels of detoxification-related genes in B-type HCC suggests that HBV-infected liver could be more susceptible than HCV-infected liver to various xenobiotics or carcinogens.

Among detoxification-related genes, only GSTP1 was exceptionally up-regulated in B-type HCC, although its mRNA level was higher in both types of HCCs than in nontumorous liver. Experiments for hepatocarcinogenesis and a recent microarray study showed up-regulation of GST in HCC (23 , 24) . Interestingly, GST expression has been shown to be induced in a HCC cell line overexpressing HBX protein (25) . Because GST is involved in scavenging reactive oxygen intermediates that are generated by many anticancer agents, our data suggest the efficacy of these anticancer agents in treating C-type HCC and their limitations in treating B-type HCC.

Immune Response-related Genes.
The 52 genes up-regulated in C-type HCC included a number of immune response-related genes. The result that C15, IFI27, C6, and OAS1 had the larger Fisher ratios implies that C-type HCC is closely related to immune response, especially inflammation. In keeping with a previous study (14) , we found up-regulation of natural killer receptor in C-type HCC versus B-type HCC. IFN-inducible genes (IFI27, OAS1, ISG15, and IFIT4) were also up-regulated in C-type HCC by >2- and 1.5-fold versus B-type HCC and nontumorous liver, respectively. Whereas Honda et al. demonstrated by cDNA microarray that IFN-a was commonly up-regulated in livers with chronic HBV or HCV infection (9) , the expression levels of IFNs were more or less the same between the two types of HCCs in this study (data not shown). Because IFN is induced by double-strand RNA species, it is reasonable that up-regulation of these IFN-inducible genes is the consequence of the generation of the double-strand RNA by infection with HCV. The mechanism of IFN-{alpha} induction in B-type HCC, however, remains to be elucidated.

The time lag between HCV infection and cancer development is several decades. As a result, HCV-associated tumors arise in older patients and are almost always associated with cirrhosis. Thus, it is apparent that C-type HCC is closely related to chronic inflammation (26) , suggesting that the immune response-related genes identified here serve as molecular targets for chemoprevention and treatment of C-type HCC.

The Other Genes.
Wu et al. showed many signal transduction-related genes, including MAPK family genes to be up-regulated in B-type HCC (13) . Up-regulation of MAPK is also suggested as a common pathway for the hepatocarcinogenesis caused by infection with HBV and HCV (14) . In our study, MAP2K4 and MAP2K5 were up-regulated in B-type HCC versus C-type HCC; however, MAP2K5 was down-regulated in both types of HCC versus nontumorous liver. Thus, additional studies are necessary to clarify contribution of the MAPK pathway to each type of HCC.

Xu et al. reported up-regulation of liver-enriched transcription factors in HCC versus nontumorous liver (11) . We found in this study that transcription factors PPARG, SIAHBP1, and MYOG were up-regulated in B-type HCC but not in C-type HCC. These transcription factors seem to be abundant in organs other than the liver. We selected genes by focusing on differences between B- and C-type HCCs. The discrepancy, therefore, might be due partly to a difference in the gene selection method. However, our method could identify additional genes that had not been associated with these two types of HCCs thus far. Genes such as MMP9, VEGF, HMMR, TACSTD1, and MCAM that may promote metastasis, for example, were up-regulated in B-type HCC. Overall, we provide evidence that B- and C-type HCCs use different mechanisms for the promotion and suppression of metastasis. We expect the results obtained in this study to aid in understanding the molecular mechanism underlying the pathogenesis of B-type and C-type HCCs.


   ACKNOWLEDGMENTS
 
We thank Frances Ford for reading the manuscript.


   FOOTNOTES
 
The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1 Supported in part by a Grant-in-Aid from the Ministry of Education, Science, Sports and Culture of Japan (12671230). Back

2 To whom requests for reprints should be addressed, at Department of Surgery II, Yamaguchi University School of Medicine, 1-1-1 Minami-Kogushi, Ube, Yamaguchi 755-8505, Japan. Phone: 81-836-22-2262; Fax: 81-836-22-2262; E-mail: 2geka-1{at}po.cc.yamaguchi-u.ac.jp Back

3 The abbreviations used are: HCC, hepatocellular carcinoma; HB, hepatitis B; HBV, hepatitis B virus; HCV, hepatitis C virus; Ag, antigen; Ab, antibody; GST, glutathione S-transferase; MAPK, mitogen-activated protein kinase. Back

4 Gene abbreviations are used based on LocusLink at internet address: www.ncbi.nlm.nih.gov/LocusLink/. Back

5 Internet address: www3.ncbi.nlm.nih.gov/PubMed/. Back

6 Internet address: www.tigr.org/tdb/hgi/searching/reports.html. Back

Received 3/18/02. Accepted 5/23/02.


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 Introduction
 Materials and Methods
 Results and Discussion
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