Comparison of Gene Expression Profiles

[CANCER RESEARCH 62, 3939–3944, July 15, 2002] 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 Oka,2 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 nisms responsible for the pathogenesis of HCC differ between HBV and HCV infections. Several studies compared gene expression be- Gene expression profiles of hepatocellular carcinomas (HCCs) associ- tween nontumorous liver and HCC and revealed gene expression ated with hepatitis B virus (HBV) and hepatitis C virus (HCV) were patterns that are rather specific to HCC (10–14). However, there is analyzed and compared. Oligonucleotide microarrays containing >6000 genes and subsequent gene selection by a supervised learning method only one study that compared gene expression patterns between HCC yielded 83 genes for which expression differed between the two types of with HBV infection (B-type HCC) and HCC with HCV infection HCCs. Expression levels of 31 of these 83 genes were increased in HBV- (C-type HCC; 14), and only a limited number of specimens were associated HCCs, and expression levels of the remaining 52 genes were analyzed. Therefore, additional studies are needed to understand mo- increased in HCV-associated HCCs. The 31 genes up-regulated in HBV- lecular mechanisms involved in the development and progression of associated HCC included imprinted genes (H19 and IGF2) and genes virus-induced HCCs. In this study, we investigated gene expression relating to signal transduction, transcription, and metastasis. The 52 genes patterns of 45 HCC samples using high-density oligonucleotide mi- up-regulated in HCV-associated HCC included a number of genes respon- croarrays and the supervised learning method to gain additional in- sible for detoxification and immune response. These results suggest that sight into hepatocarcinogenesis or cancer progression related to HBV HBV and HCV cause hepatocarcinogenesis by different mechanisms and provide novel tools for diagnosis and treatment of HBV- and HCV- or HCV infection. The results of this study provide additional markers associated HCCs. and molecular targets for the diagnosis and treatment of B- and C-type HCCs. Introduction Materials and Methods HCC3 is one of the most common fatal cancers worldwide (1). The most clearly established risk factor for HCC is chronic infection with Tumor Samples. Surgical specimens were obtained from 45 patients who HBV or HCV (2). More than 350 million people worldwide are underwent surgical treatment for HCC at Yamaguchi University Hospital known to be chronic carriers of HBV (3). It is reported that the between May 1997 and August 2000. Written informed consent was obtained incidence of HCC is increasing in many countries in parallel to an from all patients before surgery. The study protocol was approved by the increase in chronic HCV infection (1, 2). Therefore, clarification of Institutional Review Board for Human Use at the Yamaguchi University the genetic portraits of hepatocarcinogenesis caused by HBV or HCV School of Medicine. Histopathological diagnosis of HCC was made after infection might provide clues toward effecting a decrease in the surgery in each case. The clinicopathological characteristics of the 45 patients incidence of HCC and establishing effective treatments for each type based on the International Union against Cancer TNM classification (15) are shown in Table 1. Of the 45 patients, 14 were positive for serum HBs Ag and of HCC. 31 were positive for HCV Ab; none were positive for both HBs Ag and HCV Recent development of DNA microarray technology, a type of Ab. Thus, the patients were classified into two groups, those positive for HBs high-throughput analysis for gene expression, has opened a new era in Ag (B-type HCC, n ϭ 14) and those positive for HCV Ab (C-type HCC, medical sciences (4–6). Supervised learning and unsupervised learn- n ϭ 31). ing methods have been introduced into gene expression analysis of Control Liver Samples. Six nontumorous liver samples were obtained DNA microarray data (7, 8). Using hierarchical cluster analysis, an from patients who underwent hepatic resection for benign liver tumor or unsupervised learning method, Honda et al. (9) showed different gene metastatic liver tumor, which derive from gastrointestinal cancer. Liver func- expression profiles in the hepatic lesions of chronic hepatitis associ- tion for these 6 patients was shown to be normal, and the liver was shown to ated with HBV and HCV and suggested that the molecular mecha- 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- Received 3/18/02; accepted 5/23/02. The costs of publication of this article were defrayed in part by the payment of page RNAI (Nacalai Tesque, Tokyo, Japan) and purified with the RNeasy Mini kit charges. This article must therefore be hereby marked advertisement in accordance with (Qiagen, Tokyo, Japan) according to the manufacturer’s instructions. Quality 18 U.S.C. Section 1734 solely to indicate this fact. of the total RNA was judged from the ratio between 28S and 18S RNase after 1 Supported in part by a Grant-in-Aid from the Ministry of Education, Science, Sports agarose gel electrophoresis. and Culture of Japan (12671230). 2 To whom requests for reprints should be addressed, at Department of Surgery II, cDNA Synthesis and in Vitro Translation for Labeled cRNA Probe. Yamaguchi University School of Medicine, 1-1-1 Minami-Kogushi, Ube, Yamaguchi cDNA was synthesized with the reverse SuperScript Choice System (Invitro- 755-8505, Japan. Phone: 81-836-22-2262; Fax: 81-836-22-2262; E-mail: 2geka-1@po. gen Life Technologies, Carlsbad, CA) according to the manufacturer’s instruc- cc.yamaguchi-u.ac.jp. tions. cRNA was synthesized from the cDNA template by use of the MEGAs- 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 cript T7 kit (Ambion, Austin, TX) according to the manufacturer’s S-transferase; MAPK, mitogen-activated protein kinase. instructions. Mononucleotides and short oligonucleotides were removed by 3939

Table 1 Patient characteristics per study group stock of 9 B-type and 12 C-type HCC samples that were subjected to microar- BT group (n ϭ 14)a CT group (n ϭ 31) P ray study. Reverse transcriptase step was performed as described previously (17). Five ␮l of cDNA solution (equivalent to the cDNA from 100 ng of initial Sex N.S. ␮ Male/Female 8/6 22/9 RNA) were amplified in 45 l of PCR mixture (17) containing 25 pmol of each Age (yrs) 51.5 Ϯ 3.0 66.0 Ϯ 1.1 0.0001b primer for IGF-2 and ␤-actin genes. PCR was performed for 26 cycles for Primary lesion N.S. IGF-2 and 24 cycles for ␤-actin. Each cycle consisted of denaturation at 94°C Single tumor 6 12 for 1 min, annealing at 60°C for 45 s, and elongation at 72°C for 2 min. The Multiple tumors 8 19 Tumor size (cm) 5.9 Ϯ 1.4 5.5 Ϯ 0.8 N.S. primers used in this study were as follows: IGF-2,5Ј-ctggtggacaccctccagttc-3Ј Stagec N.S. (sense) and 5Ј-gcccacggggtatctggggaa-3Ј (antisense); and ␤-actin,5Ј-CCA- I/II 5 12 GAGCAAGAGAGGTAT-3Ј (sense) and 5Ј-CTGTGGTGGTGAAGCTG- IIIA/IVA/IVB 9 19 Ј Histological gradec N.S. TAG-3 (antisense). The expected sizes were 235 and 436 bp for IGF-2 and G1 0 2 ␤-actin genes, respectively. PCR products were separated by electrophoresis G2 9 25 on 1.5% agarose gels and visualized under UV light after ethidium bromide G3 5 4 c staining. We determined the mean band densities using NIH Image 1.62 Venous invasion N.S. ␤ (Ϫ)722software, and we calculated levels of IGF-2 relative to -actin gene. (ϩ)79Statistical Analysis. Clinicopathological factors pertaining to B- and C- Nontumorous liver N.S. type HCCs were compared, and differences were analyzed by ␹2 test, Fisher’s Nonspecific change 2 1 exact test, Student’s t test, or Mann-Whitney’s U test (Table 1). P Ͻ 0.05 was Chronic hepatitis 4 13 Liver cirrhosis 8 17 accepted for statistical significance. Pearson’s correlation coefficient (r) was calculated to examine the relation between microarray and reverse tran- a BT group, patients with HBV-associated HCC; CT group, patients with HCV- 2 Ͼ Ͻ associated HCC; NS, not significant. scriptase PCR results. r 0.16 and P 0.05 were considered significant. b P by Mann-Whitney U test. Calculations were done with Statview 5.0 (Abacus Concepts, Berkeley, CA) on c Assessment based on TNM classification by the International Union against cancer. a Macintosh computer (Apple Computers, Inc., Cupertino, CA).

Results and Discussion column chromatography on a CHROMA SPIN ϩSTE-100 column (Clontech, Palo Alto, CA). Clinicopathological Characteristics Pertaining to B- and C-type Gene Expression Analysis by Means of High-density Oligonucleotide HCCs. The clinicopathological characteristics of the 14 patients with Arrays. Gene expression patterns were examined by high-density oligonu- B-type HCC and the 31 patients with C-type HCC are shown in Table cleotide arrays (HuGeneFL Array; Affymetrix, Santa Clara, CA). After the 1. Patients with B-type HCC were significantly younger than those cRNA was fragmented at 95°C for 35 min, hybridization was performed in 200 with C-type HCC (P Ͻ 0.0001 by Mann-Whitney t test). There were ␮ l of buffer containing 0.1 M 2-(N-morpholino)ethanesulfonic acid (pH 6.7), 1 no significant differences in other factors between the two types of M NaCl, 0.01% Triton X-100, 20 ␮g of herring sperm DNA, 100 ␮gof HCCs. acetylated BSA, 10 ␮g of the fragmented cRNA, and biotinylated control oligonucleotides at 45°C for 12 h. To increase hybridization signals, the Selection of the Top 83 Genes Linked to B- or C-type HCC. washed chips were further hybridized with biotinylated antistreptavidin Ab and Many studies have successfully identified gene subsets (i.e., gene stained with streptavidin R-phycoerythrin (Molecular Probes, Eugene, OR) as clusters) linked to various states of many diseases by unsupervised described in the instruction manual (Affymetrix). The intensity of each pixel learning such as hierarchical clustering (5, 7–9). However, one cannot was detected by laser scanner (Affymetrix), and expression levels of each effectively use information on the category label of sample data by cDNA and reliability (Present/Absent call) were calculated with software unsupervised learning (18). Application of the supervised learning (Affymetrix GeneChip version 3.3 and Affymetrix Microarray Suite version 4.0). method by the Fisher ratio to the analysis of DNA microarray data ϳ Procedure for Gene Selection. To filter genes out of the 6000, we first makes identification of disease-related genes easier and more precise investigated all genes for which mean average differences were Ͼ2-fold (19). We therefore used the Fisher ratio to select appropriate genes for between B- and C-type HCCs. Of the filtered genes, we selected D genes that had average expression levels of Ͼ20 (arbitrary units by Affymetrix) in both this study. Of an approximate 6000 genes, we first identified 169 for types of HCCs. which expression differed between B- and C-type HCCs. We then We used the Fisher ratio to evaluate separability between B- and C-type ranked these 169 genes in the order of decreasing magnitude of the HCCs. The Fisher ratio for gene i is given by Fisher ratio. Next, we performed the random permutation test to assess the statistical significance of the Fisher ratios. From the distri- 2 ͑␮BT͑i͒ Ϫ ␮CT͑i͒͒ F͑i͒ ϭ bution of the Fisher ratios based on the randomized data, 83 genes ͑ ͒␴2 ͑ ͒ ϩ ͑ ͒␴2 ͑ ͒ P BT BT i P CT CT i with the Fisher ratio Ͼ 0.4 were determined to be statistically signif- Ͻ ␮ ␴2 icant (P 0.05) in expression between B- and C-type HCCs. There- where BT(i) and BT(i) are the sample mean and sample variance, respectively, of the expression levels of gene i for the samples in B-type HCC (BT). fore, we selected the top 83 genes of the 169 (Fig. 1; Table 2). P(BT)isthepriori probability of BT. As a final step, we ranked D selected Molecular Feature of B- and C-type HCCs. Among the top 83 Ͼ Ͼ ⅐⅐⅐ Ͼ genes selected, 31 were up-regulated in B-type HCC in comparison to genes by the Fisher ratio as F(i1) F(i2) F(iD). To investigate how many genes should be considered, we performed the C-type HCC. These 31 genes included imprinted genes (H19 and random permutation test according to the method by Luo et al. (16). In the test, IGF2), genes linked to signal transduction or transcription (MAP2K4, samples labels were randomly permuted among the two types of HCCs, and the MAP2K5, SF1, SIAHBP1, and MYOG), and metastasis-related genes Fisher ratio for each gene was again computed. This random permutation of (i.e., MMP9 and VEGF). Fifty-two genes were up-regulated in C-type sample labels was repeated 1000 times. The Fisher ratios generated from the HCC in comparison to B-type HCC and these included detoxification- actual data were then assigned Ps based on the distribution of the Fisher ratios related genes (i.e., MT1E, MT1H, ADH1B, ADH4, CYP2A7, and from randomized data. CYPIIE) and many immune response-related genes (Fig. 1; Table 2). Reverse Transcriptase PCR Analysis for IGF-2 Gene. To validate our microarray results and to further clarify a difference in expression pattern for Genes with the Larger Fisher Ratio. The Fisher ratio measures IGF-2,4 we carried out semiquantitative reverse transcriptase PCR using RNA the difference between two means normalized by the average variance. Thus, the Fisher ratio represents the ability of a gene to dis- 4 Gene abbreviations are used based on LocusLink at internet address: www.ncbi.nlm. criminate the two types of HCCs. Among the top 83 genes, ACP nih.gov/LocusLink/. yielded the largest Fisher ratio, and RPL39L and TACSTD1 yielded 3940

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 “Ma- terials 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.

the second and third largest Fisher ratio, respectively (Table 2). All 3 an important role in cellular adhesion and its overexpression has been genes were up-regulated in B-type HCC in comparison to C-type reported in certain other types of cancers (21). Consistent with these HCC. ACP is known to play a role in the differentiation of normal reports, RPL39L and TACSTD1 mRNA levels were also higher in our human monocytes to macrophages (20) but its role in the development B-type HCC than in our nontumorous liver tissue. Thus, it seems that of HCC remains unclear. Using microarray, Xu et al. found that many the 3 genes are potential molecular targets for the treatment of B-type ribosome-related genes such as RPL family genes were up-regulated HCC rather than C-type HCC. in HCC, suggesting the activation of protein translation in HCC (11). Imprinted Genes. We found that imprinted genes H19 and IGF2, TACSTD1, which was identified as a gastrointestinal cancer Ag, plays which are located close together on chromosome 11p15.5, were up-regulated in B-type HCC in comparison to both C-type HCC and 5 Internet address: www3.ncbi.nlm.nih.gov/PubMed/. nontumorous liver. There was a positive correlation in gene expres- 6 Internet address: www.tigr.org/tdb/hgi/searching/reports.html. sion levels between H19 and IGF2 (r ϭ 0.517, P Ͻ 0.0001; data not 3941

Table 2 The 83 genes for which expression levels differed between HBV- and HCV-associated HCCs Fisher Accession ratio BT/Na CT/N Name no.b Abbreviationc Locus Function Thirty-one genes up-regulated in HBV-associated HCC 1.962 8.943 1.745 Tartrate-resistant acid phosphatase type 5 J04430 ACP5 19p13.3-p13.2 Metabolism/differentiation of macrophage 1.883 9.596 3.629 Ribosomal protein L39-like 1 HG2874-HT3018 RPL39L 3q27 Ribosomal protein 1.800 4.444 1.225 Carcinoma-associated antigen GA733-2 M93036 TACSTD1 4q Cell adhesion 1.677 3.087 1.315 Histone H1(0) X03473 H1F0 22q13.1 Basic nuclear protein 1.117 7.357 2.087 H19 RNA M32053 H19 11p15.5 Unknown 1.055 2.980 1.149 Splicing factor (SF1-Bo isoform) Y08766 SF1 11q13 Signal transduction 1.050 3.168 1.505 Serine/threonine kinase stk2 L20321 STK2 3p21.1 Cell cycle regulation 1.049 3.421 1.269 KIAA0159 D63880 CNAP1 12p13.3 Unknown 0.874 3.529 1.123 MAP kinase kinase 4 (MKK4) L36870 MAP2K4 17p11.2 Signal transduction 0.817 6.329 2.506 Ubiquitin-conjugating enzyme U45328 UBE21 16p13.3 Proteolysis and peptidolysis 0.789 0.706 0.294 MAP kinase kinase 5 U25265 MAP2K5 15q22.2-q22.31 Signal transduction 0.783 1.401 0.680 Hyaluronan receptor U29343 HMMR 5q33.2-qter Cell motility 0.776 6.781 2.497 Glutathione S-transferase ␲ M24485 GSTP1 11q13 Detoxification and drug metabolism 0.775 2.181 0.803 KIAA0184 D80006 KIAA0184 21q22.3 Unknown 0.774 0.828 0.406 Peroxisome proliferator-activated receptor ␥ L40904 PPARG 3p25 Transcription 0.764 6.721 1.692 Macrophage-capping protein M94345 CAPG 2cen-q24 Cytoskeleton 0.691 1.082 0.366 c-Myc promoter-binding protein X63417 IRLB 15q22.1 DNA binding protein 0.675 3.792 1.482 Asparagine synthetase M27396 ASNS 7q21-q21 Metabolism/cell cycle regulation 0.671 4.943 2.105 HSU35835 Human DNA-PK mRNA U35835 Unknown Unknown 0.649 5.396 2.335 HLA class II region expressed gene KE4 D82060 HKE4 6p21.3 Unknown 0.616 12.155 5.797 H. sapiens H4/g gene for H4 histone X60486 H4FG 6p21.3 Basic nuclear protein 0.567 4.135 1.529 MUC18 glycoprotein M29277 MCAM 11q23.3 Cell adhesion 0.548 3.228 1.552 Heat shock 70kD protein 1B M59830 HSPA1B 6p21.3 Stress response 0.530 6.358 2.943 Type IV collagenase J05070 MMP9 20q11.2-q13.1 Invasion and metastasis 0.514 1.114 0.515 Osteomodulin AB000114 OMD 9q22.1 Leucine-rich proteoglycan 0.513 1.539 0.571 Vascular endothelial growth factor M27281 VEGF 6p12 Angiogenesis and metastasis 0.510 10.368 4.673 Siah-binding protein 1 U51586 SIAHBP1 8q24.2-qter Transcription 0.493 1.731 0.658 Insulin-like growth factor 2 HG3543-HT3739 IGF2 11p15.5 Growth factor 0.459 4.275 1.547 Myogenic factor 4 X17651 MYOG 1q31-q41 Transcription 0.425 1.418 0.569 Immunoglobulin ␮ V00563 Unknown Immune response 0.415 2.534 1.154 MAP kinase phosphatase 4 Y08302 DUSP9 Xq28 Signal transduction Fifty-two genes up-regulated in HCV-associated HCCs 1.426 0.281 0.590 Complement component Clr J04080 C1S 12p13 Immune response 1.082 0.157 6.760 p27 X67325 IFI27 14q32 Immune response/interferon-inducible 0.970 0.040 0.293 Tyrosine aminotransferase X52520 TAT 16q22.1 Metabolism/mitochondrial protein 0.968 0.131 0.316 Zn-␣2-glycoprotein X59766 AZGP1 7q22.1 Plasma glycoprotein/cachectic factor 0.918 0.221 0.461 Human clone 23815 mRNA U90916 Unknown Unknown 0.911 0.154 0.315 Complement component C6 X72177 C6 5p13 Immune response 0.891 0.561 1.219 (2Ј-5Ј) oligo A synthetase E X02874 OAS1 12q24.1 Metabolism/interferon-inducible protein 0.858 0.091 0.298 3,4-catechol estrogen UDP-glucuronosyltransferase J05428 UGT2B7 4q13 Detoxification and drug metabolism 0.856 0.549 1.519 NK receptor X99479 Unknown Immune response 0.835 0.529 1.158 Putative carboxylesterase Y09616 CES2 16q22.1 Detoxification and drug metabolism 0.830 0.123 0.799 Apolipoprotein apoC-IV U32576 APOC4 19q13.2 Lipid metabolism 0.797 0.145 0.467 Tryptophan oxygenase U32989 TDO2 4q31-q32 Metabolism 0.795 0.188 0.428 Plasma kallikrein-sensitive glycoprotein D38535 ITIH4 3p21-p14 Proteogycan 0.789 0.155 0.419 Paraoxonase 3 L48516 PON3 7q21.3 Metabolism 0.787 1.230 2.536 Smooth muscle LIM protein (h-SmLIM) U46006 CSRP2 12q21.1 Cell growth and differentiation 0.737 0.482 1.748 Interferon-induced 17-kDa/15-kDa protein M13755 ISG15 1p36.33 Immune response/interferon-inducible 0.719 0.085 0.277 Glycogen synthase 2 S70004 GYS2 12p12.2 Metabolism 0.699 0.060 0.231 Aldo-keto reductase family 1 Z28339 AKR1D1 7q32-q33 Detoxification and drug metabolism 0.698 0.180 0.408 4-aminobutyrate aminotransferase L32961 ABAT 16p13.3 Metabolism 0.676 0.111 0.358 Serum amyloid A4 S48983 SAA4 11p15.1-p14 Inflammation 0.660 0.096 0.264 Alcohol dehydrogenase 4 (class II) X56411 ADH4 4q21-q24 Detoxification and drug metabolism 0.660 0.034 0.261 Metallothionein from cadmium-treated cells V00594 Unknown Detoxification and drug metabolism 0.654 0.181 0.382 Complement 8 ␣ subunit U08006 C8A 1p32 Immune response 0.651 0.186 0.381 Coagulation factor XI M20218 F11 4q35 Blood coagulation cascade 0.638 0.926 2.399 Polymeric immunoglobulin receptor X73079 PIGR 1q31-q41 Immune response 0.631 0.329 0.895 Human follistatin gene M19481 FST 5q11.2 Developmental processes 0.625 0.175 0.360 Human hemopexin gene M36803 HPX 11p15.5-p15.4 Heme transport 0.623 0.213 0.697 Hydroxysteroid (11-␤) dehydrogenase 1 M76665 HSD11B1 1q32-g41 Metabolism 0.608 1.257 2.718 RIG-G U52513 IFIT4 10q24 Immune response/interferon-inducible 0.607 0.062 0.326 Nicotinamide N-methyltransferase U08021 NNMT 11q23.1 Detoxification and drug metabolism 0.596 0.929 2.027 Dipeptidyl peptidase IV X60708 DPP4 2q24.3 Immune response (CD26)/glucose homeostasis 0.595 0.133 0.440 Cytochrome P450IIE1 (ethanol-inducible) J02843 CYP2E 10q24-qter Detoxification and drug metabolism 0.591 0.038 0.238 Nicotinamide N-methyltransferase U51010 NNMT 11q23.1 Detoxification and drug metabolism 0.582 0.055 0.200 H. sapiens mRNA for metallothionein X64177 MT1H 16q13 Detoxification and drug metabolism 0.568 0.102 0.219 Human metallothionein-le gene (hMT-le) M10942 MT1E 16q13 Detoxification and drug metabolism 0.560 0.492 1.264 Thyroxine-binding globulin M14091 SERPINA7 Xq22.2 Serine (or cysteine) proteinase inhibitor 0.521 0.146 0.555 Cystathionine ␥-lyase S52028 CTH 16 Metabolism/removal of ammonia 0.509 0.257 0.582 Pre-B cell enhancing factor U02020 PBEF 7q22.1 Immune response 0.506 0.733 1.469 KIAA0216 D86970 TIAF1 17q11.1 TGF-beta-induced anti-apoptotic factor 1 0.503 0.018 1.401 Serum amyloid A2 J03474 SAA2 11p15.1-p14 Inflammation 0.491 0.500 2.833 GP-39 cartilage protein Y08374 CHI3L1 1q32.1 Extracellular matrix 0.478 0.131 0.364 Complement factor H-related protein 4 X98337 FHR-4 1q32 Immune response 0.474 0.032 0.336 Serine dehydratase J05037 SDS 12q24.21 Metabolism 0.468 0.143 1.760 Serum amyloid A protein X51441 SAA 11p15.1-p14 Inflammation 0.458 0.268 0.591 CD14 antigen X13334 CD14 5q31.1 Immune response 0.450 0.882 1.837 CTP synthetase X52142 CTPS 1p34.1 Metabolism of phospholipids and nucleic acids 0.447 0.041 0.137 Cytochrome P450, subfamily IIA, polypeptide 7 M33317 CYP2A7 19q13.2 Detoxification and drug metabolism 0.440 1.029 2.978 Macrophage lectin 2 D50532 HML2 17p13.2 Immune response 0.423 0.513 1.219 Neuronal nitric oxide synthase U17327 NOS1 12q24.2-q24.3 Biosynthesis of nitric oxide/miscellaneous 0.420 1.159 2.565 Transmembrane 4 superfamily member 3 M35252 TM4SF3 12q14.1-q21.1 Tumor-associated antigen 0.410 0.169 0.351 Alcohol dehydrogenase 1B, ␤ subunit X03350 ADH1B 4q21-q23 Detoxification and drug metabolism 0.404 0.193 0.410 Angiogenin M11567 ANG 14q11.1-q11.2 Angiogenesis and metastasis a BT/N, fold change of HBV-associated HCC versus nontumorous liver; CT/N, fold change of HCV-associated HCC versus nontumorous liver. b Accession number of each gene was obtained from PubMed or the Institute for Genomic Research database. c Gene symbols used are based on the data from LocusLink.

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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 Ͼ2- 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-␣ 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 Fig. 2. Validation of expression pattern of IGF2. a, representative reverse transcriptase here serve as molecular targets for chemoprevention and treatment of 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 C-type HCC. obtained from HCV-associated HCC (HCV21T, HCV29T, HCV20T, and HCV45T, The Other Genes. Wu et al. showed many signal transduction- respectively). b, validation of microarray data for IGF2 by semiquantitative reverse related genes, including MAPK family genes to be up-regulated in transcriptase PCR. The PCR products for IGF2 were semiquantitatively analyzed with the use of NIH Image 1.62 and calculated as levels relative to ␤-actin. The reverse tran- B-type HCC (13). Up-regulation of MAPK is also suggested as a scriptase PCR data correlated with the microarray data (P ϭ 0.0075 and r ϭ 0.558). 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 shown). Although these genes are reported to be coordinately up- was down-regulated in both types of HCC versus nontumorous liver. regulated in HCC (22), this is the first study to show up-regulation of Thus, additional studies are necessary to clarify contribution of the these genes specifically in B-type HCC. H19 is an untranslated gene, MAPK pathway to each type of HCC. and the biological function remains unclear. IGF2 is known to be an Xu et al. reported up-regulation of liver-enriched transcription autocrine growth factor in many malignant tumors (22). Expression factors in HCC versus nontumorous liver (11). We found in this study levels of the IGF2 mRNA were further confirmed by our semiquan- that transcription factors PPARG, SIAHBP1, and MYOG were up- titative reverse transcriptase PCR; the result of the DNA microarray regulated in B-type HCC but not in C-type HCC. These transcription was reproduced even by reverse transcriptase PCR (P ϭ 0.0075, factors seem to be abundant in organs other than the liver. We selected r ϭ 0.558; Fig. 2a and b). This result suggests for the first time that genes by focusing on differences between B- and C-type HCCs. The up-regulation of the IGF-2 pathway may play an important role in the discrepancy, therefore, might be due partly to a difference in the gene pathogenesis of B-type HCC but not C-type HCC. selection method. However, our method could identify additional Detoxification-related Genes. The expression levels of many de- genes that had not been associated with these two types of HCCs thus toxification-related genes were increased in C-type HCC in compar- far. Genes such as MMP9, VEGF, HMMR, TACSTD1, and MCAM ison to B-type HCC, although nontumorous liver contained higher that may promote metastasis, for example, were up-regulated in levels of the mRNA for these genes. The result for CYPIIE was quite B-type HCC. Overall, we provide evidence that B- and C-type HCCs consistent with that obtained by Okabe et al. (14). ADH and CYP use different mechanisms for the promotion and suppression of me- family genes are also reported to be down-regulated in HCCs in tastasis. We expect the results obtained in this study to aid in under- comparison to nontumorous liver tissue (12–14). 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Norio Iizuka, Masaaki Oka, Hisafumi Yamada-Okabe, et al.

Cancer Res 2002;62:3939-3944.

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