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Genome-wide Analysis of Gene Expression in Human Hepatocellular Carcinomas Using cDNA Microarray

Identification of Genes Involved in Viral Carcinogenesis and Tumor Progression¹

Laboratory of Molecular Medicine, Human Genome Center, Institute of Medical Science, The University of Tokyo, Tokyo 108-8639 [H. O., S. S., T. K., O. K., R. Y., Y. F., Y. N.], Department of Gastroenterological Surgery, Kyoto University Graduate School of Medicine, Kyoto 606-8507 [H. O., T. K., Y. Y.], and Laboratory for Medical Informatics, SNP Research Center, Riken (Institute of Physical and Chemical Research), Tokyo 108-8639 [T. T.], Japan

	ABSTRACT

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To disclose detailed genetic mechanisms in hepatocellular carcinoma (HCC) with a view toward development of novel therapeutic targets, we analyzed expression profiles of 20 primary HCCs and their corresponding noncancerous tissues by means of cDNA microarrays consisting of 23,040 genes. Up-regulation of mitosis-promoting genes was observed in the majority of the tumors examined. Some genes showed expression patterns in hepatitis B virus-positive HCCs that were different from those in hepatitis C virus-positive HCCs; most of them encoded enzymes that metabolize carcinogens and/or anticancer agents. Furthermore, we identified a number of genes associated with malignant histological type or invasive phenotype. Accumulation of such data will make it possible to define the nature of individual tumors, to provide clues for identifying new therapeutic targets, and ultimately to optimize treatment of each patient.

	INTRODUCTION

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Primary HCC³ is one of the most common malignancies in the world. Despite development of novel therapeutic methods in recent years, prognosis of advanced HCC remains very poor. Major risk factors for HCC are chronic hepatitis resulting from infection with HBV or HCV, and exposure to various exogenous carcinogens including aflatoxin B1 (1) . Molecular approaches have recently revealed involvement of altered TP53, CTNNB1 (ß-catenin), and/or AXIN1 genes in hepatocarcinogenesis (2 , 3) . However, these genetic changes do not precisely reflect the biological nature of cancer cells or the clinical characteristics of individual HCC patients. Like other cancers, HCCs manifest diverse clinicopathological and biological phenotypes including grade of differentiation, proliferation rate, ability to invade vessels, potential for metastasis, sensitivity to chemotherapeutic agents, and so on. Hence, analysis of expression profiles of a large number of genes in clinical HCC materials is an essential step toward clarifying the detailed mechanisms of hepatocarcinogenesis and discovering target molecules for the development of novel therapeutic drugs.

cDNA microarray technology, which enables investigators to obtain comprehensive data with respect to gene-expression profiles, is progressing rapidly. Several studies have already demonstrated the usefulness of this technique for identifying novel cancer-related genes and for classifying human cancers at the molecular level (4 , 5) .

In this paper, we report the identification of genes the expression of which has been altered during hepatocarcinogenesis through the use of a genome-wide cDNA microarray containing 23,040 genes. Expression profiles of these genes in 20 primary HCCs fell into three categories that correlated well with the infection status and type of hepatitis virus. Analyses of these profiles along with clinicopathological data also facilitated identification of genes associated with tumor differentiation and vessel invasiveness. This large body of information not only furthers an understanding of the mechanisms of hepatocarcinogenesis but also reveals novel features of known genes and identifies additional biological factors involved in liver cancer.

	MATERIALS AND METHODS

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Patients and Tissue Samples.
Primary HCCs and corresponding noncancerous liver tissues were obtained with informed consent from 20 patients who underwent hepatectomy. Patient profiles were obtained from medical records. Serologically, 10 cases were hepatitis B surface antigen-positive and 10 cases were HCV-positive. No cases with coinfections of HBV and HCV were included in this study. Histopathological classification was performed according to the Edmondson grading system; clinical stages were determined according to the Union International Contre Cancer TNM classification. No significant differences were seen between HBV-positive and HCV-positive status with respect to age, sex, grade of differentiation, vessel invasion, or tumor stage.

cDNA Microarrays.
We fabricated a "genome-wide" cDNA microarray with 23,040 cDNAs selected from the UniGene database of the National Center for Biotechnology Information. The cDNAs were amplified by reverse transcription-PCR using poly(A)+RNA isolated from various human organs as templates; lengths of the amplicons ranged from 200 to 1100 bp without repetitive or poly(A) sequences. The PCR products were spotted in duplicate on type-7 glass slides (Amersham) using an Array Spotter Generation III (Amersham). Each slide contained 52 housekeeping genes, to normalize the signal intensities of the different fluorescent dyes.

RNA Preparation, Hybridization, and Acquisition of Data.
Frozen specimens were serially sectioned in 10-µm slices and stained with H&E to define the analyzed regions. To avoid cross-contamination of cancer and noncancerous cells, we prepared these two populations by laser-captured microdissection. Total RNA was extracted from each population and then amplified using Ampliscribe T7 Transcription Kit (Epicentre Technologies). The preparation of probes, hybridization, and scanning was performed as described previously (6) . The fluorescence intensities of Cy5 (nontumor) and Cy3 (tumor) for each target spot were adjusted so that the mean Cy5 and Cy3 intensities of 52 housekeeping genes for each slide were equal.

Validation of Data.
To assess the reproducibility of the normalized intensity ratios, we compared the log₂(Cy3:Cy5 intensity ratio) of the 52 housekeeping genes between different slide sets. When the difference between normalized logarithmic ratios from two experiments was less than 1.0, we defined the data as reproducible. The reproducibility was more than 90% when the intensities of Cy3 and Cy5 were both above 25,000.

Classification of 20 HCCs According to Gene Expression Profiles.
We applied the hierarchical clustering method to both genes and samples. To obtain reproducible clusters, we used only selected genes that passed the cutoff filter (both Cy3 and Cy5 signals greater than 25,000 in more than 80% cases examined). The analysis was performed using web-available software ("Cluster" and "TreeView") written by M. Eisen.⁴ Before applying the clustering algorithm, the fluorescence ratio for each spot was first log-transformed; then the data for each sample were centered to remove experimental biases.

Identification of Genes Responsible for Clinicopathological Factors.
We first arranged the relative expression of each gene (Cy3:Cy5 intensity ratio) into one of four categories: up-regulated (ratio, >2.0), down-regulated (ratio, <0.5), unchanged (ratio, between 0.5 and 2.0), and not expressed (or slight expression but under the cutoff level for detection). We used these categories to detect changes in expression that were common among samples as well as specific to a certain subgroup. To detect differentially expressed genes, we recorded the number of samples in each category within each subgroup, for each gene. Then we calculated the U values of Mann-Whitney tests, which measured how the sample distributions between subgroups overlap. The number of samples within each group is counted and, according to the order of the category, the number of overlapped samples is incorporated into the U value. A small U shows that the sample distribution of the two groups is clearly separated, e.g., commonly up-regulated in the HBV group and down-regulated in the HCV group. We applied a hierarchical clustering algorithm to all of the selected genes using hamming distance (edit distance).

	RESULTS AND DISCUSSION

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Identification of Genes That Were Differently Regulated in HCCs.
To identify genes generally involved in hepatocarcinogenesis, we compared expression profiles between 20 HCCs and their corresponding noncancerous liver tissues by means of cDNA microarray. We excluded individual data when Cy3 and Cy5 signals were <25,000 because data were not reliable for genes giving low signal intensities (see "Materials and Methods"). When we applied a cutoff signal:intensity ratio of cancer:noncancer at 2.0 165 genes including 69 ESTs were selected as being up-regulated in 75% or more of the cases examined (Table 1) . This list of up-regulated genes contained MAP4K1 as well as MAPK3, suggesting that activation of the MAPK pathway is a common feature of hepatocarcinogenesis. Interestingly, expression of several genes associated with mitosis, including CDC23, TUBG1, CBX1, CKS1, PCTK1, PSMD8, CSE1L, TTK, and PLK1, was commonly increased in cancer cells. As a cell-cycle modulator, CDC23 is a known component of the anaphase-promoting complex (APC) and leads to metaphase/anaphase transition through degradation of cyclin B. TUBG1 (-tubulin) and CBX1 participate in centrosome formation (7 , 8) ; CKS1 and PCTK1, encoding cdc2/cdc28 kinases, are essential for activation of the anaphase-promoting complex. PSMD8 (26S proteasome subunit p31) is reportedly responsible for activation of these kinases (9) . Others have reported that CSE1L, TTK, and PLK1 are associated with formation of the mitotic spindle (7 , 10) and that PLK1 can affect the number of centrosomes when exogenously expressed (11) ; overexpression of PLK1 has been correlated with poor prognosis in a subset of human cancers (12) . Our comprehensive expression data for these genes may account for a high incidence of chromosomal instability in HCC, and they suggest that promotion of the mitotic process is generally involved in hepatocarcinogenesis. Therefore, regulation of these mitosis-associated genes either by chemotherapeutic agents or by gene delivery might be an effective therapeutic strategy for HCCs.

Classification of HCCs by Gene Expression Profiles.
We further investigated whether clinical HCCs could be classified into groups on the basis of their gene-expression profiles. For this purpose, we used the hierarchical clustering method. To obtain reproducible clusters, we selected 4,531 genes that passed the cutoff filter (both cy3 and cy5 signals greater than 25,000). The overall expression patterns across 20 HCC samples are shown in Fig. 1 . The analyses resulted in the clustering of identical genes spotted on different positions into adjacent rows, indicating the reliability of the expression data. The 20 HCCs examined fell into three groups, as the dendrogram shows.

View larger version (59K): [in this window] [in a new window]   Fig. 1. Overall patterns of expression of 4531 genes across the 20 HCC samples. Red color, overexpression in cancer cells; green color, underexpression in cancer cells; black, unchanged expression; gray, no expression was detected (intensities of both Cy3 and Cy5 under the cutoff value). Graduated color patterns correspond to the degrees of expression changes. Each row, a gene; each column, a HCC sample. The dendrogram of the 20 cases at the right of the matrix indicates the degree of similarity between tumor samples demonstrating that the tumors are clustered in three groups (red, blue, or green). Sample No.123 is a very well differentiated tumor and does not appear to belong to any of the clusters. The dendrogram at the top also indicates the degree of similarity among the 4531 genes examined by expression patterns.

Identification of Genes Related to HBV-positive or HCV-positive Status.
To identify genes responsible for the differences between HBV-positive and HCV-positive tumors, we performed Mann-Whitney tests and found that 19 known genes and 21 ESTs showed significantly different expression patterns between these two groups. Among the 19 known genes (Fig. 2) , seven (GPX2, CYP2E, EPHX1, AKR1C4, FMO3, UGT1A1, and UGT2B10) encode key molecules for activating chemotherapeutic drugs or detoxifying xenobiotic carcinogens.

View larger version (60K): [in this window] [in a new window]   Fig. 2. Nineteen known genes of the 40 that were differentially expressed between HBV-based and HCV-based HCCs. Changes in relative expression are presented in graduated color patterns. Red, overexpression; green, underexpression; yellow, unchanged expression. The number to the left of each row is the U value of the Mann-Whitney test, and the dendrogram indicates the degree of similarity between the genes selected. *, the seven genes that encode key enzymes for detoxification of chemotherapeutic drugs or xenobiotic carcinogens.

On the other hand, expression of UGT1A1, UGT2B10, and GPX2 was preferentially repressed in HBV-positive HCCs (UGT1A1 was reduced in 8 of 10 HBV-positive HCCs examined), but expression levels of these genes were unchanged in most HCV-positive HCCs. In accordance with our observations, Strassburg et al. (21) have shown decreased expression of UGT1A1 in HCCs as well as in hepatic adenomas, implicating UGT1A1 in an early step of hepatocarcinogenesis. UGT1A1 and UGT2B10 catalyze Phase II conjugation reactions, which are frequently related to detoxification of the active forms of carcinogens. GPX2, a major form of glutathione peroxidase in liver, functions as an antioxidant, and decreased glutathione peroxidase activity in HCCs has been reported elsewhere (22) . Hence, reduced activities of these enzymes may reflect enhanced exposure of hepatocytes to activated carcinogens or radicals. Our results suggest that decreased expression of detoxification enzymes may be involved especially in the mechanisms of HBV-specific hepatocarcinogenesis. Furthermore, because UGT1A1 also catalyzes glucuronidation of SN-38, an active form of irinotecan (23) , HBV-positive HCCs may show greater sensitivity to irinotecan than do HCV-positive HCCs. Different expression patterns among detoxification enzymes should provide information for optimizing the choice and/or the dosage of anticancer drugs for treating HCC patients on an individual basis.

Results of comparing expression profiles between HBV-positive and HCV-positive HCCs implied that hepatitis viruses affect expression of dozens of genes in HCC in a type-specific manner, invoking partly different mechanisms of carcinogenesis. Consequently, identification of genes defining virus-type-specific expression profiles would contribute to our ability to develop virus-type-dependent treatment regimens.

Identification of Genes Related to HCC Progression.
As in the multistep model of adenoma-to-carcinoma sequence accepted for colorectal tumors, HCCs are considered to develop as well-differentiated tumors and then progress to moderately-to-poorly differentiated states (24) . A comparison of expression profiles between well-differentiated tumors (Edmondson grade I; n = 7) and moderately to poorly differentiated tumors (Edmondson grade II or III; n = 13; Fig. 3A ) by means of Mann-Whitney test identified a total of 321 genes (including 193 ESTs) that showed different expression patterns between the two histologically divided groups. In addition to the genes encoding liver-specific proteins, they included genes associated with apoptosis and the immune system. Apoptosis-related genes including TNFSF10, TNFSF14, GADD34, CFLAR, CLU, CASP6, and phosphatidylserine receptor (25 , 26) were preferentially reduced in moderately-to-poorly differentiated tumors, implying that a reduced rate of apoptosis is a major characteristic of tumor progression. Genes associated with immune systems included MAGEC1, one of the tumor antigens recognized by CTLs, whose expression was also repressed only in moderately-to-poorly differentiated tumors. Reduced expression of genes encoding immune targets may confer a growth advantage by allowing tumor cells to escape from immune surveillance.

View larger version (74K): [in this window] [in a new window]   Fig. 3. Genes the expression of which is related to HCC progression. We used colors corresponding to relative gene expression as in Fig. 2 . Genes related to Edmondson grade (A) and to vascular invasion (B). Among the 321 genes related to histological grade and 151 genes related to vascular invasion, 128 and 41 named genes are listed here, respectively. Blue, genes that are associated with both vascular invasion and grade of differentiation.

View larger version (64K): [in this window] [in a new window]   Fig. 3A. Continued.

The genes and their products represented by the numerous ESTs of unknown function that we classified in the same clusters as genes associated with apoptosis or immunity may be useful as novel targets for drug discovery or tumor markers. Accumulation of data with respect to expression profiles of cancer specimens, clinicopathological data, sensitivity to treatment, and prognosis will not only help us to understand the precise mechanisms of carcinogenesis but also yield practical information for identifying optimized therapeutic modalities and novel therapeutic targets.

	ACKNOWLEDGMENTS

 
We thank Hideaki Ogasawara, Jun-ichi Okutsu, Kenji Hirotani, Hiroko Bando, Noriko Nemoto, and Noriko Sudo for the fabrication of cDNA microarray.

	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 Research for the Future Program Grant 96L00102 from the Japan Society for the Promotion of Science.

² To whom requests for reprints should be addressed, at Laboratory of Molecular Medicine, Human Genome Center, Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan. Phone: 81-3-5449-5372; Fax: 81-3-5449-5433; E-mail: yusuke{at}ims.u-tokyo.ac.jp

³ The abbreviations used are: HCC, hepatocellular carcinoma; HBV, hepatitis B virus; HCV, hepatitis C virus; EST, expressed sequence tag.

⁴ Internet address: http://www.microarrays.org/software.

Received 11/27/00. Accepted 1/ 4/01.

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				S. T. Cheung, K. L. Leung, Y. C. Ip, X. Chen, D. Y. Fong, I. O. Ng, S. T. Fan, and S. So Claudin-10 Expression Level is Associated with Recurrence of Primary Hepatocellular Carcinoma Clin. Cancer Res., January 15, 2005; 11(2): 551 - 556. [Abstract] [Full Text] [PDF]

				N. Uchida, T. Tsunoda, S. Wada, Y. Furukawa, Y. Nakamura, and H. Tahara Ring Finger Protein 43 as a New Target for Cancer Immunotherapy Clin. Cancer Res., December 15, 2004; 10(24): 8577 - 8586. [Abstract] [Full Text] [PDF]

				T. Nakatsura, H. Komori, T. Kubo, Y. Yoshitake, S. Senju, T. Katagiri, Y. Furukawa, M. Ogawa, Y. Nakamura, and Y. Nishimura Mouse Homologue of a Novel Human Oncofetal Antigen, Glypican-3, Evokes T-Cell-Mediated Tumor Rejection without Autoimmune Reactions in Mice Clin. Cancer Res., December 15, 2004; 10(24): 8630 - 8640. [Abstract] [Full Text] [PDF]

				K. Breuhahn, S. Vreden, R. Haddad, S. Beckebaum, D. Stippel, P. Flemming, T. Nussbaum, W. H. Caselmann, B. B. Haab, and P. Schirmacher Molecular Profiling of Human Hepatocellular Carcinoma Defines Mutually Exclusive Interferon Regulation and Insulin-Like Growth Factor II Overexpression Cancer Res., September 1, 2004; 64(17): 6058 - 6064. [Abstract] [Full Text] [PDF]

				Y. Hippo, K. Watanabe, A. Watanabe, Y. Midorikawa, S. Yamamoto, S. Ihara, S. Tokita, H. Iwanari, Y. Ito, K. Nakano, et al. Identification of Soluble NH2-Terminal Fragment of Glypican-3 as a Serological Marker for Early-Stage Hepatocellular Carcinoma Cancer Res., April 1, 2004; 64(7): 2418 - 2423. [Abstract] [Full Text] [PDF]

				V. V. Kalinichenko, M. L. Major, X. Wang, V. Petrovic, J. Kuechle, H. M. Yoder, M. B. Dennewitz, B. Shin, A. Datta, P. Raychaudhuri, et al. Foxm1b transcription factor is essential for development of hepatocellular carcinomas and is negatively regulated by the p19ARF tumor suppressor Genes & Dev., April 1, 2004; 18(7): 830 - 850. [Abstract] [Full Text] [PDF]

				W. Kim, S. Oe Lim, J.-S. Kim, Y. H. Ryu, J.-Y. Byeon, H.-J. Kim, Y.-I. Kim, J. S. Heo, Y. M. Park, and G. Jung Comparison of Proteome between Hepatitis B Virus- and Hepatitis C Virus-Associated Hepatocellular Carcinoma Clin. Cancer Res., November 15, 2003; 9(15): 5493 - 5500. [Abstract] [Full Text] [PDF]

				V. Paradis, I. Bieche, D. Dargere, I. Laurendeau, C. Laurent, P. Bioulac Sage, C. Degott, J. Belghiti, M. Vidaud, and P. Bedossa Molecular Profiling of Hepatocellular Carcinomas (HCC) Using a Large-Scale Real-Time RT-PCR Approach: Determination of a Molecular Diagnostic Index Am. J. Pathol., August 1, 2003; 163(2): 733 - 741. [Abstract] [Full Text] [PDF]

				H. Okabe, S. Satoh, Y. Furukawa, T. Kato, S. Hasegawa, Y. Nakajima, Y. Yamaoka, and Y. Nakamura Involvement of PEG10 in Human Hepatocellular Carcinogenesis through Interaction with SIAH1 Cancer Res., June 15, 2003; 63(12): 3043 - 3048. [Abstract] [Full Text] [PDF]

				S. Kakiuchi, Y. Daigo, T. Tsunoda, S. Yano, S. Sone, and Y. Nakamura Genome-Wide Analysis of Organ-Preferential Metastasis of Human Small Cell Lung Cancer in Mice Mol. Cancer Res., May 1, 2003; 1(7): 485 - 499. [Abstract] [Full Text] [PDF]

				K. Meyer, J.-S. Lee, P. A. Dyck, W.-Q. Cao, M.S. Rao, S. S. Thorgeirsson, and J. K. Reddy Molecular profiling of hepatocellular carcinomas developing spontaneously in acyl-CoA oxidase deficient mice: comparison with liver tumors induced in wild-type mice by a peroxisome proliferator and a genotoxic carcinogen Carcinogenesis, May 1, 2003; 24(5): 975 - 984. [Abstract] [Full Text] [PDF]

				J. W. Kim and X. W. Wang Gene expression profiling of preneoplastic liver disease and liver cancer: a new era for improved early detection and treatment of these deadly diseases? Carcinogenesis, March 1, 2003; 24(3): 363 - 369. [Abstract] [Full Text] [PDF]

				R. A. Anders, L. M. Yerian, M. Tretiakova, J. M. Davison, R. J. Quigg, P. H. Domer, J. Hoberg, and J. Hart cDNA Microarray Analysis of Macroregenerative and Dysplastic Nodules in End-Stage Hepatitis C Virus-Induced Cirrhosis Am. J. Pathol., March 1, 2003; 162(3): 991 - 1000. [Abstract] [Full Text] [PDF]

M. W. Smith, Z. N. Yue, G. K. Geiss, N. Y. Sadovnikova, V. S. Carter, L. Boix, C. A. Lazaro, G. B. Rosenberg, R. E. Bumgarner, N. Fausto, et al. Identification of Novel Tumor Markers in Hepatitis C Virus-associated Hepatocellular Carcinoma Cancer Res., February 15, 2003; 63(4): 859 - 864. [Abstract] [Full Text] [PDF]