Using oligonucleotide microarray data of 45 hepatocellular carcinoma (HCC) samples, we evaluated gene expression in hepatitis B virus-positive and hepatitis C virus-positive HCCs (HBV- and HCV-HCCs) for an association with liver cirrhosis (LC). In all, 89 genes were expressed differentially between HBV-HCCs associated with LC and those not associated with LC. Among them, tumors from LC patients showed significantly lower expression levels of 72 genes and significantly higher levels of 17 genes than the levels found in tumors from non-LC patients. The former included genes responsible for signal transduction, transcription, metabolism, and cell growth. The latter included a tumor suppressor gene and a cell-growth-related gene. Only eight genes were expressed differentially between HCV-HCCs associated with and without LC. Our findings provide as a framework for clarifying the role of LC in HBV- and HCV-related hepatocarcinogenesis.
Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide (Schafer and Sorrell, 1999; Okuda, 2000). The risk of HCC increases in parallel to the progression of hepatic fibrosis associated with liver cirrhosis (LC) (Okita et al., 2002). LC is likely to play a particularly central role in hepatocarcinogenesis caused by chronic hepatitis B or C virus (HBV or HCV) infection (Poynard et al., 1997; Schafer and Sorrell, 1999; Okuda, 2000; Okita et al., 2002); however, some types of HCC are associated with chronic hepatitis or with normal liver function during continuous HBV or HCV infection. Thus, how LC affects HBV- or HCV-related hepatocarcinogenesis remain unclear. Understanding the effects of LC on hepatocarcinogenesis will help us to provide patients suffering from chronic HBV or HCV infections with suitable therapy. DNA microarray studies have detected specific gene-expression patterns associated with HCC (Lau et al., 2000; Okabe et al., 2001; Xu L et al.; Xu XR et al., 2001; Shirota et al., 2001; Delpuech et al., 2002; Goldenberg et al., 2002; Iizuka et al., 2002b). Only one report described on LC-related gene-expression pattern in HCC, but this report did not address the effects of distinct virological backgrounds (Delpuech et al., 2002). Using high-density oligonucleotide microarrays, we found distinct differences in gene-expression pattern between 14 HBV-positive HCCs (HBV-HCCs) and 31 HCV-positive HCCs (HCV-HCCs) (Iizuka et al., 2002b). We used a supervised learning method to profile the gene expression of HBV- and HCV-HCCs that developed in patients with and those without LC. Our results provide new insight into the involvement of LC in the pathogenesis of HBV- and HCV-HCCs.
Clinicopathologic characteristics of HBV- and HCV-HCCs grouped by the presence or absence of LC
The characteristics of 14 HBV-HCCs and 31 HCV-HCCs are shown in Table 1. The presence or absence of LC in patients with HBV- or HCV-HCC did not correlate with any clinicopathologic factor.
Molecular characteristics of HBV-HCCs associated with cirrhotic and noncirrhotic livers
By combining the Fisher ratio and random permutation test (Figure 1, Table 2), we selected 89 genes that were differentially expressed between HBV-HCCs from patients with LC and those from patients without LC. Among them, tumors from LC patients showed significantly lower expression levels of 72 genes and significantly higher levels of 17 genes than the levels found in tumors from non-LC patients. The former included genes responsible for signal transduction (BMP4, FLNB, GNG11, GFR, and PTPRK), transcription (NFIB, GFI1, TAF1, ONECUT1, BTF, and TRIM32), lipid metabolism (APOC1, APOC2, and APOC3), and cell growth (IGF2). The latter included a tumor suppressor gene, PHB, and a cell-growth-related gene, IGFBP3.
Molecular characteristics of HCV-HCCs associated with cirrhotic and noncirrhotic livers
In contrast to HBV-HCCs, only eight genes were differentially expressed between HCV-HCCs from patients with LC and those without LC (Figure 1, Table 3). There was a difference (n=14 vs n=31) in sample size between HBV- and HCV-HCCs in the present study. To evaluate whether the difference in sample size between the two groups affects our gene selection, we randomly selected 14 samples from HCV-HCCs. We performed gene selection based on the Fisher ratio and the random permutation test. This procedure was repeated 10 times. The number of genes related to the presence or the absence of LC in 14 samples of HCV-HCC ranged from 17 to 28 (mean, 21) (data not shown) and it was small when compared with the 89 genes identified in HBV-HCC. Interestingly, FLNB was identified as an LC-related gene in both HBV- and HCV-HCCs; however, its expression level was lower in HBV-HCCs from patients with LC than from those without LC, whereas the level was higher in HCV-HCCs from patients with LC than from those without LC (Figure 1, Table 3).
Validation of oligonucleotide microarray data by reverse transcripase (RT)–PCR analysis
To validate our microarray results, we carried out RT–PCR with RNA for six sets of HBV- and HCV-HCC samples that were subjected to the DNA microarray. The gene expression profile of four genes (IGF2, IGFBP3, FLNB, and NFIB) by the microarray analysis was reproduced even by the RT–PCR analysis (Figure 2).
In the present study, we showed that 89 genes were expressed differentially between HBV-HCCs associated with LC and those not associated with LC. Thus, HBV-HCC could be classified into two types according to gene expression profile, that is, tumors associated with LC and those not associated with LC. By contrast, in HCV-HCC, the number of genes related to the presence or the absence of LC was only eight. Additionally, it was relatively small even when we matched sample size between the two types of HCC. It was reported that HCV-HCC had more heterogeneous gene expression profile than did HBV-HCC (Delpeuch et al., 2002), suggesting that our current result is due in part to heterogeneity within samples, which may represent the biological characteristics of HCV-HCC. Namely, this difference in the influence of LC on HBV- and HCV-related hepatocarcinogenesis may be explained by the distinctive virological structures (e.g., DNA virus vs RNA virus) and biological functions of the two hepatitis viruses. HBV and HCV cause HCC via different mechanisms (Okuda, 2000). The integration of HBV DNA into chromosomal DNA of a host hepatocyte plays a major role in HBV-related hepatocarcinogenesis (Koike et al., 2002). The hepatitis B X (HBX) protein encoded by HBV DNA is considered to enhance cell proliferation and cell apoptosis, both of which are involved in hepatocarcinogenesis (Koike et al., 2002). Truncated preS2/S sequences of HBV DNA contribute to hepatocarcinogenesis (Kekule et al., 1990). These reports suggest that HBV can transform hepatocytes even in the absence of chronic inflammation. A recent study showed that HBV could be classified into six genotypes (A–F) (Kao et al., 2000). Among them, genotype B frequently causes HCC in young patients without LC and genotype C is prevalent in older patients with LC (Kao et al., 2000). Thus, genotyping of HBV in the patients included in this study may provide further insights into the present findings. Unlike HBV, HCV is an RNA virus that is not reverse-transcribed to DNA (Okuda, 2000). This means that HCV genome does not integrate into cellular genome, suggesting that numerous pathways are involved in HCV-related carcinogenesis. As a possible mechanism, HCV is thought to contribute to carcinogenesis by causing inflammation such as chronic hepatitis or LC (Schafer and Sorrell, 1999; Okuda, 2000; Okita et al., 2002). Moreover, it is known that several proteins (Core, NS5A and NS3) derived from HCV are implicated in hepatocarcinogenesis (Okuda, 2000; Lan et al., 2002). Thus, the fact that HCV-related carcinogenesis depends on numerous pathways including inflammation may support our present result that the number of genes related to the presence or the absence of LC in HCV-HCC was relatively small compared with that in HBV-HCC.
After a Q1previous study identified a group of genes that were differentially expressed between HCCs associated with LC and those not associated with it (Delpeuch et al., 2002), we further investigated this difference by analysing gene expression patterns for an association between LC and distinct virological types within HCCs. We found that many transcription-related genes (NFIB, GFI1, TAF1, ONECUT1, BTF, and TRIM32) and signaling-related genes (BMP4, FLNB, GFR, and PTPRK) were upregulated in HBV-HCCs from patients without LC. Interestingly, GFI1 and TAF1 function as a transactivator during the G1–S transition of the cell cycle. GFR also promotes cell cycle progression. Since HBX protein promotes cell proliferation (Koike et al., 2002), these two transactivators, as well as GFR, may play important roles in cell cycle regulation in cases of HBV-HCC arising from a noncirrhotic liver. Since BMP4 is overexpressed and linked to the Wnt signaling pathway in colon cancer cells (Kim et al., 2002), our present finding of a differential expression in HBV-HCCs from patients with and without LC may provide further insight into the role of Wnt/catenin pathways in the development of HCC. In our present study, molecule transport genes (SLC2A5, SLC6A6, SLC15A2, AQP3, and SEC61B) were exclusively upregulated in HBV-HCCs not associated with LC. Since cancer cells use more glucose and amino acids than do the corresponding benign tissues (Smith, 1999; Bode and Souba, 1999), this finding is intriguing for considering the value of these genes as molecular targets.
Our present data confirm and extend our previous finding that IGF2, a growth factor, is upregulated in HBV-HCC (Iizuka et al., 2002b). Namely, among the cases of HBV-HCCs, IGF2 was upregulated in tumors arising from noncirrhotic livers. Since IGF2 is an imprinted gene, its upregulation in HBV-HCC patients without LC may be caused by an epigenetic mechanism. Notably, IGFBP3, a putative negative regulator of the IGF signal pathway, was downregulated in HBV-HCC patients without LC. Furthermore, the expression of IGFBP3 is thought to be regulated by wild-type p53 at a transcriptional level (Grimberg, 2000), and the HBX protein interacts with the wild-type p53 (Huo et al., 2001). Thus, our data suggest that stimulation of the IGF signal pathway plays a central role in the development of HBV-HCC, especially when HBV-HCC arises from a noncirrhotic liver. Regarding the tumor suppressor genes, our present results show that BIN1 and PHB were downregulated in HBV-HCCs from patients with LC and those without LC, respectively, suggesting the importance of these two genes as molecular targets for chemoprevention in their respective types of HBV-HCC.
The present results show that only eight genes were related to the presence or the absence of LC in HCV-HCC patients. Among these eight genes, FLNB was also upregulated in HBV-HCCs not associated with LC. FLNB can modulate the organization of the actin cytoskeleton, bind integrins (Van der Flier et al., 2002), and interact with the HBV core protein (Huang et al., 2000), the combination of which raises the possibility of this gene as a target for preventing metastasis by controlling cancer cell motility in HCV-HCCs associated with LC and in HBV-HCCs not associated with LC. Previously, we showed that the expression of HSPA1B, which functions as a molecular chaperone in response to various stresses, was downregulated in HCV-HCCs (Iizuka et al., 2002b). In the current study, HSPA1B expression was markedly downregulated in HCV-HCC not associated with LC vs that associated with LC. This result may reflect a difference in cellular stress between HCV-HCCs in patients with LC and those without LC.
Materials and methods
In the present study, we used the oligonucleotide array data previously obtained from 14 HBV- and 31 HCV-HCC patients (Iizuka et al., 2002b). As shown in Table 1, among 14 HBV-HCC patients, eight had pathological LC, and the remaining six did not. Among 31 HCV-HCC patients, 17 had pathological LC, and the remaining 14 did not. Tumor factors were determined according to the International Union against Cancer TNM classification (Sobin and Wittekind, 2002). The χ2, Fisher's exact and Student's t-tests were used to analyse differences in patient and tumor characteristics between patients with LC and those without LC in each tumor type (HBV- or HCV-HCC). P<0.05 was considered significant.
Procedure for gene selection
After the levels of the expression of each gene were calculated with software (Affymetrix GeneChip ver.3.3 and Affymetrix Microarray Suite ver.4.0), we applied a supervised learning method to the interpretation of the data of the 45 HCC samples. We first selected genes that had expression levels >20 (arbitrary units by Affymetrix) in all the 14 HBV-HCC samples and the 31 HCV-HCC samples. Among approximately 6000 genes, this filtering resulted in the selection of 2609 genes in HBV-HCC and 2000 genes in HCV-HCC. Next, among the selected genes, we used the Fisher ratio (Iizuka et al., 2002b) to evaluate gene expression differences between tumors from patients with LC and those from patients without LC in each tumor type (HBV- or HCV-HCC), and ranked the selected genes in the order of decreasing magnitude of the Fisher ratio. To decide how many genes should be considered, we used a random permutation test described previously (Luo et al., 2001; Iizuka et al., 2002b). In the test, sample labels were randomly permuted between tumors from patients with LC and those without LC in each tumor type (HBV- or HCV-HCC), and the Fisher ratio for each gene was computed again. This random permutation of sample labels was repeated 1000 times. The Fisher ratios generated from the actual data were then assigned P’s based on the distribution of the Fisher ratios from randomized data. From the distribution of these Fisher ratios from randomized data, the top 89 genes with Fisher ratios >2.20 were determined to show statistically significant (P<0.05) differences in expression between HBV-HCCs from patients with LC and those without LC (Figure 1, Table 2). Likewise, in HCV-HCCs, we selected the top eight genes with Fisher ratios >1.04, and that could pass the random permutation test (P<0.05).
RT step and PCR were performed as previously described Iizuka et al., 1995,2002a,2002b). PCR was performed for 24 cycles for β-actin, 27 cycles for IGF2, 30 cycles for IGFBP3, FLNB, and NFIB, respectively. Each cycle consisted of denaturation at 94°C for 1 min, annealing at 61°C for 45 s and elongation at 72°C for 2 min. The primers used in this study were as follows: IGF-2, 5′-IndexTermCTGGTGGACACCC TCCAGTTC-3′ (sense) and 5′-IndexTermGCCCACGGGGTATCTGGGGAA-3′ (antisense); IGFBP3, 5′-IndexTermGCTCTGCGTCAACGCTAGTGC-3′ (sense) and 5′-IndexTermGCTTCCTGCCTTTGGAAGGGC-3′ (antisense); FLNB, 5′-IndexTermTCCGTCACCATCGAAGGCCCA-3′ (sense) and 5′-IndexTermTTGAGAGCCCTGCCCCCTTAG-3′ (antisense); NFIB, 5′-IndexTermCCCCTCTCCAAGTTCACCAGC-3′ (sense) and 5′-IndexTermTCGATGGGGCTGGAGGAAGGA-3′ (antisense); and β-actin, 5′-IndexTermCCAGAGCAAGAGAGGTAT-3′ (sense) and 5′-IndexTermCTGTGGTGGTGAAGCTGTAG-3′ (antisense). The expected sizes were 235, 439, 318, 269, and 436 bp for IGF-2, IGFBP3, FLNB, NFIB, and β-actin genes, respectively. PCR products were separated by electrophoresis on 1.5% agarose gels and visualized under ultraviolet light after ethidium bromide staining (Figure 2a). The PCR products for the four genes were semiquantitatively analysed with the use of NIH Image 1.62, and calculated as levels relative to β-actin (Figure 2b). The data were analysed by ANOVA with Fisher's projected least significant difference test. P<0.05 was considered significant.
hepatitis B virus
hepatitis C virus
- HBX protein:
hepatitis B X protein
- Gene abbreviations are used from Locus Link:
Bode BP, and, Souba WW . (1999). J. Parenter. Enteral. Nutr., 23, S33–S37.
Delpeuch O, Trabut JB, Carnot F, Feuillard J, Brechot C and Kremsdorf D . (2002). Oncogene, 21, 2926–2937.
Goldenberg D, Ayesh S, Schneider T, Pappo O, Jurim O,, Eid A, Fellig Y, Dadon T, Ariel I, de Groot N, Hochberg A and Galun E . (2002). Mol. Carcinogen., 33, 113–124.
Grimberg A . (2000). Mol. Genet. Metab., 70, 85–98.
Huang CJ, Chen YH and Ting LP . (2000). J. Biomed. Sci., 7, 160–168.
Huo TI, Wang XW, Forgues M, Wu CG, Spillare EA, Giannini C, Brechot C and Harris CC . (2001). Oncogene, 20, 3620–3628.
Iizuka N, Hazama S, Yoshimura K, Yoshino S, Tangoku A, Miyamoto K, Okita K and Oka M . (2002a). Int. J. Cancer, 99, 286–291.
Iizuka N, Oka M, Noma T, Nakazawa A, Hirose K and Suzuki T . (1995). Cancer Res., 55, 652–657.
Iizuka N, Oka M, Yamada-Okabe H, Mori N, Tamesa T, Okada T, Takemoto N, Tangoku A, Hamada K, Nakayama H, Miyamoto T, Uchimura S and Hamamoto Y . (2002b). Cancer Res., 62, 3939–3944.
Kao JH, Chen PJ, Lai MY and Chen DS . (2000). Gastroenterology, 118, 554–559.
Kekule AS, Lauer U, Meyer M, Caselmann WH, Hofschneider PH and Koshy R . (1990). Nature, 343, 457–461.
Kim JS, Crooks H, Dracheva T, Nishanian TG, Singh B, Jen J and Waldman T . (2002). Cancer Res., 62, 2744–2748.
Koike K, Tsutsumi T, Fujie H, Shintani Y and Kyoji M . (2002). Oncology, 62, 29–37.
Lan KH, Sheu ML, Hwang SJ, Yen SH, Chen SY, Wu JC, Wang YJ, Kato N, Omata M, Chang FY and Lee SD . (2002). Oncogene, 21, 4801–4811.
Lau WY, Lai PB, Leung MF, Leung BC, Wong N, Chen G, Leung TW and Liew CT . (2000). Oncol. Res., 12, 59–69.
Luo J, Duggan DJ, Chen Y, Sauvageot J, Ewing CM, Bittner ML, Trent JM and Isaacs WB . (2001). Cancer Res., 61, 4683–4688.
Okabe H, Satoh S, Kato T, Kitahara O, Yanagawa R, Yamaoka Y, Tsunoda T, Furukawa Y and Nakamura Y . (2001). Cancer Res., 61, 2129–2137.
Okita K, Sakaida I and Hino K . (2002). Oncology, 62, 24–28.
Okuda K . (2000). J. Hepatol., 32, 225–237.
Poynard T, Bedossa P and Opolon P . (1997). Lancet, 349, 825–832.
Schafer DF and Sorrell MF . (1999). Lancet, 353, 1253–1257.
Shirota Y, Kaneko S, Honda M, Kawai HF and Kobayashi K . (2001). Hepatology, 33, 832–840.
Smith TA . (1999). Br. J. Biomed. Sci., 56, 285–292.
Sobin LH and Wittekind C . (2002). TNM Classification of Malignant Tumours, 6th edn., UICC, Wiley-Liss, New York, pp. 81–83.
van der Flier A, Kuikman I, Kramer D, Geerts D, Kreft M, Takafuta T, Shapiro SS and Sonnenberg A . (2002). J. Cell Biol., 156, 361–376.
Xu L, Hui L, Wang S, Gong J, Jin Y, Wang Y, Ji Y, Wu X, Han Z and Hu G . (2001). Cancer Res., 61, 3176–3681.
Xu XR, Huang J, Xu ZG, Qian BZ, Zhu ZD, Yan Q, Cai T, Zhang X, Xiao HS, Qu J, Liu F, Huang QH, Cheng ZH, Li NG, Du JJ, Hu W, Shen KT, Lu G, Fu G, Zhong M, Xu SH, Gu WY, Huang W, Zhao XT, Hu GX, Gu JR, Chen Z and Han ZG . (2001). Proc. Natl. Acad. Sci. USA, 98, 15089–15094.
This work was supported in part by a Grant-in-Aid from the Ministry of Education, Science, Sports and Culture of Japan (No. 12671230).
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Iizuka, N., Oka, M., Yamada-Okabe, H. et al. Differential gene expression in distinct virologic types of hepatocellular carcinoma: association with liver cirrhosis. Oncogene 22, 3007–3014 (2003). https://doi.org/10.1038/sj.onc.1206401
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