[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
Gene expression profiles of hepatocellular carcinomas (HCCs) associated with hepatitis 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 HBVassociated HCCs, and expression levels of the remaining 52 genes were
increased in HCV-associated HCCs. The 31 genes up-regulated in HBVassociated 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 HCVassociated HCCs.
nisms 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 –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.
Introduction
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 – 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 mechaReceived 3/18/02; accepted 5/23/02.
The costs of publication of this article were defrayed in part by the payment of page
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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).
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@po.
cc.yamaguchi-u.ac.jp.
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.
Materials and Methods
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 1. 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).
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 SepasolRNAI (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
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GENE EXPRESSION PATTERNS LINKED TO VIRUS TYPE IN HCC
Table 1 Patient characteristics per study group
BT group (n ⫽ 14)
Sex
Male/Female
Age (yrs)
Primary lesion
Single tumor
Multiple tumors
Tumor size (cm)
Stagec
I/II
IIIA/IVA/IVB
Histological gradec
G1
G2
G3
Venous invasionc
(⫺)
(⫹)
Nontumorous liver
Nonspecific change
Chronic hepatitis
Liver cirrhosis
a
CT group (n ⫽ 31)
P
N.S.
8/6
51.5 ⫾ 3.0
22/9
66.0 ⫾ 1.1
6
8
5.9 ⫾ 1.4
12
19
5.5 ⫾ 0.8
5
9
12
19
0
9
5
2
25
4
7
7
22
9
2
4
8
1
13
17
0.0001b
N.S.
N.S.
N.S.
N.S.
N.S.
N.S.
a
BT group, patients with HBV-associated HCC; CT group, patients with HCVassociated HCC; NS, not significant.
b
P by Mann-Whitney U test.
c
Assessment based on TNM classification by the International Union against cancer.
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
␮l of buffer containing 0.1 M 2-(N-morpholino)ethanesulfonic acid (pH 6.7), 1
M NaCl, 0.01% Triton X-100, 20 ␮g of herring sperm DNA, 100 ␮g of
acetylated BSA, 10 ␮g 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
F共i兲 ⫽
共␮BT共i兲 ⫺ ␮CT共i兲兲2
P共BT兲␴2BT共i兲 ⫹ P共CT兲␴2CT共i兲
where ␮BT(i) and ␴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) ⬎ 䡠䡠䡠 ⬎ 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
4
Gene abbreviations are used based on LocusLink at internet address: www.ncbi.nlm.
nih.gov/LocusLink/.
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 ␮l of cDNA solution (equivalent to the cDNA from 100 ng of initial
RNA) were amplified in 45 ␮l of PCR mixture (17) containing 25 pmol of each
primer for IGF-2 and ␤-actin genes. PCR was performed for 26 cycles for
IGF-2 and 24 cycles for ␤-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 ␤-actin, 5⬘-CCAGAGCAAGAGAGGTAT-3⬘ (sense) and 5⬘-CTGTGGTGGTGAAGCTGTAG-3⬘ (antisense). The expected sizes were 235 and 436 bp for IGF-2 and
␤-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 ␤-actin gene.
Statistical Analysis. Clinicopathological factors pertaining to B- and Ctype HCCs were compared, and differences were analyzed by ␹2 test, Fisher’s
exact test, Student’s t test, or Mann-Whitney’s U test (Table 1). 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
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
1. 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, 7–9). 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. 1; Table 2).
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 detoxificationrelated genes (i.e., MT1E, MT1H, ADH1B, ADH4, CYP2A7, and
CYPIIE) and many immune response-related genes (Fig. 1; Table 2).
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
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GENE EXPRESSION PATTERNS LINKED TO VIRUS TYPE IN HCC
Fig. 1. Gene expression profiles linked to HBVand HCV-associated HCCs. Color displays of the
expressions of (a) 31 genes up-regulated in HBVassociated 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.
the second and third largest Fisher ratio, respectively (Table 2). 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
5
6
Internet address: www3.ncbi.nlm.nih.gov/PubMed/.
Internet address: www.tigr.org/tdb/hgi/searching/reports.html.
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
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GENE EXPRESSION PATTERNS LINKED TO VIRUS TYPE IN HCC
Table 2 The 83 genes for which expression levels differed between HBV- and HCV-associated HCCs
Fisher
ratio
BT/Na CT/N
Name
Thirty-one genes up-regulated in HBV-associated HCC
1.962 8.943 1.745 Tartrate-resistant acid phosphatase type 5
1.883 9.596 3.629 Ribosomal protein L39-like 1
1.800 4.444 1.225 Carcinoma-associated antigen GA733-2
1.677 3.087 1.315 Histone H1(0)
1.117 7.357 2.087 H19 RNA
1.055 2.980 1.149 Splicing factor (SF1-Bo isoform)
1.050 3.168 1.505 Serine/threonine kinase stk2
1.049 3.421 1.269 KIAA0159
0.874 3.529 1.123 MAP kinase kinase 4 (MKK4)
0.817 6.329 2.506 Ubiquitin-conjugating enzyme
0.789 0.706 0.294 MAP kinase kinase 5
0.783 1.401 0.680 Hyaluronan receptor
0.776 6.781 2.497 Glutathione S-transferase ␲
0.775 2.181 0.803 KIAA0184
0.774 0.828 0.406 Peroxisome proliferator-activated receptor ␥
0.764 6.721 1.692 Macrophage-capping protein
0.691 1.082 0.366 c-Myc promoter-binding protein
0.675 3.792 1.482 Asparagine synthetase
0.671 4.943 2.105 HSU35835 Human DNA-PK mRNA
0.649 5.396 2.335 HLA class II region expressed gene KE4
0.616 12.155 5.797 H. sapiens H4/g gene for H4 histone
0.567 4.135 1.529 MUC18 glycoprotein
0.548 3.228 1.552 Heat shock 70kD protein 1B
0.530 6.358 2.943 Type IV collagenase
0.514 1.114 0.515 Osteomodulin
0.513 1.539 0.571 Vascular endothelial growth factor
0.510 10.368 4.673 Siah-binding protein 1
0.493 1.731 0.658 Insulin-like growth factor 2
0.459 4.275 1.547 Myogenic factor 4
0.425 1.418 0.569 Immunoglobulin ␮
0.415 2.534 1.154 MAP kinase phosphatase 4
Fifty-two genes up-regulated in HCV-associated HCCs
1.426 0.281 0.590 Complement component Clr
1.082 0.157 6.760 p27
0.970 0.040 0.293 Tyrosine aminotransferase
0.968 0.131 0.316 Zn-␣2-glycoprotein
0.918 0.221 0.461 Human clone 23815 mRNA
0.911 0.154 0.315 Complement component C6
0.891 0.561 1.219 (2⬘-5⬘) oligo A synthetase E
0.858 0.091 0.298 3,4-catechol estrogen UDP-glucuronosyltransferase
0.856 0.549 1.519 NK receptor
0.835 0.529 1.158 Putative carboxylesterase
0.830 0.123 0.799 Apolipoprotein apoC-IV
0.797 0.145 0.467 Tryptophan oxygenase
0.795 0.188 0.428 Plasma kallikrein-sensitive glycoprotein
0.789 0.155 0.419 Paraoxonase 3
0.787 1.230 2.536 Smooth muscle LIM protein (h-SmLIM)
0.737 0.482 1.748 Interferon-induced 17-kDa/15-kDa protein
0.719 0.085 0.277 Glycogen synthase 2
0.699 0.060 0.231 Aldo-keto reductase family 1
0.698 0.180 0.408 4-aminobutyrate aminotransferase
0.676 0.111 0.358 Serum amyloid A4
0.660 0.096 0.264 Alcohol dehydrogenase 4 (class II)
0.660 0.034 0.261 Metallothionein from cadmium-treated cells
0.654 0.181 0.382 Complement 8 ␣ subunit
0.651 0.186 0.381 Coagulation factor XI
0.638 0.926 2.399 Polymeric immunoglobulin receptor
0.631 0.329 0.895 Human follistatin gene
0.625 0.175 0.360 Human hemopexin gene
0.623 0.213 0.697 Hydroxysteroid (11-␤) dehydrogenase 1
0.608 1.257 2.718 RIG-G
0.607 0.062 0.326 Nicotinamide N-methyltransferase
0.596 0.929 2.027 Dipeptidyl peptidase IV
0.595 0.133 0.440 Cytochrome P450IIE1 (ethanol-inducible)
0.591 0.038 0.238 Nicotinamide N-methyltransferase
0.582 0.055 0.200 H. sapiens mRNA for metallothionein
0.568 0.102 0.219 Human metallothionein-le gene (hMT-le)
0.560 0.492 1.264 Thyroxine-binding globulin
0.521 0.146 0.555 Cystathionine ␥-lyase
0.509 0.257 0.582 Pre-B cell enhancing factor
0.506 0.733 1.469 KIAA0216
0.503 0.018 1.401 Serum amyloid A2
0.491 0.500 2.833 GP-39 cartilage protein
0.478 0.131 0.364 Complement factor H-related protein 4
0.474 0.032 0.336 Serine dehydratase
0.468 0.143 1.760 Serum amyloid A protein
0.458 0.268 0.591 CD14 antigen
0.450 0.882 1.837 CTP synthetase
0.447 0.041 0.137 Cytochrome P450, subfamily IIA, polypeptide 7
0.440 1.029 2.978 Macrophage lectin 2
0.423 0.513 1.219 Neuronal nitric oxide synthase
0.420 1.159 2.565 Transmembrane 4 superfamily member 3
0.410 0.169 0.351 Alcohol dehydrogenase 1B, ␤ subunit
0.404 0.193 0.410 Angiogenin
a
b
c
Accession
no.b
Abbreviationc
J04430
HG2874-HT3018
M93036
X03473
M32053
Y08766
L20321
D63880
L36870
U45328
U25265
U29343
M24485
D80006
L40904
M94345
X63417
M27396
U35835
D82060
X60486
M29277
M59830
J05070
AB000114
M27281
U51586
HG3543-HT3739
X17651
V00563
Y08302
ACP5
RPL39L
TACSTD1
H1F0
H19
SF1
STK2
CNAP1
MAP2K4
UBE21
MAP2K5
HMMR
GSTP1
KIAA0184
PPARG
CAPG
IRLB
ASNS
J04080
X67325
X52520
X59766
U90916
X72177
X02874
J05428
X99479
Y09616
U32576
U32989
D38535
L48516
U46006
M13755
S70004
Z28339
L32961
S48983
X56411
V00594
U08006
M20218
X73079
M19481
M36803
M76665
U52513
U08021
X60708
J02843
U51010
X64177
M10942
M14091
S52028
U02020
D86970
J03474
Y08374
X98337
J05037
X51441
X13334
X52142
M33317
D50532
U17327
M35252
X03350
M11567
C1S
IFI27
TAT
AZGP1
HKE4
H4FG
MCAM
HSPA1B
MMP9
OMD
VEGF
SIAHBP1
IGF2
MYOG
DUSP9
C6
OAS1
UGT2B7
CES2
APOC4
TDO2
ITIH4
PON3
CSRP2
ISG15
GYS2
AKR1D1
ABAT
SAA4
ADH4
C8A
F11
PIGR
FST
HPX
HSD11B1
IFIT4
NNMT
DPP4
CYP2E
NNMT
MT1H
MT1E
SERPINA7
CTH
PBEF
TIAF1
SAA2
CHI3L1
FHR-4
SDS
SAA
CD14
CTPS
CYP2A7
HML2
NOS1
TM4SF3
ADH1B
ANG
Locus
Function
19p13.3-p13.2
3q27
4q
22q13.1
11p15.5
11q13
3p21.1
12p13.3
17p11.2
16p13.3
15q22.2-q22.31
5q33.2-qter
11q13
21q22.3
3p25
2cen-q24
15q22.1
7q21-q21
Unknown
6p21.3
6p21.3
11q23.3
6p21.3
20q11.2-q13.1
9q22.1
6p12
8q24.2-qter
11p15.5
1q31-q41
Unknown
Xq28
Metabolism/differentiation of macrophage
Ribosomal protein
Cell adhesion
Basic nuclear protein
Unknown
Signal transduction
Cell cycle regulation
Unknown
Signal transduction
Proteolysis and peptidolysis
Signal transduction
Cell motility
Detoxification and drug metabolism
Unknown
Transcription
Cytoskeleton
DNA binding protein
Metabolism/cell cycle regulation
Unknown
Unknown
Basic nuclear protein
Cell adhesion
Stress response
Invasion and metastasis
Leucine-rich proteoglycan
Angiogenesis and metastasis
Transcription
Growth factor
Transcription
Immune response
Signal transduction
12p13
14q32
16q22.1
7q22.1
Unknown
5p13
12q24.1
4q13
Unknown
16q22.1
19q13.2
4q31-q32
3p21-p14
7q21.3
12q21.1
1p36.33
12p12.2
7q32-q33
16p13.3
11p15.1-p14
4q21-q24
Unknown
1p32
4q35
1q31-q41
5q11.2
11p15.5-p15.4
1q32-g41
10q24
11q23.1
2q24.3
10q24-qter
11q23.1
16q13
16q13
Xq22.2
16
7q22.1
17q11.1
11p15.1-p14
1q32.1
1q32
12q24.21
11p15.1-p14
5q31.1
1p34.1
19q13.2
17p13.2
12q24.2-q24.3
12q14.1-q21.1
4q21-q23
14q11.1-q11.2
Immune response
Immune response/interferon-inducible
Metabolism/mitochondrial protein
Plasma glycoprotein/cachectic factor
Unknown
Immune response
Metabolism/interferon-inducible protein
Detoxification and drug metabolism
Immune response
Detoxification and drug metabolism
Lipid metabolism
Metabolism
Proteogycan
Metabolism
Cell growth and differentiation
Immune response/interferon-inducible
Metabolism
Detoxification and drug metabolism
Metabolism
Inflammation
Detoxification and drug metabolism
Detoxification and drug metabolism
Immune response
Blood coagulation cascade
Immune response
Developmental processes
Heme transport
Metabolism
Immune response/interferon-inducible
Detoxification and drug metabolism
Immune response (CD26)/glucose homeostasis
Detoxification and drug metabolism
Detoxification and drug metabolism
Detoxification and drug metabolism
Detoxification and drug metabolism
Serine (or cysteine) proteinase inhibitor
Metabolism/removal of ammonia
Immune response
TGF-beta-induced anti-apoptotic factor 1
Inflammation
Extracellular matrix
Immune response
Metabolism
Inflammation
Immune response
Metabolism of phospholipids and nucleic acids
Detoxification and drug metabolism
Immune response
Biosynthesis of nitric oxide/miscellaneous
Tumor-associated antigen
Detoxification and drug metabolism
Angiogenesis and metastasis
BT/N, fold change of HBV-associated HCC versus nontumorous liver; CT/N, fold change of HCV-associated HCC versus nontumorous liver.
Accession number of each gene was obtained from PubMed or the Institute for Genomic Research database.
Gene symbols used are based on the data from LocusLink.
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GENE EXPRESSION PATTERNS LINKED TO VIRUS TYPE IN HCC
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 ␤-actin. The reverse transcriptase PCR data correlated with the microarray data (P ⫽ 0.0075 and r ⫽ 0.558).
shown). Although these genes are reported to be coordinately upregulated 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 b). 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.
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 (12–14). 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 Btype 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 upregulation 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-␣
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 transductionrelated 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 upregulated 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.
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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 Method
Norio Iizuka, Masaaki Oka, Hisafumi Yamada-Okabe, et al.
Cancer Res 2002;62:3939-3944.
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