BMC Research Notes
BioMed Central
Open Access
Short Report
Genetic profile of Egyptian hepatocellular-carcinoma associated
with hepatitis C virus Genotype 4 by 15 K cDNA microarray:
Preliminary study
Abdel-Rahman N Zekri*1, Mohamed M Hafez1, Abeer A Bahnassy2,
Zeinab K Hassan1, Tarek Mansour1, Mahmoud M Kamal3 and
Hussein M Khaled4
Address: 1Virology and Immunology Unit, Cancer Biology Department, National Cancer Institute, Cairo University, 1st Kasr El-Aini st, Cairo,
Egypt, 2Tissue Culture Unite Pathology Department, National Cancer Institute, Cairo University, Cairo, Egypt, 3Clinical Pathology Department,
National Cancer Institute, Cairo University, Cairo, Egypt and 4Medical Oncology Department, National Cancer Institute, Cairo University, Cairo,
Egypt
Email: Abdel-Rahman N Zekri* - ncizekri@yahoo.com; Mohamed M Hafez - mohhafez_2000@yahoo.com;
Abeer A Bahnassy - chaya2000@hotmail.com; Zeinab K Hassan - mohhafez_2000@yahoo.com; Tarek Mansour - tarekmansour@yahoo.com;
Mahmoud M Kamal - mm.kamal@yahoo.com; Hussein M Khaled - khaled@internetegypt.com
* Corresponding author
Published: 29 October 2008
BMC Research Notes 2008, 1:106
doi:10.1186/1756-0500-1-106
Received: 12 May 2008
Accepted: 29 October 2008
This article is available from: http://www.biomedcentral.com/1756-0500/1/106
© 2008 Zekri et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Background: Hepatocellular carcinoma (HCC) is a preventable disease rather than a curable one, since there is
no well-documented effective treatment modality until now, making the molecular study of this disease
mandatory.
Findings: We studied gene expression profile of 17 Egyptian HCC patients associated with HCV genotype-4
infection by c-DNA microarray. Out of the 15,660 studied genes, 446 were differentially expressed; 180 of them
were up regulated and 134 were down regulated. Seventeen genes out of the 180 up-regulated genes are involved
in 28 different pathways. Protein phosphatase 3 (PPP3R1) is involved in 10 different pathways followed by
fibroblast growth factor receptor 1 (FGFR1), Cas-Br-M ecotropic retroviral transforming sequence b (CBLB),
spleen tyrosine kinase (SYK) involved in three pathways; bone morphogenetic protein 8a (BMP8A), laminin alpha
3 (LAMA3), cell division cycle 23 (CDC23) involved in 2 pathways and NOTCH4 which regulate Notch signaling
pathway. On the other hand, 25 out of the 134 down-regulated genes are involved in 20 different pathways.
Integrin alpha V alpha polypeptide antigen CD51 (ITGVA) is involved in 4 pathways followed by lymphotoxin alpha
(TNF superfamily, member 1) (LTA) involved in 3 pathways and alpha-2-macroglobulin (A2M), phosphorylase
kinase alpha 2-liver (PHKA2) and MAGI1 membrane associated guanylate kinase 1 (MAGI1) involved in 2
pathways. In addition, 22 genes showed significantly differential expression between HCC cases with cirrhosis and
without cirrhosis. Confirmation analysis was performed on subsets of these genes by RT-PCR, including some upregulated genes such as CDK4, Bax, NOTCH4 and some down-regulated genes such as ISGF3G, TNF, and VISA.
Conclusion: This is the first preliminary study on gene expression profile in Egyptian HCC patients associated
with HCV-Genotype-4 using the cDNA microarray. The identified genes could provide a new gate for prognostic
and diagnostic markers for HCC associated with HCV. They could also be used to identify candidate genes for
molecular target therapy.
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Background
Hepatocellular carcinoma (HCC) is one of the most
malignant tumors with a high mortality, aggressive
growth behavior and a high recurrence rate. It is the sixth
most common cancer worldwide and the third most common cause of cancer death with prevalent areas in Asia
and sub-Saharan Africa [1]. HCC usually develops following chronic liver inflammation caused by hepatitis C or B
virus [2]. Although recent studies showed increased HCC
incidence in western countries, more than 80% of cases
occurred in endemic areas due to exposure to hepatitis
viruses, mycotoxins and alcohol abuse [3]. Since HCC
progression is usually asymptomatic and results in poor
prognosis with low 5-year survival rates (12–15%), comprehensive molecular genetic studies will be important for
improving clinical management of HCC.
The major etiological factor of liver cancer is hepatitis B
virus (HBV), followed by hepatitis C virus infection
(HCV). Although HCC tissue from different individuals
has many phenotypic differences, there are some features
that unify HCC occurring in a background of viral hepatitis B and C. HCC due to HBV and/or HCV may be an indirect effect of enhanced hepatocyte turnover, which occurs
in order to replace infected cells that have been immunologically attacked. Alternatively, viral functions may play
a direct role in mediating oncogenesis [4]. In Egypt, HBV
and HCV are considered major health problems and disease prognosis may be worse in conjunction with schistosomiasis (Attia, 1998).
The development and progression of HCC are caused by
the accumulation of genetic changes resulting in altered
expression of cancer-related genes, such as oncogenes,
tumor suppressor genes, and genes involved in different
regulatory pathways [5,6]. Therefore, identification of
new molecular parameters is important for cancer
research and treatment. It is now possible to use profiling
techniques such as cDNA array to identify genes that play
important roles in human carcinogenesis [5]. Identification and monitoring of gene expression profile changes in
HCC specimens will not only explain the cause(s) of pathological changes, but will also provide opportunity to
identify novel targets for disease detection and intervention. In this study, we investigated the gene expression
profile in Egyptian patients with HCV-associated HCC.
We also evaluated the prognostic and predicative value of
these genes and the possibility of defining candidate
genes for molecular target therapy.
Methods
Patients
The study included 17 patients who attended the National
Cancer Institute (NCI), Cairo University, and were consecutively diagnosed with HCC. The clinico-pathological fea-
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tures of the studied subjects are shown in table 1. Tumors
and their adjacent non-neoplastic tissues together with
venous blood samples were obtained from patients at the
operation theatre. The study was conducted in compliance with the Helsinki Declaration and was approved by
the senior staff committee and by a board regulating nonintervention study comparable to an institutional review
board. All involved patients gave a written informed consent. Tissues were immediately cut into three parts; one
piece was processed for routine histolopathological examination to confirm diagnosis, determine the pathological
features of the tumor and assess tumor: normal ratio. The
second and third portions were immediately snap-frozen
and stored in liquid nitrogen for RNA and DNA extraction. Patients' profiles were extracted from the medical
records.
Table 1: Clinical characteristic of the studies patients
s.n.
Diagnosis
Grade
CAH
cirr.
AFP
HCV
HBV
1
HCC
II
III
yes
14167
POS
NEG
2
HCC
III
III
yes
13.7
POS
NEG
3
HCC
II
No
yes
67
POS
NEG
4
HCC
II
No
no
32
POS
NEG
5
HCC
II
II
no
997
POS
NEG
6
HCC
II
III
yes
105.8
POS
NEG
7
HCC
II
III
yes
2322
POS
NEG
8
HCC
I
III
yes
63
POS
NEG
9
HCC
II
No
no
2.8
POS
NEG
10
HCC
III
No
Yes
1136
POS
NEG
11
HCC
II
No
No
107
POS
NEG
12
HCC
II
No
No
137
POS
NEG
13
HCC
I
No
no
37.5
POS
NEG
14
HCC
II
III
Yes
60
POS
NEG
15
HCC
II
No
Yes
6.3
POS
NEG
16
HCC
III
III
Yes
600
POS
NEG
17
HCC
II
III
no
2.7
POS
NEG
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Serological markers
Serological markers for HBV infection ([HBsAg and antibodies to hepatitis B core antigen [anti-HBc]) were
detected with current standard assays (enzyme immunoassay [EIA]; Innogenetics, Belgium). A sample was considered HBV positive if it was positive for HBsAg or antiHBc antibodies, or both. Antibodies to HCV were detected
with HCV EIA version 3.0 (Innogenetics, Belgium). All
serologic assays were done according to manufacturer's
instructions.
Detection of HCV-RNA
RNA was extracted from patients' sera according to manufacturer's instructions by Qiagen (Germany). The RT-PCR
was performed as previously described by Zekri et al. [7].
Detection of HBV DNA
DNA was extracted from frozen liver tissues of each
patient according to the standard protocol of Mahoney
(1996). All DNA extracts were analyzed for HBV genomes
with three different polymerase chain reaction (PCR)
assays to detect the S, X and core genes as previously
described [8] to exclude occult HBV infection.
cDNA Microarrays
RNA extraction from tissues: RNA was prepared from
tumor samples and their adjacent non-neoplastic tissues.
Each sample was tested in triplicate on array 15K (ArrayI) supplied from Fox Chase Cancer Center http://
www.fcc.edu/rsearch/facilities/biotechnology/
DNAMicroarray.htm. Briefly, RNA was extracted by
homogenization (Polytron; Kinematica, Lucerne, Switzerland) in TRIzol reagent (Gibco BRL) at maximum speed
for 90–120s. The homogenate was incubated for 5 min at
room temperature. A 1:5 volume of chloroform was
added, and the tube was vortexes and subjected to centrifugation at 12,000 g for 15 min. The aqueous phase was
isolated, and one-half of the volume of isopropanol was
added to precipitate the RNA. Purification was then performed with the Qiagen RNeasy Total RNA isolation kit
according to manufacturer's specifications (Qiagen, Germany). The purified total RNA was finally eluted in 10 ul
of diethyl pyrocarbonate-treated H2O, and the quantity
and integrity were characterized using a UV spectrophotometer (Nanodrop). RNA was electrophoresed on an
ethidium bromide stained agarose gel. It showed discrete
bands of high molecular weight RNA between 7 Kb and
15 Kb, two predominant ribosomal RNA bands at approximately 5 Kb (28S) 2 Kb (18S), and low molecular weight
RNA between 0.1 and 0.3 Kb (tRNA, 5S). The isolated
RNA has an A 260/280 ratio of 1.9–2.1.
RNA Labeling
Probes for microarray analysis were prepared from RNA
templates by the synthesis of first strand cDNA containing
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amino-allyl-labeled nucleotides (Sigma Cat # A0410), followed by a covalent coupling labeled cDNA to the Cy Dye
Ester to the NHSester of the appropriate Cyanine fluor,
Cy3-ester (Amersham Pharmacia, Cat# PA23001) and
Cy5-ester (Amersham Pharmacia, Cat# PA25001). This
was followed by purification of the two probes by passing
through a Microcon 30 columns (Millipore, Bedford, MA)
according to the manufacturer's instructions.
Hybridization
Hybridization occurred in 1× hybridization buffer containing 50% formamide, 5 × SSC, and 0.1% SDS. Prior to
hybridization, the free amino groups on the slide were
blocked or inactivated in the pre-hybridization solution
containing 1% bovine serum albumin (BSA; Sigma Cat#
A-9418), 5 × SSC and 0.1% SDS.
Data Collection
Primary data from image files were obtained using Scan
Array Express II (Perkin Elmer, USA), a confocal laser
scanner capable of interrogating both the Cy3- and Cy5labeled probes and producing separate images for each
and then normalized using intensity and spatially
dependent method, as previously described [9].
Following image processing, the data generated from the
arrayed genes were further analyzed before differentially
expressed genes could be identified. The first step in this
process was the normalization of the relative fluorescence
intensities in each of the two scanned channels. We calculated the normalization factors for each step of the experiment as follows: First, we used total measured
fluorescence intensity in order to make the total mass of
RNA labeled with either Cy3 or Cy5 equal. The total integrated intensity across all the spots in the array must be
equal for both channels. Second, we used the scatter plot
of Cy5/Cy3 of genes. (The scatter plot of Cy5/Cy3 of all
genes is statistically examined). The scatter plots of the
values of Cy3 and Cy5 fluorescent signals also revealed a
pattern of distribution and were clustered in a diagonal
line. A high correlation was observed in all samples and
showed that there was a high reliability in the experiments
by the cDNA microarray analysis of these samples. Third,
we used some subsets of housekeeping genes that had
already existed on each microarray chip. The ratio of
measured Cy5 to Cy3 for these genes was modeled, and
the mean of the ratio was adjusted to 1.
We applied the hierarchical clustering method to both
genes and samples by using the Pearson r test as the measure of similarity and average linkage clustering as
described previously [10]. To obtain reproducible clusters, we used only selected genes that passed the cut-off filter. The analysis was performed using a web-available
software ("Cluster" and "Tree View") and confirmed by
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Genesis software, a tool that uses k-means clustering function and that was presented as a gift by Dr. Alexander
Sturn, Graz University of Technology, Graz, Austria. We
applied a hierarchical clustering algorithm to all of the
selected genes. Information about genes participating in
different function were obtained from Onto-Express Soft
http://vortex.cs.wayne.edu. as a gift. Information about
genes participating in known signaling pathways was
derived from Entrez Gene http://www.ncbi.nlm.nih.gov/
entrez/query.fcgi?db=gene and KEGG pathway http://
www.genome.jp/kegg/pathway.html databases. To identify members of particular pathways, we combined the
KEGG gene number with the identifier/accession number.
Validation of the micrroarray results
PCR amplification of the studied genes
The genes selected from tables including 5 and 6 for the
validation study were NOTCH4, VISA, CDK-7, ISGF3G,
TNFα, and BAX genes (table 2) in which these genes were
correlated to the cell cycle regulation pathways. The RTPCR and quantification were performed in a 50 μl reaction volume. All samples were analyzed twice by the RTPCR on different days with different RT-PCR mix to ensure
reproducibility of results. Ten samples of human DNA
and RNA were extracted from PBL and normal liver tissue
used to optimize the best conditions for the multiplex
PCR of B-actin gene versus each of the studied genes.
Quantification of the studied genes were performed as
previously described by Zekri et al. 2000
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Statistical Analysis
The results were analyzed using Graph pad prism computer program (Graph pad software, San Diego, USA). For
gene expression analysis, Mann Witney Test was used for
numeric variables, and Chi square or Fisher's exact Test
was used to analyze categorical variables. The p value was
considered significant when P ≤ 0.05. We used Scan Array
Express II (Perkin Elmer, USA) software for image processing. This software uses a threshold algorithm that separates spots from the background, allowing a grid to be laid
across the spots. Having found a grid, spots are found
within each grid element. The local background is calculated, and the background is subtracted. The integrated
intensities were calculated for both the Cy3 and Cy5 channels. Measured intensities were analyzed using the Genesis software and R program that detect the up- and downregulated genes according to the ratio in their software's.
Results
In order to separate genes that are truly differentially
expressed from those influenced by random changes, we
conducted three independent microarray assays starting
from independent mRNA isolations and defined differential expression based on their consensus. Three experiments were independently performed with each sample
from the seventeen different HCC patients. When the triplicate experiments were compared, a percentage of reproducibility ranging from 67 to 90% was observed. Our
results indicated a global reproducibility of results, with
some discrepancies in the genes expressed. We therefore
decided to perform the different analyses, taking into
account all three experiments. Accordingly, 446 (Addi-
Table 2: Primer Sequences of the Studied Genes
Gene name
Primer sequence
Annealing temp.
B-actin
s:5'-ACA CTG TGC CCA ACG AGG-3'
as:5'-AGG GGC CGG TCA TAC T-3'
55–59
NOTCH4
s:5'-GAG GAC AGC ATT GGT CTC AAG G-3'
as:5'-CAA CTC CAT CCT CAT CAA CTT CTG-3'
60.4
VISA
s:5'-TGC CGT TTG CTG AAG ACA A-3'
as:5'-TTC GTC CGC GAG ATC AAC T-3'
56.8
CDK-7
s:5'-CGG GCT TTA CGG CGC CGG ATG G-3'
as:5'-CCC TCA GTA GTA AAA TGT TGT CC-3'
60
ISGF3G
s:5'-CTG GCA CAT GGC ACA CAC-3'
as:5'-CAT CAA AGC GAC AGC ACA GT-3'
59
TNF-α
s:5'-ACA AGC CTG TAG CCC ATG TT-3'
as:5'-AAA GTA GAC CTG CCC AGA CT-3'
57.9
BAX
s:5'-CAT GGA ACT GAT GAT GAT GAA-3'
as:5'-CTC CAA CGA AAA ATG ATA-3'
60
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tional file 1) genes of known function were differentially
expressed in our HCC cases out of the 15,645 studied
genes.
Of the 446 genes, 180 showed up-regulation (additional
file 1), 98 are involved in biological processes (table 3),
145 in molecular function (table 4), 98 in cellular components and seventeen are involved in 28 different pathways
(table 5). The most frequent genes are protein phosphatase 3 (PPP3R1), which are involved in 10 different
pathways, fibroblast growth factor receptor 1 (FGFR1),
Cas-Br-M ecotropic retroviral transforming sequence b
(CBLB) and spleen tyrosine kinase (SYK) in three pathways' and bone morphogenetic protein 8a (BMP8A), laminin alpha 3 (LAMA3), cell division cycle 23 (CDC23) in
2 pathways and NOTCH4 which regulate the Notch signaling pathway.
Down-regulation of 134 genes was reported (additional
file 1). 44 genes are involved in biological processes (table
6). The most characteristic genes were related to virus
response like virus-induced signaling adapter (VISA),
interferon-stimulated transcription factor 3 (ISGF3G),
Gardner-Rasheed feline sarcoma viral (v-fgr) oncogene
homolog (FGR). Eighty-three genes are involved in
molecular function (table 7) mainly alpha-2 macroglobu-
lin and protease serine12 (RPSS12), 60 genes in cellular
component and 25 are involved in 20 different pathways
(table 8). The most frequent one is the integrin alpha V
alpha polypeptide antigen CD51 (ITGVA) in 4 pathways
followed by lymphotoxin alpha (TNF superfamily, member 1) (LTA) in 3 pathways and alpha-2-macroglobulin
(A2M), phosphorylase kinase alpha 2-liver (PHKA2) and
MAGI1 membrane associated guanylate kinase 1
(MAGI1) in 2 pathways.
Out of these 446 genes, which were differentially
expressed, only 10 showed significant differences between
cases with high (≥ 600 IU/ml) and low (≤ 200 IU/ml) AFP
levels (figure 1). One of them is SMAD6, which is
involved in the FGF-β signaling pathway. Eighteen genes,
including CDC3 and LAMA3 which are involved in the
same pathways, showed significant differences between
HCC cases with either cirrhosis or chronic active hepatitis
(figure 2), whereas 21 genes, including FGFR1 and LAMA
3, showed significant differences between HCC with cirrhosis and HCC without cirrhosis. This is considered a
unique phenomenon in Egyptian HCC patients (figure 3).
FGFR1 is involved in adherense junction, regulation of
actin cytoskeleton and MAPK, whereas LAMA 3 is
involved in ECM receptor interaction and focal adhesion.
Using this approach, we identified many genes, which are
Table 3: Genes involved in the Biological Process of the up-regulated genes
Biological Process
Gene No. Gene Name
Transcription
22
PWP1, ZNF202, SIN3A, CRSP3, SUD33, CDK7, BCOR, RNF2, ZNF77, SND1,
SOX30, PHD finger protein 3, NOTCH4, CBX3, MNAT1, MAF, RXRA, TEAD3,
SMAD6, MYC Binding protein 2 and SP11O
Regulation of transcription, DNA-dependent
17
SMAD6, CBX3, RNF2, SND1, ARNT, SPEN, TEAD3, PHF3, LOC643641,
MYCBP2, SP110, SUDS3, ZNF77, ZNF202, RXRA, MAF
Biological process unknown
9
FETUB, UBQLN4, SURF5, PRCC, HSD17B1, ZNF77, BTBD1, SIPAILI, RXR4
Proteolysis
8
TMPRSS4, ADAMTS7, MMP23B, AZU1, PGM5P1, SPPL2B, ICEBERFG (caspase-1
inhibitor), GZMK
Protein amino acid phosphorylation
7
MAP4K4, CDK7, DYRK2, SYK, FGFR1, MARK4, RIPK2 (down regulate TLR 2/3/4,
IL1 and IL8 receptor
Transcription from RNA polymerase II Promotor
6
TCEA1, TEAD3, MNAT1, SOX30, CRSP3
Intracellular signaling cascade
6
SH2B3, MCF2L, SYK, Rho GTPase activating protein, CAPS, CHN1
Ubiquity cycle
6
OTUB2, MYCBP2, CBLB, FBO31, CDC23, VPS8
Signal transduction
6
ARNT, RXRA, GAS6, TNFSF13B, ADORA1, RIPK2
Cell proliferation
6
CDK7, MNAT1, SYK, CSE1L, GAS6, TNFSF13B
Metabolism
5
QDPR, ATP2C1, HSD17B1, GSTM4, ECHS1
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Table 4: Genes involved in the molecular function of the up regulated genes
Molecular Fuction
Gene No. Gene Name
Protein Binding
28
MICA, SIPA1L1, SIN3A, VPS8, BTBD1, FBLN2, SUDS3, SYK, ARNT, RNF2, MYRIP, RIPK2, CBLB,
SURF5, PHF3, NOTCH4, TNKS, LSM2(Bac-Bax), PSCD1, CBX3, MNAT1, RXPAP, FGFR1,
ICEBERG(caspase-1-inhibitor), POP7, SMAD6, MYCBP2(MYC binding protein-2), SP110
Nucleotide binding
16
ABCG2, TNRC6B, SYK, MAP4K4, ATP6VIA, SPEN, MARK4, CUGBP2, CDK7, RAB14, MATR3,
ATP2C1, FGFR1, RBM25, DYRK2, RIPK2
Metal ion binding
16
CHN1, RNF2, ADAMTS7, TCEA1, ATP6V1A, PLOD1, VPS8, PHF3, MYCBP2, MNAT1, MATR3,
SP110, ZNF77, MYRIP, ZNF202, RXRA
Zinc ion binding
16
CHN1, RNF2, ADAMTS7, TCEA1, CBLB, VPS8, PHF3, MYCBP2, MNAT1, MATR3, SP110, ZNF77,
MYRIP, ZNF202, MMP23B, RXPA
ATP binding
11
ABCG2, SKY, MAP4K4, ATP6V1A, MARK4, CDK4, DHX36, ATP2C1, FGFR1, DYRK2, RIPK2
Transferase activity
11
SYK, MAP4K4, HS2ST1, LYPLA3, MARK4, CDK7, FGFR1, DYRK2, RIPK2, GSTM4, TKT
Molecular Function unknown
10
WFDC1, FETUB, GPM6B, UBQLN4, PHF3, SURF5, PRCC, ZNF77, SIPA1L1, RXRA
Nucleic acid binding
9
EIF5A2, SND1, SPEN, POP7, LOC643641, MATR3, DHX36, RBM25, ZNF202
Calcium ion binding
9
GAS6, RCN1, CBLB, PPP3R1, FBLN2, ATP2C1, TKT, NOTCH4, CAPS
Hydrolase activity
7
LYPLA3, ITPA, TATDN2, POP7, DHX36, ATP6VIA, ATP2C1
DNA binding
6
SP110, TCEA1, SOX30, RNF2, ZNF77, SPEN
RNA binding
6
FNBP1, AKAP1, MATR3, LSM2, SPEN, CUGBP2
potentially implicated in HCC. These genes were not previously identified by conventional cDNA microarray
assays (see additional file 1).
Confirmation of the array results was performed by RTPCR in which limited studies were done on these genes
and the Bax gene was chosen from our previous published
data (not included). The relative value for each gene was
calculated in relation to normal pooled liver tissues level.
Then we classified cases according to the level of expression into those with reduced expression and those with
over-expression for each gene and patient. Accordingly,
genes were classified into two groups: group I included
genes showing reduced expression [ISGF3G (76%), TNFα(88%), VISA (82%)], and group II included genes showing over-expression [CDK7 (70%), NOTCH4, (64%,),
BAX (78%)] (table 9).
Discussion
The rising trend of HCC incidence has been associated
with increased prevalence of HCV infection though the
fundamental mechanism(s) by which HCV is related to
HCC is (are) not definitely known [11]. Recent progress in
molecular biology has improved our understanding of the
genesis of a wide range of human neoplasms. In HCC,
several groups have reported microarray-based profiling
data and illustrated that genes with altered expression in
most of HCCs may serve as molecular diagnostic markers
and candidates of HCC-therapeutic targets or may play
causal roles in hepatocarcinogenesis.
In this study, out of the 17 samples with HCV-associated
HCCs and their adjacent normal tissues, 446 genes of
known function were differentially expressed out of the
15,645 studied genes. Out of these, 180 showed up-regulation and 134 showed down-regulation.
We compared the HCC up- and down-regulated genes
identified by the cDNA microarray assays in accordance
with their potential molecular functions, implicated in
biological processes and sub-cellular localization. Up-regulated genes had a strong association with the regulation
of cell cycle progression, transcription, nucleic acid
metabolism, and protein metabolism. The HCC downregulated genes, on the other hand, tended to be related to
the loss of the normal physiological function of the hepatocytes. They also tended to be related to the impairment
in cellular defense activities, in maintaining cell ion
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Pathway Name
Gene Symbole
1 KEGG
Natural killer cell mediated cytotoxicity
PPP3R1, MICA, SYK,
2 KEGG
B cell receptor signaling pathway
PPP3R1, SYK
3 KEGG
TGF-beta signaling pathway
SMAD6, BMP8A
4 KEGG
T cell receptor signaling pathway
PPP3R1, CBLB
5 KEGG
Cell cycle
CDC33, CDK7
6 KEGG
MAPK signaling pathway
TGFR1, PPP3R1, MAP4K4
7 KEGG
Ubiquitin mediated proteolysis
CDC23
8 KEGG
Notch signaling pathway
NOTCH4
9 KEGG
Hedgehog signaling pathway
BMP8A
10 KEGG
Long-term potentiation
PPP3R1
11 KEGG
Adipocytokine signaling pathway
RXRA
12 KEGG
Complement and coagulation cascades
SERPINA5
13 KEGG
VEGF signaling pathway
PPP3R1
14 KEGG
Fc epsilon RI signaling pathway
SYK
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Rank Database Name
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BMC Research Notes 2008, 1:106
Table 5: Up regulated gene symbols and related pathway.
Adherens junction
FGFR1
16 KEGG
Apoptosis
PPP3R1
17 KEGG
Antigen processing and presentation
TAPBP
18 KEGG
ECM-receptor interaction
LAMA3
19 KEGG
Tight junction
EPB41L2
20 KEGG
Axon guidance
PPP3R1
21 KEGG
Insulin signaling pathway
CBLB
22 KEGG
Wnt signaling pathway
PPP3R1
23 KEGG
Jak-STAT signaling pathway
CBLB
24 KEGG
Calcium signaling pathway
PPP3R1
25 KEGG
Focal adhesion
LAMA3
26 KEGG
Regulation of actin cytoskeleton
FGFR1
27 KEGG
Cytokine-cytokine receptor interaction
TNFSF13B
28 KEGG
Neuroactive ligand-receptor interaction
ADORA1
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15 KEGG
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Table 5: Up regulated gene symbols and related pathway. (Continued)
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Table 6: Genes involved in the Biological Process of the down regulated genes
Biological Process
Gene No.
Gene Name
Transcription
6
HSF4, ISGF3G, ZNF644, AVIAN, KLF6, SP4 transcription factor
Biological process unknown
6
ARPP-21, KLF6, NAT10, SLTT2, GSTA2, GLTSCR2
Regulation of transcription, DNA-dependent
6
HSF4, ZNF644, ISGF3G, VENTX, ISGF3G, AVIAN
Immune response
4
ISGF3G, AVIAN, TNF (superfamily, member1), OAS2
Protein transport
4
VPS33B1, SEC61A1, TOMM40, VPS35
Apoptosis
3
RNF34, ATG5, RAD21
Ubiquitin cycle
3
FBXO22, RNF34, USP30
Carbohydrate metabolism
3
PHKA2, MGAT4B, TALDO1
Transcription from RNA polymerase II Promotor
3
ISGF3G, PTTG1, AVIAN
Cell adhesion
3
NRXN2, MAGI1I, TGAV
Response to virus
3
VISA, ISGF3G, FGR
Table 7: Genes involved in the molecular function of the down regulated genes
Molecular Function
Gene No. Gene Name
Protein Binding
14
PRPF6, SPINK1, RAD21, NRXN2, RNF34, CORO2A, TALD01, PTTG1, NAGK, VPS35, ISGF3G,
TTGAV, HOMER3, DMXL1,
Transferase activity
9
NDST2, GSTA2, OAS2, FGR, TALDO1, NAGK, FTCD, NAT10, MAGI1,
Metal ion binding
9
ZSWIM1, THAP4, RNF34, ZNF644, ZC3H7B, ISGF3G, KLF6, IMPDH2, SP4
Zinc ion binding
8
ZC3H7B, ZNF644, KLF6, ISGF3G, RNF34, THAP4, SP4, ZSWIM1
Nucleotide binding
8
NAT10, MYH8, MAGI1, NAGK, APRL1, FGR, A2BP1, CUGBP2
Molecular Function unknown
8
ARPP-21, C9ORF156, KLF6, NAT10, PTTG1, TMEPA1, GLSCR2, TOMM40
Calcium ion binding
7
CANX, EFCAB2, FGEQ, THBD, ITGAV, FGG, SLIT2
ATP binding
7
NAT10, MGC16169, MYH8, MAGI1, NAGK, FGR, OAS2
Transcription factor activity
5
HSF4, VENTX, ISGF3G, PTTG1, AVIAN
Nucleic acid binding
4
ZC3H7B, ARPP-21, KLF6, A2BP1
DNA binding
4
ZNF644, KLF6, THAP4, SP4
Other important genes interleukin-8 binding protein, alph-2 macroglobulin and RPSSS12
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Table 8: Down regulated gene symbols and related pathway.
Rank Pathway Name
Gene Symbol
1 Complement and coagulation cascades
FGG, THBD, A2M, SERPINA5
2 Antigen processing and presentation
LTA, CANX
3 Dentatorubropallidoluysian atrophy (DRPLA)
MAGI1
4 Amyotrophic lateral sclerosis (ALS)
SLC1A2
5 Alzheimer's disease
A2M
6 Cell adhesion molecules (CAMs)
NRXN2, ITGAV
7 Regulation of autophagy
ATG5
8 SNARE interactions in vesicular transport
C1orf142
9 Type I diabetes mellitus
LTA
10 ECM-receptor interaction
ITGAV
11 Cell cycle
PTTTG1
12 Tight junction
MAGI1
13 Axon guidance
SLIT2
14 Insulin signaling pathway
PHKA2
15 Jak-STAT signaling pathway
ISGF3G
16 Calcium signaling pathway
PHKA2
17 Focal adhesion
ITGAV
18 Regulation of actin cytoskeleton
ITGAV
19 Cytokine-cytokine receptor interaction
LTA
20 MAPK signaling pathway
MAPK8IP3
homeostasis and in cellular responses to extrinsic stresses,
including wounding and external biotic stimuli. In addition, genes related to cell responses to external growth
stimuli and signal transduction and related to cell morphogenesis and biogenesis were more frequently found in
the HCC up-regulated genes. On the other hand, the HCC
up-regulated genes had more genes with their products
distributed in cell nucleus, while the HCC down-regulated
genes had more genes of secreted proteins.
Among the up-regulated genes, we focused on LAMA3 as
a possible molecular target for HCC therapy. Ln-5 is a het-
erotrimeric glycosylated protein formed by α3, β3, γ2
chains assembled with disulfide bonds that are the product of three different genes (LAMA3, LAMB3, and
LAMC2), respectively [12]. It is a main component of the
BM structure, where it promotes different functions such
as adhesion or migration. Therefore, a number of reports
suggest its involvement in the spread and metastasis of
cancer cells [[13-15] and [16]]. Ln-5 is widely expressed in
the human body; however, no study has yet investigated
the expression of Ln-5 in the liver under pathological conditions such as HCC or the expression of all of the chains
in cancer tissues in the same patient.
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Figure 1
Differentially
expressed genes in HCC which show a significant difference between high and low AFP
Differentially expressed genes in HCC which show a significant difference between high and low AFP.
Our study showed up regulation of protein phosphatase 3
receptor gene (PP3R) in most of the HCC cases. To our
knowledge, this is the first time to detect this gene in the
HCC cases. PPP3 (formerly PP2B, Calcineurin) is a serine/
threonine protein phosphatase. Some studies showed that
the PPP3RL gene is localized on human chromosome
9q22, and transcripts of PPP3RL gene are specifically
expressed in the testis [17].
Transforming growth factor-b1 (TGF-b1) is an important
growth regulatory molecule that triggers apoptosis in
hepatocytes [18]. HCC is resistant to TGF-b1 even though
the latter is transcriptionally up-regulated in tumor cells
and is commonly elevated in the sera of HCC patients
[19,20]. TGF-b1 may thus promote tumor growth, in part
by killing or inhibiting the growth of surrounding hepatocytes.
Some data suggest that the resistance of HCC to TGF-b1 is
associated with mutation and loss of the receptor that
mediates TGF-b1 signaling [21], although this has not
been consistently observed [22]. Alternatively, HBxAg
may suppress the expression of TGF-b1 type II receptor
[23] or up-regulates the TGF-b1 gene by HBxAg [24],
probably by the constitutive activation of transcriptional
complexes containing Smad4 [25], which mediates TGFb1 signaling. TGF-b1 signaling also promotes the development of fibrosis and cirrhosis in patients with chronic
liver disease (CLD) [26]. Some studies showed an
increased expression of TGFa, aFGF and HGF and their
respective receptors during hepatocarcinogenesis [27].
Among them, TGFa and aFGF appear to be the major
growth factors produced by tumor cells and may therefore
be the main contributors to the progression of HCC. This
was also reported in our study.
Similarly, Ogasawara et al., 1996; Tsou et al., 1998 have
reported over-expression of the fibroblast growth factor
receptor (FGFR1) in HCC in relation to the proliferation
of cancerous cells. FGF has been considered to contribute
to various human tumors and malignant growth of neoplasm. Hepatocellular carcinoma (HCC) is a typical
hypervascular tumor, and it is suggested that FGF may be
involved in hepatocarcinogenesis [28,29].
The spleen tyrosine kinase (SYK) is a tumor/metastasis
suppressor gene recently found to be silenced through
DNA methylation in breast cancer and T-lineage acute
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Figure 2
Differentially
expressed genes in HCC which show a significant difference between presences of either cirrhosis or CAH
Differentially expressed genes in HCC which show a significant difference between presences of either cirrhosis or CAH.
lymphoblastic leukemia. Loss of SYK expression has been
implicated in increased invasiveness and proliferation of
breast tumors [30]. Our data regarding the expression of
SYK agree with the few available reports in this context
where methylation andloss of SYK expression in HCC
neoplastic tissues were found to be independent biomarkers of poor patient outcome [31].
Notch1 signaling may participate in the development of
HCC cells by affecting multiple pathways that control
both cell proliferation and apoptosis. In this study, we
record up-regulation of NOTCH4 gene, which regulates
Notch signaling pathway. This has also been reported by
Runzi et al. 2003. Notch1 signaling-induced growth suppression is at least partially due to G0/G1 cell cycle arrest.
Up-regulation of p53 expression and down-regulation of
Bcl-2 may be related to Notch1 signaling-induced apopto-
sis. Therefore, Notch1 signaling can inhibit HCC growth
through the induction of the cell cycle arrest and apoptosis [32].
IFN-β promoter stimulator (IPS)-1, also known as mitochondrial antiviral signaling protein (MAVS), virusinduced signaling adaptor (VISA), and CARD adaptor
inducing IFN-β (Cardif), was recently identified as an
adaptor linking RIG-I and Mda5 to the downstream signaling molecules. IPS-1 contains the CARD-like domain
that is responsible for the interaction with that of RIG-I
and Mda5. In addition, IPS-1 contains a transmembrane
region that targets this protein to the mitochondrial outer
membrane. IPS-1-deficient mice showed severe defects in
both RIG-I- and Mda5-mediated induction of type I interferon and inflammatory cytokines and were susceptible to
RNA virus infection. RNA virus-induced interferon regula-
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Figure
Differentially
3
expressed genes in HCC which show a significant difference between presences of HCV with cirrhosis in array I
Differentially expressed genes in HCC which show a significant difference between presences of HCV with cirrhosis in array I.
tory factor-3 and nuclear factor KB activation was also
impaired in IPS-1-deficient cells. IPS-1, however, was not
essential for the responses to either a DNA virus or a double-stranded B-DNA. Thus, IPS-1 is the sole adapter in
both RIG-I and Mda5 signaling that mediates effective
responses against a variety of RNA viruses. The Virusinduced signaling adaptor (VISA) is essential for host
innate immune responses against double-stranded RNA
viral infection and viral replication. Down-regulation of
this gene in our HCC cases may be related to the replication of the hepatitis virus. It is an adaptor that activates the
transcription of the nuclear factor κB (NF-κB) and interferon regulatory factor 3 (IRF3), which regulate the
expression of type I interferon.
The expression of IL-8 in human HCC has more relevance
to metastasis than to angiogenesis or cell proliferation.
The expression of IL-8 did not significantly correlate with
micro-vessel count in HCC tissues, but the incidence of
microscopic vessel invasion was significantly higher in IL8-positive than in IL-8-negative tissues. In this study, we
found decreases in the expression of IL-8 binding protein
which leads to the increase in the expression of IL-8. More
IL-8 was expressed in HCCs at pathologic stage III/IV than
in those at stage I/II [33].
A novel human malignancy-associated gene (MAG)
expressed in various malignant tumors including glioblastomas and HCCs and in tumor pre-existing conditions
such as hepatitis C virus- and hepatitis B virus-induced
liver cirrhosis. This novel gene may play a role in the progression of premalignant conditions and in the development of HCC and other cancers [34].
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Table 9: The correlation between the expression level of the studied genes and clinicopathological features of hepatocellular
carcinoma cases
Reduced expression
Variable
Total: 17 (%)
Over expression
ISGF3G
13 (76)
TNF-α
15 (88)
VISA*
14 (82)
CDK-7
12 (70)
NOTCH4
11 (64)
BAX
13 (76)
Age (Mean ± SD)
(57 ± 10.2)
< 57 = 10(59)
> 57 = 7(41)
8(80)
5(71)
9(90)
6(85)
8(80)
6(85)
7(70)
5(71)
7 (70)
4 (57)
8(80)
5(71)
Gender
Male:13(76)
Female:4(24)
10(77)
3(75)
12(92)
3(75)
11(85)
3(75)
10(77)
2(50)
9(69)
2(50)
11(85)
2(50)
Tumor site
Rirht = 11(65)
Left = 6(35)
9(82)
4(66)
10(90)
5(83)
8(73)
6(100)
7(64)
5(83)
6(55)
5(83)
9(82)
4(66)
Tumor size
≤ 8 = 12(70)
> 8 = 5(30)
10(83)
3(60)
10(83)
5(100)
11(91)
3(60)
9(75)
3(60)
8(66)
5(100)
9 (75)
4 (80)
Grade
I+II = 14(82)
III = 3(18)
10(71)
3(100)
13(93)
2(66)
12(85)
2(66)
10(71)
2(66)
8(57)
3(100)
10(71)
3(100)
Safety margin
Pos 4 (24)
Neg 13(76)
2(50)
11(85)
3(75)
12(92)
4(100)
10(77)
3(75)
9(69)
3(75)
8(61)
3(75)
10(77)
Invasion
Pos 9 (53)
Neg 8(47)
7(77)
6(75)
8(88)
7(87)
8(88)
6(75)
8(88)
4(50)
9(100)
2(25)
6(66)
7(87)
Cirrhosis
Present: 10(59)
Absent: 7(41)
8(80)
5(71)
9(90)
6(85)
8(80)
6(85)
7(70)
5(71)
7(70)
4(57)
9(90)
4(57)
CAH
Pos: 9 (53)
Neg: 8(47)
7(77)
6(75)
8(88)
7(87)
7(77)
7(87)
6(66)
6(75)
6(66)
5(63)
8(88)
5(63)
*VISA (also known as MAVS, IPS-I or CARDIF)
The twenty-one genes, which showed significant differences between HCV-associated HCC with and without cirrhosis (figure 2 and 3), reveal a unique phenomenon in
Egyptian cases and provide new focal points for cancer
research.
We conclude that the up-regulated genes identified
through the studied expression profiles of Egyptian HCC
may shed light on the mechanisms of hepatic carcinogenesis.
To confirm our results, we checked several tumor-expressing genes to elucidate whether the identified genes correlate with previous published literature and to define a
logical relation between the genes and hepatocarcinogenesis. We identified numerous genes that have previously
been associated with liver carcinoma or other types of cancer [35,36].
Competing interests
The authors declare that they have no competing interests.
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Authors' contributions
A-RNZ conceived of the study, carried out the microarray
studies, participated in its design and coordination, Data
analysis, drafted the manuscript and coordinate the whole
work team. MMH was involved in sample collection, carried out the microarray studies, participated in the drafted
the manuscript and performed the statistical analysis. AAB
carried out the histolopathological examination. ZKH was
involved in sample collection, carried out the microarray
studies and editing the manuscript. MHK was responsible
for the patient treatment and clinical data collection. TM
coordinated the research effort. All six co-authors read and
approved the final manuscript
Additional material
http://www.biomedcentral.com/1756-0500/1/106
8.
9.
10.
11.
12.
13.
14.
Additional file 1
List of the differentially express 446 gene out of the studied 15,400 gene
studied by array-I.
Click here for file
[http://www.biomedcentral.com/content/supplementary/17560500-1-106-S1.doc]
15.
16.
Acknowledgements
We thank Prof. Dr. JR. Testa and Prof. Dr. Li, Yue-Sheng from Fox Chase
Cancer Center for supplying the microarray chips and all technical support.
We thank also Prof. Dr. Alexander Sturn for allowing us to use Genesis
Software, and Onto-Express Team, Intelligent Systems and BioInformatics
Laboratory, Dept. of Comp. Sci. Wayne State University for allowing us to
use The Ono-express software for data analysis. We also wish to thank Dr.
Hanaa M. Alam El-Din for reviewing the manuscript and Gina M Gayed for
revising the manuscript. This work is supported by the USA project BIO8-002-009, USA, by the Grant office of National Cancer Institute, Cairo
University, Cairo Egypt and also by STDF, ministry of Education Cairo,
Egypt.
17.
18.
19.
20.
21.
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