Introduction
Hepatocellular carcinoma (HCC) is one of the most
common types of malignant cancer, which is associated with a poor
prognosis. In addition, HCC is the fourth most common cause of
cancer-associated mortality worldwide (1). It was previously estimated that
39,230 new cases of HCC and 27,170 cases of HCC-associated
mortality would occur in the United States in 2016 (1). Due to the highest occurrence of HCC
in East Asia, this estimation should be further increased in this
area (2). Chronic liver diseases,
including hepatitis, fibrosis and cirrhosis, are the primary causes
of HCC. In addition, ~90% of cases of HCC develop from cirrhotic
livers (3), and HCC and cirrhosis
share numerous risk factors, including hepatitis, alcohol
consumption, obesity, diabetes, gender and advanced age (4,5).
These findings suggest that cirrhosis and HCC may share
pathophysiological similarities.
Despite the aforementioned correlations between HCC
and cirrhosis, the detailed mechanisms linking these two diseases
remain to be fully elucidated. Hepatic stellate cells (HSCs) are
primary fibrogenic cells, which are involved in cirrhosis-dependent
carcinogenesis (6). Su et
al (7) indicated that HSCs can
be activated by inflammatory signals and macrophages, which
contribute to fibrogenesis, and therefore may be considered
positive risk factors for HCC. Furthermore, it has been
demonstrated that activated HSCs can foster a conducive environment
to directly support hepatic tumorigenesis via secreting numerous
cytokines, including Wnt ligands, interleukin 6 and growth factors
(8). De Minicis et al
(9) reported that HSCs can be
activated by lipopolysaccharide produced by the intestinal
microflora via the toll-like receptor 4 signaling pathway, which
can upregulate the expression of proinflammatory chemokines or
cytokines, and facilitate the invasion and migration of HCC
(10). Increased extracellular
matrix (ECM) production is another feature of cirrhosis that
contributes to tumorigenesis (11). Previous studies have reported that
increased ECM production may promote the growth, survival and
migration of precancerous cells via integrin signaling (6,12).
Furthermore, increased ECM production may disturb cell signaling by
sequestering growth factors, including interleukin families,
fibroblast growth factor and transforming growth factor (13). This sequestration may serve a role
in the aberration of normal liver cells. However, although these
existing studies have confirmed that the development of HCC is
closely associated with cirrhosis, the detailed mechanisms remain
to be elucidated. Therefore, further investigations into how HCC
develops from cirrhosis are required.
In order to identify the epigenetic alterations and
potential role of DNA methylation markers in HCC and its prognosis,
Villanueva et al (14)
generated a methylation-based prognostic signature based on 304
samples from patients with HCC after surgical resection using a
training-validation scheme. The GSE63898 dataset contains data from
this study, which analyzed whole-genome transcriptome alterations
(14). Villanueva et al
(14) confirmed a high prevalence
of methylation deregulated genes, including Ras association
domain family member 1, APC, WNT signaling pathway regulator
and insulin like growth factor 2 (IGF2), and
identified potential epidrivers, such as ephrin B2 and
septin 9; however, the mechanisms underlying the development
of HCC from cirrhosis were not determined. In order to investigate
the pathogenesis of HCC, the GSE63898 dataset was used to analyze
differences between HCC and cirrhotic liver tissues. The present
study aimed to identify potential target molecules, which may aid
HCC clinical treatment.
Materials and methods
Data acquisition
The GSE63898 expression profile (14), which contained data from HCC and
liver cirrhosis samples, was downloaded from the Gene Expression
Omnibus (www.ncbi.nlm.nih.gov/geo/). The samples were sequenced
using the Affymetrix Human Genome U219 Array [HG-U219] platform. In
this profile, a total of 396 liver tissue samples were collected,
including 228 HCC tissue samples and 168 non-tumor liver adjacent
cirrhotic tissue samples. The research was authorized by the
Institutional Review Boards of the participating centers.
Data preprocessing
The downloaded raw data were preprocessed using the
Robust Multi-array Average method (15,16)
in the Affy package of R (17),
including background correction, normalization and expression
calculation. Gene expression was presented as the mean value of
different probes.
Differentially expressed genes (DEGs)
screening
DEGs between HCC samples and cirrhotic liver samples
were screened by Student's t-test in the Linear Models for
Microarray Data package (18). The
P-value obtained from the Student's t-test was adjusted according
to the Benjamini and Hochberg method (19) and the obtained adjusted P<0.01
was set as the threshold.
Functional and pathway enrichment
analyses
Gene Ontology (GO) is a free database used in
biological process (BP), molecular function and cellular component
analyses (20). Kyoto Encyclopedia
of Genes and Genomes (KEGG) is also a free database specifically
used in pathway analysis (21).
The Database for Annotation, Visualization and Integrated Discovery
(DAVID) is a common online tool used in large-scale gene functional
analyses (22). Therefore, GO
functional and KEGG pathway enrichment analyses of DEGs were
conducted using DAVID with the criterion of P<0.05.
Protein-protein interaction (PPI)
network construction and analysis
According to the required confidence threshold set
(combined score) >0.7, PPIs were analyzed using Search Tool for
the Retrieval of Interacting Genes (23) and the PPI network was constructed
by Cytoscape (24). CytoNCA
(25), which is a plugin of
Cytoscape for PPI network evaluation, was used to perform a
topological analysis, including degree centrality (DC), betweenness
centrality (BC) and closeness centrality (CC), for hub gene
screening (26). Proteins were
defined as nodes, and PPI associations were defined as edges in the
PPI network.
Sub-module analysis
Based on the PPI network, the MCODE plugin of
Cytoscape was used to screen specific bio-functional sub-modules
(27) in the network. KEGG pathway
analysis of these modules was performed using DAVID with the
criterion of P<0.05. Proteins were defined as nodes, and PPI
associations were defined as edges in modules screened from the PPI
network.
Results
DEGs screening
Based on data preprocessing and Student's t-test, a
total of 20,568 genes were identified, and 634 DEGs were identified
between HCC samples and cirrhotic liver samples, of which 165 were
upregulated and 469 were downregulated (Fig. 1).
Functional and pathway enrichment
To investigate the biological functions of the DEGs,
GO_BP functional and KEGG pathway enrichment analyses were
conducted. GO_BPs of upregulated DEGs were significantly enriched
in cell division (P=2.91×10−10), mitotic cell cycle
(P=5.57×10−10) and mitotic nuclear division
(P=1.42×10−7), etc. (Fig.
2A). Furthermore, GO_BPs of downregulated DEGs were
significantly enriched in small molecule metabolic process
(P=1.87×10−21), xenobiotic metabolic process
(P=7.15×10−15) and immune response
(P=1.83×10−14), etc. (Fig.
2B). KEGG pathways of upregulated DEGs were significantly
enriched in cell cycle (P=1.67×10−5),
progesterone-mediated oocyte maturation (P=0.001) and p53 signaling
pathway (P=0.003), etc. (Fig. 2C).
In addition, KEGG pathways of downregulated genes were
significantly enriched in chemical carcinogenesis
(P=1.92×10−10), retinol metabolism
(P=4.53×10−9) and complement and coagulation cascades
(P=8.67×10−8), etc. (Fig.
2D).
PPI network analysis
With required confidence >0.7, the PPI network
for DEGs was constructed with 324 nodes and 915 edges (Fig. 3, Table
I), including 74 upregulated genes and 250 downregulated genes.
Downregulated Jun proto-oncogene, AP-1 transcription factor subunit
(JUN; degree, 39), Fos proto-oncogene, AP-1 transcription factor
subunit (FOS; degree, 34) and v-myc avian myelocytomatosis viral
oncogene homolog (MYC; degree, 32) had high degrees and were
therefore identified as the hub nodes in the PPI network.
 | Table I.Proteins with a degree ≥20, as
determined by topological analysis of the protein-protein
interaction network. |
Table I.
Proteins with a degree ≥20, as
determined by topological analysis of the protein-protein
interaction network.
| Gene | DC | BC | CC |
|---|
| JUN | 39 | 17,219.9 | 0.0283 |
| FOS | 34 | 19,632.8 | 0.0283 |
| MYC | 32 | 8,693.0 | 0.0282 |
| EGR1 | 31 | 7,531.4 | 0.0282 |
| CDK1 | 27 | 6,022.2 | 0.0280 |
| CDKN1A | 26 | 4,038.7 | 0.0280 |
| CXCL12 | 25 | 3,844.4 | 0.0279 |
| CYP2E1 | 22 | 2,721.3 | 0.0278 |
| CYP1A2 | 22 | 4,144.9 | 0.0276 |
| IGF1 | 21 | 6,096.5 | 0.0280 |
| CCR7 | 21 | 2,227.9 | 0.0277 |
| CYP3A4 | 21 | 1550.2 | 0.0277 |
| PTGS2 | 20 | 6,968.9 | 0.0281 |
| THBS1 | 20 | 5,690.0 | 0.0281 |
| CCL5 | 20 | 3,738.3 | 0.0280 |
| ANXA1 | 20 | 634.9 | 0.0275 |
| SAA1 | 20 | 1,475.6 | 0.0275 |
Sub-module analysis
According to the selection criteria, four
sub-modules were screened and named module 1, 2, 3 and 4 (Table II). In module 1 (Fig. 4A), there were 14 nodes with 91
edges, and according to the KEGG pathway analysis, the genes were
significantly enriched in chemokine signaling pathway
(P=3.14×10−13) and cytokine-cytokine receptor
interaction (P=5.07×10−7), etc. In particular,
downregulated C-X-C motif chemokine ligand 12 (CXCL12; degree, 13),
C-C motif chemokine receptor 7 (CCR7; degree, 13) and C-C motif
chemokine ligand 5 (CCL5; degree, 13) were the three nodes with the
highest degrees in this module. In addition, there were 7 nodes
with 21 edges identified in module 2 (Fig. 4B), and according to the KEGG
pathway analysis, the genes were significantly enriched in
complement and coagulation cascades (P=5.85×10−4) and
p53 signaling pathway (P=0.04). In particular, downregulated IGF1
(degree, 6), plasminogen (degree, 6) and IGF2 (degree, 6) were the
three nodes with the highest degrees in this module. In module 3,
there were 7 nodes with 20 edges (Fig.
4C), and according to the KEGG pathway analysis, the genes were
significantly enriched in retinol metabolism
(P=5.57×10−13) and steroid hormone biosynthesis
(P=1.11×10−5), etc. In particular, downregulated
cytochrome P450 family 1 subfamily A member 2 (degree, 6),
cytochrome P450 family 4 subfamily A member 11 (degree, 6) and
cytochrome P450 family 3 subfamily A member 4 (degree, 6) were the
three nodes with the highest degrees in this module. In addition,
there were 9 nodes with 24 edges in module 4 (Fig. 4D), and according to the KEGG
pathway analysis, the genes were significantly enriched in chemical
carcinogenesis (P=2.31×10−16) and drug metabolism
cytochrome P450 (P=8.13×10−10), etc. In particular,
downregulated cytochrome P450 family 2 subfamily E member 1
(CYP2E1; degree, 6), cytochrome P450 family 2 subfamily C member 9
(CYP2C9; degree, 6) and cytochrome P450 family 2 subfamily A member
6 (CYP2A6; degree, 5) were the three nodes with the highest degrees
in this module.
| Table II.Top 5 enriched Kyoto Encyclopedia of
Genes and Genomes terms obtained from the subnet module
analysis. |
Table II.
Top 5 enriched Kyoto Encyclopedia of
Genes and Genomes terms obtained from the subnet module
analysis.
| Term | Count | P-value | Proteins |
|---|
| Cluster 1 |
|
|
|
|
Chemokine signaling
pathway | 10 |
3.14×10−13 | CCR7, GNAI1, CXCR4,
CCL21, CXCL2 … |
|
Cytokine-cytokine receptor
interaction | 7 |
5.07×10−7 | CCR7, CXCR4, CCL21,
CXCL9, CCL19 … |
|
Rheumatoid arthritis | 3 | 0.007870 | CXCL6, CCL5,
CXCL12. |
| NF-κB
signaling pathway | 3 | 0.008048 | CCL21, CCL19,
CXCL12. |
|
Leukocyte transendothelial
migration | 3 | 0.014455 | GNAI1, CXCR4,
CXCL12. |
| Cluster 2 |
|
|
|
|
Complement and coagulation
cascades | 3 |
5.85×10−4 | A2M, SERPINE1,
PLG. |
| p53
signaling pathway | 2 | 0.037772 | SERPINE1,
IGF1. |
| Cluster 3 |
|
|
|
| Retinol
metabolism | 7 |
5.57×10−13 | CYP3A4, CYP4A11,
CYP2B6, UGT2B11, CYP26A1 … |
| Steroid
hormone biosynthesis | 4 |
1.11×10−5 | CYP3A4, UGT2B11,
CYP1A2, UGT2B7. |
|
Chemical carcinogenesis | 4 |
2.94×10−5 | CYP3A4, UGT2B11,
CYP1A2, UGT2B7. |
|
Metabolic pathways | 7 |
3.00×10−5 | CYP3A4, CYP4A11,
CYP2B6, UGT2B11, CYP26A1 … |
| Drug
metabolism cytochrome P450 | 3 |
7.30×10−4 | CYP3A4, CYP2B6,
CYP1A2. |
| Cluster 4 |
|
|
|
|
Chemical carcinogenesis | 9 |
2.31×10−16 | GSTA2, CYP3A7,
CYP2C19, CYP2C9, CYP2C18 … |
| Drug
metabolism-cytochrome P450 | 6 |
8.13×10−10 | GSTA2, CYP2C19,
CYP2C9, CYP2C8, CYP2A6 … |
|
Metabolism of xenobiotics
by | 5 |
2.30×10−7 | GSTA2, CYP2C9,
EPHX1, CYP2A6, CYP2E1. |
|
cytochrome P450 |
|
|
|
| Retinol
metabolism | 5 |
4.91×10−7 | CYP3A7, CYP2C9,
CYP2C18, CYP2C8, CYP2A6. |
|
Linoleic acid metabolism | 4 |
3.70×10−6 | CYP2C19, CYP2C9,
CYP2C8, CYP2E1. |
Discussion
Disease-specific differential gene expression
reveals potential alterations associated with disease development.
According to the analysis criteria of the present study, a total of
634 DEGs were identified in HCC versus cirrhotic tissue samples, of
which 165 were upregulated and 469 were downregulated. After GO
functional and KEGG pathway enrichment analyses, a PPI network was
constructed with 324 nodes and 915 edges. In particular, JUN, FOS
and MYC were the hub nodes in this network. Based on the PPI
network, four specific modules were identified. According to the
KEGG analysis results, module 1 was significantly enriched in
chemokine signaling pathway, module 2 was significantly enriched in
complement and coagulation cascades, module 3 was significantly
enriched in retinol metabolism, and module 4 was significantly
enriched in drug metabolism cytochrome P450.
The majority of the nodes in module 1 were
chemokines or their receptors, including CXCL12, CCR7 and CCL5.
Chemokines are cytokines that specifically respond to
proinflammatory stimuli, and are involved in the migration of
immune cells to damaged organs and are associated with HCC
development (28). Chemokines are
small molecules that can be divided into four groups (C, CC, CXC,
and CXC3C) by the motifs of their NH2 terminals
(29). CXCL12, also known as
stromal cell-derived factor 1, is produced by HSCs, biliary
epithelial cells and liver sinusoidal endothelial cells (30), and has a higher expression in
cirrhotic liver tissue and HCC (31). It is well known that the
CXCL12-CXCR4 axis serves an important role in the pathogenesis of
liver diseases, including cirrhosis (32), migration (33), invasion (34) and HCC prognosis (35). However, in the present study, a
significant downregulation was identified in HCC compared with in
cirrhosis. Furthermore, Neve Polimeno et al (36) and Shibuta et al (37) demonstrated that the expression of
CXCL12 is significantly reduced in hepatoma carcinoma cells and HCC
compared with normal controls; however, the detailed mechanism of
this reduction remains unclear. Therefore, it may be hypothesized
that the CXCL12-CXCR4 axis serves various roles in different
conditions of HCC originating from cirrhosis. CCR7 has been
reported to be associated with the clinicopathological parameters
of HCC (38). An in vitro
study indicated that the expression of CCR7 is reduced in HCC cell
lines compared with in the normal liver cell line L-02 (39). A similar result was identified in
the present study; CCR7 expression was downregulated in HCC tissue
samples compared with in cirrhotic tissue samples. Shi et al
(40) reported that CCL1, which is
exclusively expressed in non-hematopoietic stromal cells (41), may serve as a tumor suppressor by
inhibiting CCR7-associated chemotaxis in HCC. Furthermore,
overexpression of CCL21, the ligand of CCR7, in HCC cell lines
presented a potential antitumor effect in a model of HCC (42); however, the detailed function of
CCR7-CCL21 requires further investigation. CCL5 has been reported
to be associated with the inflammatory cirrhosis stages in chronic
liver disease (43). It has been
demonstrated that CCL5 downregulation is able to inhibit the
effects of the human bone marrow stromal cell line HS-5 on Huh-7
cell migration and invasion via the phosphatidylinositol
3-kinase/Akt pathway (44). These
findings suggested that CCL5 may promote the migration and invasion
of Huh-7 cells. In addition, Sadeghi et al (45) reported that CCL5 is upregulated in
the serum of patients with HCC compared with patients with
cirrhosis. Conversely, in the present study, a significant
downregulation in CCL5 was detected in HCC tissue samples compared
with in cirrhotic tissue. Therefore, it may be hypothesized that
CCL5 expression differs between tissue and serum and in
vitro and in vivo. This may be a novel opportunity for
CCL5-targeted therapy.
CYPs belong to a superfamily of enzymes that serve
important roles in the metabolism of procarcinogens, carcinogens
and drugs (46). CYP2E1, CYP2C9
and CYP2A6 were three important CYPs identified in module 4 in the
present study. CYP2E1 is often deficient in HCC cell lines
(47). A significant decrease in
CYP2E1 expression was identified in HCC samples compared with
cirrhotic tissue samples in the present study. In addition,
Kinoshita and Miyata (48)
reported that CYP2E1 is significantly downregulated in HCC liver
tissue. Wu et al (49)
reported that CYP2E1 can be downregulated by resveratrol to
attenuate diethylnitrosamine and 2-acetylaminofluorene-induced
hepatocarcinogenesis in Sprague Dawley rats, thereby suggesting
that CYP2E1 may have an inhibitory effect on HCC carcinogenesis. In
addition, hepatitis B virus (HBV)-x protein can inhibit the
expression of CYP2E1 via downregulating hepatocyte nuclear factor 4
(HNF4), resulting in the promotion of human hepatoma cell growth
(48). Taken together, these
results suggested that CYP2E1 may be a promising target for the
drug-targeted therapy of HCC. CYP2C9 has been reported to be
downregulated in HCC compared with in peri-HCC and normal control
tissues (50); this result was
similar to the findings of the present study. In addition, it has
been reported that CYP2C9 can be suppressed by
hsa-microRNA-128-3P, resulting in the invasion of HCC (51). Myung et al (52) also reported that CYP2C9 is involved
in the sensitization of liver cancer stem cells to anticancer drugs
via the signal transducer and activator of transcription 3
signaling pathway, particularly in advanced stages. However, the
detailed mechanisms of these results remain unclear; therefore,
further study is required. CYP2A6 is another important member of
the CYP family that was downregulated in module 4. Similar to
CYP2C9, CYP2A6 can be regulated by HNF4 and is involved in the
metabolism of nitrosamines and aflatoxin B1 (53). Fushiya et al (54) reported that CYP2A6 is implicated in
the metabolism of 5-fluorouracil, which is commonly used in HCC
clinical chemotherapy. In addition, a previous study indicated that
CYP2A6 was downregulated in HBV- and hepatitis C
virus-infected livers, and in HCC (55); the present study suggested that HCC
samples had a significantly lower expression of CYP2A6
compared with cirrhotic liver samples. Furthermore, a previous
study reported that CYP2A6 activity was decreased in moderate or
severe alcoholic liver diseases, but not in mild severe alcoholic
liver disease (56). Therefore, it
may be hypothesized that CYP2A6 may be negatively correlated with
the stage of HCC development. Further studies are required to
investigate the CYP2A6-associated functions and pathways in
HCC.
Although numerous DEGs with their potential
functions were identified between HCC and cirrhosis samples in an
in silico analysis, there remain limitations to the present
study. For example, in silico analysis of the obtained
results revealed that numerous chemokines and cytokines were
involved in HCC development; however, the involvement of the
identified genes in vitro remains unknown. Therefore,
further experimental verifications of these genes are required.
Furthermore, these analyses were based on subjective criteria;
therefore, some genes, which have lower degrees but important roles
in HCC carcinogenesis, may have been overlooked. Despite these
limitations, these analytical results may provide novel insights
into the mechanism of HCC originating from cirrhosis.
In conclusion, the present study identified a series
of DEGs between HCC and cirrhotic tissue samples. Based on the GO
and KEGG enrichment analyses of DEGs in the PPI network,
chemokines, such as CXCL12, CCR7, CCL5, and cytokines, such as
CYP2E1, CYP2C9, CYP2A6, were identified as two important components
in the process of HCC developing from cirrhosis as they are
associated with the regulation of inflammation, growth and the
invasion of pre-cancerous cells in the liver; thus, they may serve
crucial roles in HCC development. However, these results were
derived from bioinformatics analysis; therefore, the effects of
these genes in HCC in vivo require further verification.
Competing interests
The authors declare that they have no competing
interests.
Glossary
Abbreviations
Abbreviations:
|
HCC
|
hepatocellular carcinoma
|
|
HSCs
|
hepatic stellate cells
|
|
GO
|
Gene Ontology
|
|
BP
|
biological process
|
|
KEGG
|
Kyoto Encyclopedia of Genes and
Genomes
|
|
DC
|
degree centrality
|
|
BC
|
betweenness centrality
|
|
CC
|
closeness centrality
|
|
CCR7
|
C-C motif chemokine receptor 7
|
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