Open Access

Identification of potential key genes and pathways in hepatitis B virus‑associated hepatocellular carcinoma by bioinformatics analyses

  • Authors:
    • Xiang Zhang
    • Lingchen Wang
    • Yehong Yan
  • View Affiliations

  • Published online on: March 20, 2020     https://doi.org/10.3892/ol.2020.11470
  • Pages: 3477-3486
  • Copyright: © Zhang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Chronic hepatitis B virus (HBV) is one of the leading causes of hepatocellular carcinoma (HCC). The precise molecular mechanisms by which HBV contributes to HCC development are not fully understood. The key genes and pathways involved in the transformation of nontumor hepatic tissues into HCC tissues in patients with HBV infection are essential to guide the treatment of HBV‑associated HCC. Five datasets were collected from the Gene Expression Omnibus database to form a large cohort. Differentially expressed genes (DEGs) were identified between HCC tissues and nontumor hepatic tissues from HBV‑infected patients using the ‘limma’ package. The top 50 upregulated and top 50 downregulated DEGs in HCC vs. nontumor tissues were demonstrated in subsets by heat maps. Based on the DEGs, Gene Ontology functional and Kyoto Encyclopedia of Genes and Genomes pathways enrichment analyses were performed. Several key pathways of the up‑ and downregulated DEGs were identified and presented by protein‑protein interaction (PPI) networks. A total of 1,934 DEGs were identified. The upregulated DEGs were primarily associated with the ‘cell cycle’. Among the DEGs enriched in the ‘cell cycle’ pathway, 6 genes had a log2‑fold change >2: SFN, BUB1B, TTK, CCNB1, CDK1 and CDC20. The downregulated DEGs were primarily associated with the metabolic pathways, such as ‘carbon metabolism’, ‘glycine, serine and threonine metabolism’, ‘tryptophan metabolism’, ‘retinol metabolism’ and ‘alanine, aspartate and glutamate metabolism’. The DEGs in the ‘cell cycle’ and ‘metabolic pathways’ were presented by the PPI networks respectively. Overall, the present study provides new insights into the specific etiology of HCC and molecular mechanisms for the transformation of nontumor hepatic tissues into HCC tissues in patients with a history of HBV infection and several potential therapeutic targets for targeted therapy in these patients.

Introduction

Hepatocellular carcinoma (HCC) is a prevalent malignant liver disease (1). In most cases, viral infection contributes to the development, invasion and metastasis of HCC (2), which is a global public health problem. In particular, chronic hepatitis B virus (HBV) is one of the leading causes of HCC with an estimated 400 million individuals currently affected by chronic infection worldwide (3,4). More than 50% of HCC cases arise from chronic HBV infections (5,6). In high-prevalence areas, chronic HBV infection is estimated to account for over 80% of HCC cases (7). Moreover, patients with HBV-associated HCC have notably higher rates of metastasis and recurrence compared with those without HBV infection (8,9). Three-quarters of the world's population live in areas where there are high levels of HBV infection (10). However, the currently available antiviral agents can barely eliminate chronic HBV infection (11). HBV-associated liver diseases cause approximately 1 million deaths per year (3), driving an intensive search for curative treatment approaches (12). Han et al (13) reported that WNT family gene expression is associated with the development of HBV-associated HCC. Tian and Ou's study (14) found that chronic HBV infection could lead to chronic inflammation in the liver, which could cause normal liver cells to transform into cancer cells (15). Although the correlation between chronic HBV infection and HCC development is strong, the precise molecular mechanisms by which HBV contributes to HCC development are not fully understood (16). Therefore, a clearer understanding of the molecular mechanisms of the transformation of nontumor hepatic tissues into HCC tissues in patients with HBV infection is required to guide the treatment of HBV-associated HCC (17).

A large array of data could be analyzed, given the remarkable development of high-throughput technologies for the profiling of genome-wide methylation and expression, such as methylation microarray and MeDip-seq, and RNA-seq, and the datasets publicly available worldwide (18). Potential biomarkers and signaling pathway associated with tumor regression could be identified using bioinformatics methods.

Thus far, there are insufficient bioinformatics studies focusing on the differentially expressed genes (DEGs) between HCC tissues and nontumor tissues from HBV-infected patients based on a large sample size. In the present study, data from more datasets on the same platform were collected in order to increase the sample size. Using a large cohort, DEGs between HCC tissues and nontumor tissues were identified. Furthermore, Gene Ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the DEGs were performed. In addition, protein-protein interaction (PPI) networks were constructed based on the most enriched pathways. The results of the present study may help to identify key biomarkers for the personalized treatment of patients with HCC and a history of HBV infection, and provide further insights into tumor progression and further studies on HCC.

Materials and methods

Microarray datasets for differential expression analyses

A comprehensive search was conducted for HCC microarray datasets, including tissue samples from HBV-infected patients in the Gene Expression Omnibus (GEO) database of the National Center for Biotechnology Information (http://www.ncbi.nlm.nih.gov/geo/) website. All the data of the selected datasets [GSE17548 (19), GSE55092 (20), GSE62232 (21), GSE84044 (22) and GSE84402 (23)] were produced from the GPL570 platform. Subsequently, the raw intensity files (CEL) of the datasets were downloaded from the GEO database. The robust multiarray average method of the R package ‘affy’ (version 1.60.0; http://bioconductor.org/packages/affy/) was used to process the raw intensity files and generate a large gene expression matrix of all the selected samples from the datasets meeting the criteria (HBV positive liver tissue samples with status information) for differential expression analyses (24). The matrices for each selected dataset were also generated. In addition, we used two independent HCC datasets, The Cancer Genome Atlas (TCGA; http://portal.gdc.cancer.gov/projects/TCGA-LIHC) and GSE76427 (25), including HCC patients without HBV infection to further elucidate the specificity of the expression of target genes in HBV-associated HCC.

Analyses of DEGs

Within the large cohort, the DEGs between HCC tissues and nontumor hepatic tissues were identified using the R package ‘limma’ (version 3.38.3; http://bioconductor.org/packages/limma/), which is based on unpaired t-test (26); with the thresholds of log2-fold change >1 or <-1 and adjusted P-value <0.05. The results of the differential expression analyses were visualized with a volcano plot using the R package ‘ggplot2’ (version 3.1.0; http://bioconductor.org/packages/ggplot2/). The top 50 upregulated and top 50 downregulated DEGs were represented by heatmaps using the MeV software (version 4.9.0; http://mev.tm4.org/) in the selected datasets. The unsupervised hierarchical clustering of the selected genes and samples in the heatmaps was performed using an average linkage method using Pearson's correlation.

Enrichment analysis of GO function and KEGG pathway

GO (http://www.geneontology.org) function and KEGG (https://www.kegg.jp/) pathways enrichment analyses of the upregulated and downregulated DEGs were performed using the WEB-based GEne SeT AnaLysis Toolkit (http://www.webgestalt.org/) via a significance threshold of false discovery rate (FDR) <0.05, in order to understand the critical biological implications of the identified DEGs in HBV-positive HCC tissues.

PPI network analyses

To further understand the direct and indirect associations among the DEGs, PPI networks of the upregulated and downregulated DEGs based on the top pathways of the KEGG pathway enrichment analysis were constructed and visualized using the Search Tool for the Retrieval of Interacting Genes/Proteins (https://string-db.org) database. The aforementioned methods are summarized in Fig. 1.

Results

Selection of microarray datasets for differential expression analysis

From the GEO database of NCBI, five datasets (GSE17548, GSE55092, GSE62232, GSE84044 and GSE84402) that met the study criteria were used for differential expression analyses. Within the five datasets, 321 HBV-positive samples with valid hepatic tissue status were selected to generate the gene expression matrix; 82 of which were tumor tissues, and 239 of which were nontumor tissues (Table I). The clinical characteristics of enrolled subjects can be found in supplementary data (Table SI).

Table I.

Characteristics of the datasets used in this study.

Table I.

Characteristics of the datasets used in this study.

Samples (n)

DatasetHCCNon-tumorTissuePlatform
GSE175481011HBV hepatic tissueAffymetrix Human Genome U133 Plus 2.0 Array
GSE550924991HBV hepatic tissueAffymetrix Human Genome U133 Plus 2.0 Array
GSE62232100HBV hepatic tissueAffymetrix Human Genome U133 Plus 2.0 Array
GSE840440124HBV hepatic tissueAffymetrix Human Genome U133 Plus 2.0 Array
GSE844021313HBV hepatic tissueAffymetrix Human Genome U133 Plus 2.0 Array

[i] HCC, human hepatocellular carcinoma; HBV, hepatitis B virus.

DEGs in HCC tissues compared with nontumor hepatic tissues

The expression values of 42,901 genes among 82 HCC samples were compared with 239 nontumor hepatic samples from five GEO datasets. A total of 1934 DEGs were identified with the thresholds of fold change >1 or <-1 and adjusted P-value <0.05, including 682 upregulated genes and 1,252 downregulated genes. All DEGs are marked as red dots in the volcano plot (Fig. 2). In addition, the top 50 upregulated genes and downregulated genes are listed in Tables II and III, respectively (the top 100 up- and downregulated genes are listed in Tables SII and SIII, respectively). The heat maps of GSE17548, GSE55092 and GSE84402 demonstrate the expression of the top 100 DEGs (50 upregulated and 50 downregulated) in different subsets (GSE17548 and GSE84402 in Fig. 3; GSE55092 in Fig. S1).

Table II.

Top 50 upregulated differentially expressed genes.

Table II.

Top 50 upregulated differentially expressed genes.

Probe IDGeneFold changeAdjusted P-value
206239_s_atSPINK14.567348387 9.90×10−43
211470_s_atSULT1C23.963534914 1.47×10−64
205815_atREG3A3.770039412 1.10×10−24
209220_atGPC33.752196862 1.99×10−40
201291_s_atTOP2A3.555152759 3.85×10−77
206561_s_atAKR1B103.468810343 1.21×10−24
242881_x_atDUXAP103.301086607 1.01×10−75
204602_atDKK13.048004476 1.88×10−37
203820_s_atIGF2BP33.043781559 1.76×10−56
238021_s_atCRNDE2.985078406 4.63×10−52
209921_atSLC7A112.980302339 1.66×10−51
213194_atROBO12.947155321 1.69×10−71
241418_atNMRAL1P12.911349891 8.91×10−59
223642_atZIC22.821525207 4.36×10−64
222608_s_atANLN2.787055993 6.53×10−62
209875_s_atSPP12.758625836 3.14×10−23
235004_atRBM242.743050577 1.06×10−57
212551_atCAP22.739651435 9.30×10−91
214612_x_atMAGEA62.698311238 6.80×10−34
202422_s_atACSL42.663914238 2.38×10−33
219918_s_atASPM2.652302575 4.07×10−52
235763_atSLC44A52.472658451 2.37×10−33
207828_s_atCENPF2.472581020 2.98×10−78
214710_s_atCCNB12.403923370 1.64×10−56
225681_atCTHRC12.384346752 1.53×10−25
212531_atLCN22.383664937 3.04×10−31
201890_atRRM22.381259393 3.31×10−38
33323_r_atSFN2.365919617 9.97×10−36
218009_s_atPRC12.350952594 8.64×10−54
204162_atNDC802.332439517 1.54×10−54
219787_s_atECT22.313749179 8.57×10−52
207165_atHMMR2.304640216 1.82×10−48
204825_atMELK2.298883197 1.43×10−57
203477_atCOL15A12.295323078 1.49×10−33
203213_atCDK12.271576100 9.42×10−49
206626_x_atSSX12.253448717 3.68×10−30
207325_x_atMAGEA12.252155461 3.97×10−42
204720_s_atDNAJC62.236082454 2.40×10−52
204105_s_atNRCAM2.232778933 1.57×10−38
227892_atPRKAA22.229124796 3.47×10−44
227510_x_atMALAT12.224576694 5.41×10−17
201468_s_atNQO12.209490964 2.70×10−26
205110_s_atFGF132.207535230 1.23×10−32
223381_atNUF22.186987390 5.66×10−56
205476_atCCL202.181812965 1.34×10−20
203755_atBUB1B2.178984131 2.76×10−50
218755_atKIF20A2.153373571 8.72×10−52
231265_atCOX7B22.150792051 5.69×10−41
221558_s_atLEF12.122850025 2.29×10−38
225612_s_atB3GNT52.117997087 7.16×10−31

Table III.

Top 50 downregulated differentially expressed genes.

Table III.

Top 50 downregulated differentially expressed genes.

Probe IDGeneFold changeAdjusted P-value
220491_atHAMP−4.983632921 4.89×10−60
205866_atFCN3−4.729403108 1.63×10−96
222484_s_atCXCL14−4.664513026 2.76×10−105
205984_atCRHBP−4.504769969 5.41×10−83
207804_s_atFCN2−4.498217224 1.69×10−97
207201_s_atSLC22A1−4.496482306 9.93×10−68
220496_atCLEC1B−4.489579549 1.13×10−112
217546_atMT1M−4.427062929 4.20×10−64
230478_atOIT3−4.401727477 1.29×10−106
1559573_atLINC01093−4.372165201 7.87×10−89
223699_atCNDP1−4.185131569 2.30×10−73
229476_s_atTHRSP−4.124366630 2.88×10−56
1559065_a_atCLEC4G−4.114961753 1.36×10−99
206354_atSLCO1B3−4.081557901 3.55×10−63
207102_atAKR1D1−4.020716573 6.22×10−69
1564706_s_atGLS2−4.018224948 3.74×10−70
207608_x_atCYP1A2−4.013090089 7.43×10−78
206727_atC9−3.961247256 9.82×10−48
209687_atCXCL12−3.885309405 1.16×10−60
219014_atPLAC8−3.774128748 3.42×10−72
207995_s_atCLEC4M−3.732362194 5.50×10−88
231678_s_atADH4−3.682472263 1.77×10−48
205554_s_atDNASE1L3−3.666231016 1.29×10−68
220801_s_atHAO2−3.663835833 4.99×10−76
211896_s_atDCN−3.625520940 1.19×10−46
220432_s_atCYP39A1−3.616388369 2.51×10−64
220116_atKCNN2−3.587327181 4.31×10−57
205819_atMARCO−3.569710716 5.45×10−76
202992_atC7−3.473352727 3.89×10−43
230135_atHHIP−3.458281369 1.29×10−91
210328_atGNMT−3.412414732 1.25×10−52
213629_x_atMT1F−3.412271922 3.79×10−72
214478_atSPP2−3.405396399 1.74×10−74
205225_atESR1−3.355285624 2.09×10−74
237350_atTTC36−3.334397580 7.78×10−84
214320_x_atCYP2A6−3.332114585 3.55×10−54
219954_s_atGBA3−3.304920162 1.98×10−59
207262_atAPOF−3.287687923 1.09×10−72
214621_atGYS2−3.272101039 8.61×10−56
206797_atNAT2−3.270355860 8.59×10−76
242817_atPGLYRP2−3.225627679 2.41×10−53
205498_atGHR−3.223775643 1.20×10−66
237390_atADRA1A−3.213027372 8.85×10−54
204704_s_atALDOB−3.208187451 3.84×10−55
209301_atCA2−3.203130234 1.38×10−58
206172_atIL13RA2−3.185151095 1.11×10−52
206210_s_atCETP−3.179352566 1.73×10−66
204428_s_atLCAT−3.145019681 1.47×10−76
205363_atBBOX1−3.135951206 2.16×10−42
208147_s_atCYP2C8−3.115222728 8.68×10−61
GO functional and KEGG pathway enrichment analyses of the upregulated and downregulated DEGs

In order to understand the biological implications of the identified DEGs in HBV-positive HCC tissues, GO functional and KEGG pathway enrichment analyses of the identified DEGs were performed. The GO terms of the up- and downregulated DEGs are presented in Fig. 4.

In the GO biological process category, upregulated DEGs were closely associated with the ‘biological regulation’ and ‘metabolic process’ terms, whereas the downregulated DEGs were closely associated with the ‘metabolic process’ and ‘biological regulation’ terms.

In the GO cellular component category, upregulated DEGs were closely associated with the ‘nucleus’ and ‘membrane’ terms, whereas the downregulated DEGs were closely associated with the ‘membrane’ and ‘vesicle’ terms.

In the GO molecular category, upregulated DEGs were closely associated with the ‘protein binding’ and ‘nucleic acid binding’ terms, whereas the downregulated DEGs were closely associated with the ‘protein binding’ and ‘ion binding’ terms.

In addition, the top 10 enriched KEGG pathway terms of the up- and downregulated genes are provided in Tables IV and V, respectively. The upregulated DEGs were primarily associated with the ‘cell cycle’, whereas the downregulated DEGs were primarily associated with the ‘metabolic pathways’.

Table IV.

Top 10 enriched KEGG pathway terms of upregulated differentially expressed genes.

Table IV.

Top 10 enriched KEGG pathway terms of upregulated differentially expressed genes.

KEGG IDKEGG pathwayNo. ofss genesP-value
hsa04110Cell cycle24 2.51×10−14
hsa05222Small cell lung cancer13 5.70×10−07
hsa05200Pathways in cancer28 4.02×10−06
hsa04512ECM-receptor interaction11 1.42×10−05
hsa04115p53 signaling pathway10 1.76×10−05
hsa04151PI3K-Akt signaling pathwayss21 4.69×10−04
hsa04510Focal adhesion15 4.89×10−04
hsa05146Amoebiasis9 1.74×10−03
hsa05213Endometrial cancer6 2.97×10−03
hsa05218Melanoma7 3.37×10−03

[i] KEGG, Kyoto Encyclopedia of Genes and Genomes; ECM, extracellular matrix.

Table V.

Top 10 enriched KEGG pathway terms of downregulated differentially expressed genes.

Table V.

Top 10 enriched KEGG pathway terms of downregulated differentially expressed genes.

KEGG IDKEGG pathwayNo. of genesP-value
hsa01100Metabolic pathways185<0.01
hsa04610Complement and coagulation cascades33<0.01
hsa05204Chemical carcinogenesis27 5.49×10−12
hsa01200Carbon metabolism30 2.04×10−10
hsa00260Glycine, serine and threonine metabolism17 5.18×10−10
hsa00380Tryptophan metabolism17 5.18×10−10
hsa05150Staphylococcus aureus infection20 6.30×10−10
hsa00830Retinol metabolism21 1.95×10−09
hsa00071Fatty acid degradation17 3.05×10−09
hsa00250Alanine, aspartate and glutamate metabolism15 4.8×10−09

[i] KEGG, Kyoto Encyclopedia of Genes and Genomes.

Notably, there were six genes with log2-fold change >2 in DEGs enriched in the ‘cell cycle’ pathway: SFN, BUB1B, TTK, CCNB1, CDK1 and CDC20. Differential expression analysis was performed on the aforementioned six genes in non-HBV tissues from two independent HCC datasets (TCGA and GSE76427) on different platforms (TCGA, Illumina RNA Sequencing; GSE76427, Illumina HumanHT-12 V4.0 expression beadchip). None of these genes had a log2-fold change >2 (Table VI), which demonstrates that the high expression of these six DEGs in HBV-associated HCC is more significant compared with non-HBV HCC.

Table VI.

Differential expression of six differentially expressed genes in different datasets.

Table VI.

Differential expression of six differentially expressed genes in different datasets.

Large HBV cohortGSE76427TCGA



GeneFold changeAdj. P-valueFold changeAdj. P-valueFold changeAdj. P-value
CCNB12.403923 1.64×10−560.587062 1.36×10−110.607121 1.92×10−11
SFN2.365920 9.97×10−360.880437 3.36×10−030.676444 2.14×10−05
CDK12.271576 9.42×10−490.802341 5.35×10−110.769405 1.93×10−11
BUB1B2.178984 2.76×10−501.121998 5.27×10−120.234839 9.11×10−07
TTK2.066772 2.45×10−471.587680 3.34×10−130.791737 2.17×10−12
CDC202.012448 7.80×10−461.073711 8.29×10−091.712167 4.21×10−10

[i] HBV, hepatitis B virus; Adj., adjusted; TCGA, The Cancer Genome Atlas.

PPI network analysis of the DEGs

To further understand the biological meaning of the DEGs identified by the top KEGG pathways at the protein level, two PPI networks for the proteins encoded by the DEGs in the top pathways were constructed. The PPI network of the ‘cell cycle’ consisted of 24 nodes and 85 edges, whereas the PPI network of the ‘metabolic pathways’ consisted of 184 nodes and 566 edges (Fig. 5).

Discussion

The present study specifically focused on HBV-infected patients, which is different from the previous studies on HCC regardless of etiology (19,27). A total of 682 upregulated DEGs and 1,252 downregulated DEGs were identified in HCC tissues compared with nontumor hepatic tissues in 321 HBV-positive samples. KEGG analyses demonstrated that the upregulated DEGs were enriched in signaling pathways such as the cell cycle, p53 signaling pathway and extracellular matrix-receptor interaction. A previous study showed that HBV infection deregulates the cell cycle pathway (28). Notably, there were 6 genes with a log2-fold change >2 among the DEGs enriched in the ‘cell cycle’ pathway: SFN, BUB1B, TTK, CCNB1, CDK1 and CDC20.

SFN (14-3-3σ) protein is a member of the 14-3-3 superfamily (29). SFN has been found to play a key role in various vital regulatory processes, such as cell cycle regulation and signaling pathways (30). In a previous study, high expression of SFN was detected in HCC tissues but not in adjacent nontumor tissues, which indicated an association between SFN and HCC (31). In another study, SFN exhibited high diagnostic accuracy in the differentiation of HCC from nontumorous hepatocytes (32).

BUB1B (encoding BUBR1) is an important component in the SAC protein family, which has been found to be involved in several forms of human cancer, such as lung cancer and breast cancer (33,34). However, the contradiction of BUB1B expression in cancer cells remains controversial. Low expression of BUB1B is associated with the poor survival of patients with colon adenocarcinomas and lung cancer, however overexpression of BUB1B contributes to the progression and recurrence of gastric cancer and bladder cancer (35). However, several studies showed that the overexpression of BUB1B is associated with worse prognosis in patients with HCC (36,37).

TTK, a dual-specific protein kinase participating in the p53 pathway, has been found to be involved in several cancer types by modulating the mitotic checkpoint (38). A previous study by Miao et al (39), regarding HBV-associated HCC, reported TTK as a promising prognostic marker of HCC. TTK alone can accurately predict the recurrence rate and recurrence time. These findings on TTK drew interest and resulted in further studies on cancer (4042). The results of the present study supported the conclusion of the study by Miao et al (39).

CCNB1 plays an integral role in regulating the G2/M transition in the cell cycle (43). Several studies have found an elevated expression of CCNB1 in different cancer types, such as breast cancer (44), non-small cell carcinoma (45) and gastric cancer (46). In a previous study, CCNB1 was reported as an independent risk factor of recurrence in patients with HBV-associated HCC following surgery (47). However, it is still unclear how CCNB1 contributes to oncogenesis and tumor progression.

CDK1 is required for the role of CCNB1 in the G2/M transition and mitosis resumption (48). Cheng et al (49) conducted in vitro experiments, which demonstrated that HBV could activate the CCNB1-CDK1 kinase in HCC cells. In other studies, CCNB1 and CDK1 were found to be upregulated in the HCC tissues of HBV-positive patients (50). Moreover, overexpression of these two genes is associated with poor prognosis. CDK1 was considered important as CCNB1, since it could affect both overall survival and recurrence-free survival of HBV-positive patients with HCC (51).

CDC20 functions as a regulatory protein that interacts with several other proteins at multiple points in the cell cycle. Chae et al (52) demonstrated that HBV-infection could attenuate the association between BubR1 and CDC20, thus preventing CDC20 from performing its original function, which provided a novel view on the development of HBV-associated HCC.

The high expression of the six DEGs was more significant in HBV-associated HCC than in non-HBV HCC, and was validated in two independent HCC datasets. In future studies, clinical HCC samples should be collected in order to verify that these genes are affected by HBV infection.

The downregulated DEGs were enriched in signaling pathways such as ‘carbon metabolism’, ‘glycine, serine and threonine metabolism’, ‘tryptophan metabolism’, ‘retinol metabolism’ and ‘alanine, aspartate and glutamate metabolism’. A previous study reported that HBV-infection could induce alterations in metabolic signaling pathways. The consequences may alter normal hepatocyte metabolism, thus contributing to the progression of HBV-associated carcinogenesis (53).

In conclusion, the present study identified several DEGs in HCC tissues compared with nontumor tissues from HBV-infected patients, based on a large cohort. Based on the DEGs, several key pathways were identified. The interactions of the DEGs in the pathways were also presented by PPI networks. Some results were consistent with previous studies (39,50). Furthermore, the present study provides new insights into the specific etiology of HCC and molecular mechanisms for the transformation of nontumor hepatic tissues into HCC tissues, in patients with a history of HBV infection. Importantly, these results may provide several potential therapeutic targets for targeted therapy in these patients, which could aid early diagnosis and treatment of HCC.

Supplementary Material

Supporting Data

Acknowledgements

Not applicable.

Funding

This study was supported in part by grants from the Natural Science Foundation of Jiangxi Province (grant no. 20181BBG78042).

Availability of data and materials

The datasets used and/or analyzed during the present study are available from the corresponding author on reasonable request.

Authors' contributions

XZ, LW and YY conceived and designed the study. LW analyzed the data. XZ contributed to literature review. XZ and LW wrote the manuscript. XZ and YY reviewed and edited the manuscript. All authors read and approved the final manuscript.

Ethics approval and consent to participate

Not applicable.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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Volume 19 Issue 5

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Spandidos Publications style
Zhang X, Wang L and Yan Y: Identification of potential key genes and pathways in hepatitis B virus‑associated hepatocellular carcinoma by bioinformatics analyses. Oncol Lett 19: 3477-3486, 2020.
APA
Zhang, X., Wang, L., & Yan, Y. (2020). Identification of potential key genes and pathways in hepatitis B virus‑associated hepatocellular carcinoma by bioinformatics analyses. Oncology Letters, 19, 3477-3486. https://doi.org/10.3892/ol.2020.11470
MLA
Zhang, X., Wang, L., Yan, Y."Identification of potential key genes and pathways in hepatitis B virus‑associated hepatocellular carcinoma by bioinformatics analyses". Oncology Letters 19.5 (2020): 3477-3486.
Chicago
Zhang, X., Wang, L., Yan, Y."Identification of potential key genes and pathways in hepatitis B virus‑associated hepatocellular carcinoma by bioinformatics analyses". Oncology Letters 19, no. 5 (2020): 3477-3486. https://doi.org/10.3892/ol.2020.11470