search button
Open Access

Screening therapeutic targets of ribavirin in hepatocellular carcinoma

  • Authors:
    • Chen Xu
    • Liyun Luo
    • Yongjun Yu
    • Zhao Zhang
    • Yi Zhang
    • Haimei Li
    • Yue Cheng
    • Hai Qin
    • Xipeng Zhang
    • Hongmei Ma
    • Yuwei Li

  • View Affiliations

  • Published online on: April 20, 2018     https://doi.org/10.3892/ol.2018.8552
  • Pages: 9625-9632

  • Copyright: © Xu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )
Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )


Abstract

The objective of the present study was to screen the key genes of ribavirin in hepatocellular carcinoma (HCC) and provide novel therapeutic targets for HCC treatment. The mRNA expression datasets of GSE23031 and GSE74656, as well as the microRNA (miRNA) expression dataset of GSE22058 were downloaded from the Gene Expressed Omnibus database. In the GSE23031 dataset, there were three HCC cell lines treated with PBS and three HCC cell lines treated with ribavirin. In the GSE74656 dataset, five HCC tissues and five carcinoma adjacent tissues were selected. In the GSE22058 dataset, 96 HCC tissues and 96 carcinoma adjacent tissues were selected. The differentially expressed genes (DEGs) and differentially expressed miRNAs were identified via the limma package of R. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed with the Database for Annotation, Visualization and Integrated Discovery. The target mRNAs of DEMs were obtained with TargetScan. A total of 559 DEGs (designated DEG‑Ribavirin) were identified in HCC cells treated with ribavirin compared with PBS and 632 DEGs (designated DEG‑Tumor) were identified in HCC tissues compared with carcinoma adjacent tissues. A total of 220 differentially expressed miRNAs were identified in HCC tissues compared with carcinoma adjacent tissues. In addition, 121 GO terms and three KEGG pathways of DEG‑Ribavirin were obtained, and 383 GO terms and 25 KEGG pathways of DEG‑Tumor were obtained. A total of five key miRNA‑mRNA regulated pairs were identified, namely miR‑183→CCNB1, miR‑96→DEPDC1, miR‑96→NTN4, miR‑183→NTN4 and miR‑145→NTN4. The present study indicated that certain miRNAs (including miR‑96, miR‑145 and miR‑183) and mRNAs (including NAT2, FBXO5, CCNB1, DEPDC1 and NTN4) may be associated with the effects of ribavirin on HCC. Furthermore, they may provide novel therapeutic targets for HCC treatment.

Introduction

Hepatocellular carcinoma (HCC) is the fifth most common malignancy and arises most frequently in patients with cirrhosis (1). It is the second most common cause of cancer-associated mortality globally with 1.6 million mortalities per year, and it is hypothesized that the high global incidence rate and late presentation of HCC may be responsible for this (2,3). Additionally, the general prognosis was poor with an overall survival rate between 3 and 5% in 2006 (4). Symptoms of HCC include yellow skin, bloating from fluid in the abdomen, easy bruising from blood clotting abnormalities, loss of appetite, unintentional weight loss, nausea, vomiting and tiredness (5,6). The primary risk factors for HCC were hepatitis C, hepatitis B, alcoholism, aflatoxin and cirrhosis of the liver (7–10). Liver transplantation, tyrosine kinase inhibitors and surgical resection are currently the primary treatment options (11–13). The treatment of HCC has not been fundamentally improved, which may be seen in the increasing morbidity and mortality each year (14). Ribavirin is an anti-viral drug used to treat hepatitis C, respiratory syncytial virus and other viral infections. If infection is persistent, ribavirin is often used in combination with peginterferon α-2b or peginterferon α-2a (15,16). It has been reported that hepatitis C infection was globally associated with 25% of HCC cases in 2006 (15). Therefore, ribavirin, by itself or in conjunction with peginterferon α-2b or pegylated interferon, has been used to treat HCC in patients with viral infections (17–20). Exploration of the genetic changes in HCC cells is necessary for the study of the pathogenesis and progression of HCC, as well as to develop effective treatments. In the present study, a microarray analysis of mRNA and microRNA (miRNA) was performed in the treatment of ribavirin on HCC, in order to identify possible biomarkers and provide novel potential therapeutic targets for HCC.

Materials and methods

Microarray data and data prx10-processing

The mRNA expression datasets of GSE23031 and GSE74656, as well as the miRNA expression dataset of GSE22058, (21–23) were downloaded from the Gene Expressed Omnibus database (http://www.ncbi.nlm.nih.gov/geo/). They were analyzed using the platforms GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array (Thermo Fisher Scientific, Inc., Waltham, MA, USA), GPL16043 GeneChip® PrimeView™ Human Gene Expression Array (with External spikx10-in RNAs; Thermo Fisher Scientific, Inc.) and GPL10457 Rosetta human miRNA qPCR array (Rosetta Inpharmatics; Merck Sharp & Dohme, Hoddesdon, UK), respectively. The mRNA data (GSE23031) contained three HCC cell lines treated with PBS and three HCC cell lines treated with ribavirin. In the GSE74656, five HCC tissues and five carcinoma adjacent tissues were selected for the study. In the GSE22058, 96 HCC tissues and 96 carcinoma adjacent tissues were selected to study. Robust Multi-Array Average (RMA) was an algorithm used to create an expression matrix from Affymetrix data (24). The raw data were converted into a recognizable format by R, and the RMA was used for correction and normalization.

Differential expression analysis

The differentially expressed genes (DEGs) were identified via the limma package V3.32.10 (http://www.bioconductor.org/packages/3.5/bioc/html/limma.html) (25). According to the criteria: P<0.05 and |log(fold change)|>1, the DEGs were identified in HCC cells treated with ribavirin compared with PBS and designated DEG-Ribavirin. With the same criteria, the DEGs were identified in HCC tissues compared with their matched adjacent tissues and designated DEG-Tumor. Additionally, the differentially expressed miRNAs (DEMs) were obtained in HCC tissues compared with carcinoma adjacent tissues with P<0.05 and |log(fold change)|>0.3.

Functional and pathway enrichment analysis

The Database for Annotation, Visualization and Integrated Discovery (https://david.ncifcrf.gov/) (26) is a widely-used web-based tool for functional and pathway enrichment analysis. In the present study, it was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEG-Ribavirin and DEG-Tumor data. The GO terms and KEGG pathways were selected with P<0.05.

Comparison of DEGs and screening of miRNA-mRNA regulated pairs

The overlapped DEGs of DEG-Ribavirin and DEG-Tumor were selected, and the overlapped DEGs with opposite expression between DEG-Ribavirin and DEG-Tumor were also selected. The TargetScan database was used to predict biological target mRNAs of miRNAs that matched the seed region of each miRNA (27). The target mRNAs of DEMs were then selected using TargetScan. The key miRNAs, which regulated the overlapped DEGs with opposite expression between DEG-Ribavirin and DEG-Tumor, were identified. Subsequently, the miRNA-mRNA regulated pairs were constructed.

Results

DEGs. A total of 559 DEGs (269 upregulated and 290 downregulated) and 623 DEGs (272 upregulated and 351 downregulated) were identified in DEG-Ribavirin and DEG-Tumor. The heat map of them and the top 30 most significant DEGs are presented in Figs. 1 and 2, Tables I and II, respectively. A total of 220 DEMs were obtained. The 30 most significant DEMs are presented in Table III.

Table I.

Top 30 most significant differentially expressed genes in hepatocellular carcinoma cells treated with ribavirin compared with PBS.

Table I.

Top 30 most significant differentially expressed genes in hepatocellular carcinoma cells treated with ribavirin compared with PBS.

GenelogFCAverage expressionP-value
NCF23.63623710.08812 4.01×10−13
TXNIP3.048979.867782 5.95×10−12
PRORSD1P2.5271836.729178 4.91×10−11
NPPB3.007212.09607 8.26×10−11
CYR612.01857210.7489 9.19×10−11
PLA2G4C1.939155.61348 9.29×10−11
CDKN2B2.2820637.523593 9.52×10−11
LEAP22.525599.937438 1.39×10−10
DUSP41.868318.419143 3.18×10−10
FLVCR1-AS12.016828.90536 4.17×10−10
ASH1L-AS11.8031179.666342 4.20×10−10
CXCL31.7974978.092698 5.80×10−10
GLIPR21.6885229.089844 5.91×10−10
CYP1A11.56086711.576 7.61×10−10
EID2B1.6670578.101158 9.18×10−10
JUNB1.5740278.65205 1.10×10−09
BTG21.4986629.091506 1.41×10−09
PPL1.949658.769882 2.08×10−09
ZNF436-AS12.5709478.267133 2.12×10−09
TIGD71.6548076.61165 2.17×10−09
LOC2845131.595886.487373 2.40×10−09
TUFT11.5099310.31677 2.43×10−09
CTSE1.43926711.22817 2.48×10−09
ERP271.37654310.6632 2.51×10−09
UCA11.61105310.73581 2.60×10−09
HSD3B11.6995975.372037 2.61×10−09
GATA6-AS11.9714439.336978 2.99×10−09
LOC1001348221.456047.840603 3.38×10−09
THUMPD3-AS11.3657017.84944 3.41×10−09
GDA1.4827428.376296 3.62×10−09

[i] logFC, log fold-change.

Table II.

Top 30 most significant DEGs in HCC tissues compared with carcinoma adjacent tissues.

Table II.

Top 30 most significant DEGs in HCC tissues compared with carcinoma adjacent tissues.

GenelogFCAverage expressionP-value
CTHRC12.8569455.825291 6.69×10−07
PEA151.4681458.69977 1.04×10−06
CENPE2.0436586.056165 1.29×10−06
C21orf561.2374485.670084 1.54×10−06
DBN11.3835596.53977 1.76×10−06
GLA1.3803237.172235 2.00×10−06
DDX391.178727.721654 2.63×10−06
MPV171.1901429.079669 2.67×10−06
RFX51.4178557.558042 2.89×10−06
TMEM1441.3794565.655336 3.59×10−06
ASNS2.5150066.603396 3.63×10−06
SLC38A61.6784987.591919 3.93×10−06
GRAMD1A1.0134927.228709 4.65×10−06
COMMD81.1096788.353807 7.57×10−06
YWHAZ1.0085728.92205 7.94×10−06
PLXNC11.4513815.772042 1.18×10−05
PLVAP1.0018185.796379 1.20×10−05
SHCBP11.3276734.917339 1.39×10−05
LAMC11.1047936.905647 1.46×10−05
ANXA2P21.8959411.61567 1.52×10−05
ACTR31.0949219.606995 1.63×10−05
CCDC88A1.0026496.195715 1.64×10−05
E2F31.0939466.541921 1.75×10−05
FAM118B1.0062486.9915 1.76×10−05
ZNF354A1.0690785.918941 1.81×10−05
RASGEF1A2.0218724.908126 1.84×10−05
NUP371.3626356.839194 2.01×10−05
ESM11.2915724.762991 2.03×10−05
KPNA22.6063989.341649 2.13×10−05
DCUN1D51.388898.463629 2.34×10−05

[i] logFC, log fold-change; HCC, hepatocellular carcinoma; DEGs, differentially expressed genes.

Table III.

Top 30 most significant differentially expressed miRNA in HCC tissues compared with carcinoma adjacent tissues.

Table III.

Top 30 most significant differentially expressed miRNA in HCC tissues compared with carcinoma adjacent tissues.

miRNAlogFCP-value
hsa-mir-1880.36842 2.41×10−39
hsa-mir-106b0.32071 1.52×10−36
hsa-mir-214−0.54861 7.34×10−36
hsa-mir-930.32346 1.73×10−35
hsa-mir-10a−0.51702 6.33×10−35
hsa-mir-199a-1−0.78765 6.28×10−33
hsa-mir-199a-2−0.73035 5.57×10−31
hsa-mir-3010.5629 1.16×10−28
hsa-mir-424−0.31246 1.72×10−28
hsa-mir-330.29308 7.92×10−26
hsa-mir-324-5p0.37868 1.01×10−23
hsa-mir-250.20282 1.53×10−22
hsa-mir-125b−0.37741 1.95×10−22
hsa-mir-3390.21825 3.36×10−21
hsa-mir-145−0.35693 4.27×10−21
hsa-mir-148b0.20955 6.76×10−21
hsa-mir-1510.25728 9.53×10−21
hsa-mir-2210.35829 1.39×10−20
hsa-mir-18a0.41731 3.09×10−20
hsa-mir-130b0.38724 3.75×10−20
hsa-mir-195−0.34663 6.72×10−20
hsa-mir-99a−0.38524 8.55×10−20
hsa-mir-15b0.27273 1.80×10−19
hsa-mir-1830.42758 2.31×10−19
hsa-mir-2220.30098 2.62×10−18
hsa-mir-125a−0.26924 4.47×10−18
hsa-mir-378−0.33767 7.60×10−18
hsa-mir-101−0.24801 1.85×10−17
hsa-mir-3310.20462 2.33×10−17
hsa-mir-200b−0.6996 2.38×10−17

[i] HCC, hepatocellular carcinoma; logFC, log fold-change; mir, microRNA.

GO terms and KEGG pathways

A total of 121 GO terms and 3 KEGG pathways (cell cycle pathway, p53 signaling pathway and glycine, serine and threonine metabolism pathway) of DEG-Ribavirin were obtained. A total of 383 GO terms and 25 KEGG pathways of DEG-Tumor were obtained. The top 20 enriched GO terms of DEG-Ribavirin and DEG-Tumor are presented in Tables IV and V, respectively. The enriched KEGG pathways of DEG-Ribavirin and DEG-Tumor are presented in Tables VI and VII, respectively.

Table IV.

Top 20 enriched GO terms of DEG-Ribavirin.

Table IV.

Top 20 enriched GO terms of DEG-Ribavirin.

CategoryGO IDGo nameGene numberP-value
BPGO:0022403Cell cycle phase29 1.66×10−06
BPGO:0007049Cell cycle43 1.66×10−06
BPGO:0000279M phase24 8.49×10−06
BPGO:0007067Mitosis19 1.02×10−05
BPGO:0000280Nuclear division19 1.02×10−05
BPGO:0000087M phase of mitotic cell cycle19 1.29×10−05
BPGO:0048285Organelle fission19 1.73×10−05
BPGO:0000278Mitotic cell cycle25 1.88×10−05
BPGO:0022402Cell cycle process32 3.23×10−05
BPGO:0008283Cell proliferation27 3.62×10−05
BPGO:0031497Chromatin assembly11 5.88×10−05
BPGO:0065004Protein-DNA complex assembly11 8.65×10−05
BPGO:0006334Nucleosome assembly10 2.36×10−04
BPGO:0051726Regulation of cell cycle21 2.43×10−04
BPGO:0051301Cell division19 4.39×10−04
BPGO:0034728Nucleosome organization10 5.08×10−04
BPGO:0006323DNA packaging11 6.79×10−04
CCGO:0000786Nucleosome  8 9.25×10−04
BPGO:0010033Response to organic substance330.001109
CCGO:0005819Spindle120.001114

[i] GO, Gene Ontology; DEGs, differentially expressed genes; CC, cellular component; BP, biological process.

Table V.

Top 20 enriched GO terms of DEG-tumor.

Table V.

Top 20 enriched GO terms of DEG-tumor.

CategoryGO IDGo nameGene numberP-value
BPGO:0000279M phase49 1.22×10−17
BPGO:0022403Cell cycle phase54 7.41×10−17
BPGO:0007067Mitosis39 1.19×10−16
BPGO:0000280Nuclear division39 1.19×10−16
BPGO:0000087M phase of mitotic cell cycle39 2.21×10−16
BPGO:0000278Mitotic cell cycle50 4.09×10−16
BPGO:0048285Organelle fission39 5.79×10−16
BPGO:0022402Cell cycle process61 5.37×10−15
MFGO:0048037Cofactor binding39 3.37×10−14
BPGO:0016054Organic acid catabolic process26 4.73×10−14
BPGO:0046395Carboxylic acid catabolic process26 4.73×10−14
CCGO:0005819Spindle30 7.20×10−14
BPGO:0007049Cell cycle71 9.42×10−14
BPGO:0055114Oxidation reduction62 3.81×10−13
BPGO:0007059Chromosome segregation21 2.69×10−12
MFGO:0009055Electron carrier activity34 3.23×10−12
CCGO:0000793Condensed chromosome26 5.97×10−12
CCGO:0000777Condensed chromosome kinetochore18 1.41×10−11
MFGO:0050662Coenzyme binding29 6.32×10−11
CCGO:0000779Condensed chromosome, centromeric region18 1.37×10−10

[i] GO, Gene Ontology; DEGs, differentially expressed genes; CC, cellular component; BP, biological process; MF, molecular foundation.

Table VI.

Enriched KEGG pathways of DEG-Ribavirin.

Table VI.

Enriched KEGG pathways of DEG-Ribavirin.

CategoryPathway nameGene numberP-value
KEGG_PATHWAYhsa04110: Cell cycle15 1.19×10−05
KEGG_PATHWAYhsa00260: Glycine, serine and threonine metabolism  50.011495
KEGG_PATHWAYhsa04115: p53 signaling pathway  60.045851

[i] DEGs, differentially expressed genes; KEGG, Kyoto Encyclopedia of Genes and Genomes.

Table VII.

Enriched KEGG pathways of DEG-tumor.

Table VII.

Enriched KEGG pathways of DEG-tumor.

CategoryPathway nameGene numberP-value
KEGG_PATHWAYhsa00071: Fatty acid metabolism15 1.37×10−09
KEGG_PATHWAYhsa00280: Valine, leucine and isoleucine degradation14 5.58×10−08
KEGG_PATHWAYhsa04110: Cell cycle21 1.08×10−06
KEGG_PATHWAYhsa00830: Retinol metabolism12 3.12×10−05
KEGG_PATHWAYhsa00380: Tryptophan metabolism10 7.32×10−05
KEGG_PATHWAYhsa00650: Butanoate metabolism  9 1.33×10−04
KEGG_PATHWAYhsa00250: Alanine, aspartate and glutamate metabolism  8 4.63×10−04
KEGG_PATHWAYhsa04114: Oocyte meiosis15 5.67×10−04
KEGG_PATHWAYhsa00640: Propanoate metabolism  8 5.69×10−04
KEGG_PATHWAYhsa03320: PPAR signaling pathway110.001281
KEGG_PATHWAYhsa00980: Metabolism of xenobiotics by cytochrome P450100.00174
KEGG_PATHWAYhsa00910: Nitrogen metabolism  60.003688
KEGG_PATHWAYhsa00590: Arachidonic acid metabolism  90.004249
KEGG_PATHWAYhsa00140: Steroid hormone biosynthesis  80.005165
KEGG_PATHWAYhsa00982: Drug metabolism  90.007941
KEGG_PATHWAYhsa00591: Linoleic acid metabolism  60.008896
KEGG_PATHWAYhsa00340: Histidine metabolism  60.010346
KEGG_PATHWAYhsa04115: p53 signaling pathway  90.013645
KEGG_PATHWAYhsa00260: Glycine, serine and threonine metabolism  60.013718
KEGG_PATHWAYhsa00410: β-Alanine metabolism  50.017838
KEGG_PATHWAYhsa04920: Adipocytokine signaling pathway  80.036604
KEGG_PATHWAYhsa00620: Pyruvate metabolism  60.037809
KEGG_PATHWAYhsa00232: Caffeine metabolism  30.039507
KEGG_PATHWAYhsa05222: Small cell lung cancer  90.042544
KEGG_PATHWAYhsa04512: ECM-receptor interaction  90.042544

[i] DEGs, differentially expressed genes; KEGG, Kyoto Encyclopedia of Genes and Genomes.

miRNA-mRNA regulated pairs

There were 50 overlapped DEGs, with 32 [including N-acetyltransferase (NAT2) and F-box only protein 5 (FBXO5)] exhibiting opposite expression between DEG-Ribavirin, and DEG-Tumor. A heat map of the 32 overlapped DEGs is presented in Fig. 3. Furthermore, three DEMs (miR-96, miR-145 and miR-183) were revealed to correspond to three DEGs (CCNB1, DEPDC1 and NTN4) which were included in the aforementioned 32 overlapped DEGs. Finally, five miRNA-mRNA regulated pairs were selected between the above three DEGs and the three DEMs, namely miR-183→CCNB1, miR-96→DEPDC1, miR-96→NTN4, miR-183→NTN4 and miR-145→NTN4.

Discussion

In the present study, the DEGs in HCC cells treated with ribavirin compared with PBS treated HCC tissue, HCC tissues and carcinoma adjacent tissues, were firstly identified, and 32 overlapped DEGs with opposite expression between DEG-Ribavirin and DEG-Tumor were selected. It was notable that NAT2 and FBXO5 were two mRNAs of them with opposite expression between DEG-Ribavirin and DEG-Tumor. NAT2 serves a function in the metabolic activation and detoxification of aromatic amines, which in turn serves a function in the metabolism of aromatic and heterocyclic amines, and hydrazines via N-acetylation and O-acetylation (28). As early as in 1996, Agúndez et al (29) reported that the slow acetylation was associated with an increased risk of HCC. Furthermore, it has been demonstrated that NAT2 activity is associated with smoking-associated HCC (30–32). A number of previous studies that have investigated the association between NAT2 genotypes and HCC risk have been published (32–36). FBXO5, also known as early mitotic inhibitor-1, is a key cell-cycle regulator that promotes S-phase and M-phase entry by inhibiting anaphasx10-promoting complex/cyclosome activity (37). Zhao et al (38) revealed that FBXO5 was overexpressed in HCC, which is in agreement with the results of the present study, and also reported that FBXO5 may control tumor cell proliferation in HCC. In the present study, it was identified that the expression of NAT2 was lower in HCC cells and HCC tissues. However, expression was increased following treatment with ribavirin. However, FBXO5 was overexpressed in HCC cells and HCC tissues, and decreased following treatment with ribavirin. Therefore, it is suspected that NAT2 and FBXO5 may be biomarkers of ribavirin in the treatment of HCC.

The cell cycle has been demonstrated to be associated with the progression and migration of HCC (39–41), and regulation of the cell cycle is considered an effective strategy for HCC treatment (42–45). The p53 signaling pathway has been heavily studied and is reported to serve a function in the occurrence and development of HCC (45–49). The association between the glycine, serine and threonine metabolism pathway and HCC has been less studied, and the glycine, serine and threonine metabolism pathway was also enriched in DEG-Tumor tissues. In this study, only three KEGG pathways of DEG-Ribavirin were obtained, namely cell cycle, p53 signaling pathway and glycine, serine and threonine metabolism. Cell cycle was the most significantly enriched function in this study, which was identified from the enriched GO terms of DEG-Ribavirin (e.g. cell cycle phase, cell cycle and M phase) and DEG-Tumor (e.g. M phase and cell cycle phase), as well as the enriched KEGG pathways of DEG-Tumor. The results of the present study suggest that these three KEGG pathways may be associated with the pathogenesis and treatment of HCC; however, more in-depth research is required.

In the present study, three DEMs (miR-96, miR-145 and miR-183) were identified to correspond to three overlapped DEGs (CCNB1, DEPDC1 and NTN4) with opposite expression in DEG-Ribavirin and DEG-Tumor, and 5 miRNA-mRNA regulated pairs were selected, namely miR-183→CCNB1, miR-96→DEPDC1, miR-96→NTN4, miR-183→NTN4 and miR-145→NTN4. It has been demonstrated that miR-96 downregulation may suppress the growth of HCC (50), and miR-96 may promote cell proliferation and invasion through targeting ephrinA5 in HCC (51). Chen et al (52) considered serum miR-96 as a promising biomarker for HCC with chronic hepatitis B virus infection. Previous studies have demonstrated that miR-145 may inhibit proliferation, migration and invasion, as well as promote apoptosis in HCC (53–56). MiR-183 may also regulate the growth, invasion and apoptosis of HCC (57–59). It has been identified that these miRNA and mRNA are possible biomarkers of ribavirin in HCC, and they may regulate HCC through the 5 miRNA-mRNA pairs.

In conclusion, a number of miRNAs (e.g. miR-96, miR-145 and miR-183) and mRNAs (e.g. NAT2, FBXO5, CCNB1, DEPDC1 and NTN4) may be associated with the effects of ribavirin on HCC. Furthermore, they may provide novel therapeutic targets for drugs of HCC.

References

1 

El-Serag HB: Epidemiology of viral hepatitis and hepatocellular carcinoma. Gastroenterology. 142:1264–1273.e1. 2012. View Article : Google Scholar : PubMed/NCBI

2 

McGuire S: World Cancer Report 2014. Geneva, Switzerland: World Health Organization, International Agency for Research on Cancer, WHO Press, 2015. Adv Nutr. 7:418–419. 2016. View Article : Google Scholar : PubMed/NCBI

3 

Ferlay J, Soerjomataram I, Dikshit R, Eser S, Mathers C, Rebelo M, Parkin DM, Forman D and Bray F: Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer. 136:E359–E386. 2015. View Article : Google Scholar : PubMed/NCBI

4 

Lopez PM, Villanueva A and Llovet JM: Systematic review: Evidencx10-based management of hepatocellular carcinoma-an updated analysis of randomized controlled trials. Aliment Pharmacol Ther. 23:1535–1547. 2006. View Article : Google Scholar : PubMed/NCBI

5 

Kaiser K, Mallick R, Butt Z, Mulcahy MF, Benson AB and Cella D: Important and relevant symptoms including pain concerns in hepatocellular carcinoma (HCC): A patient interview study. Support Care Cancer. 22:919–926. 2014. View Article : Google Scholar : PubMed/NCBI

6 

Ji Z, Meng G, Huang D, Yue X and Wang B: NMFBFS: A NMF-based feature selection method in identifying pivotal clinical symptoms of hepatocellular carcinoma. Comput Math Methods Med. 2015:8469422015. View Article : Google Scholar : PubMed/NCBI

7 

Paul SB, Shalimar, Sreenivas V, Gamanagatti SR, Sharma H, Dhamija E and Acharya SK: Incidence and risk factors of hepatocellular carcinoma in patients with hepatic venous outflow tract obstruction. Aliment Pharmacol Ther. 41:961–971. 2015. View Article : Google Scholar : PubMed/NCBI

8 

Rong G, Wang H, Bowlus CL, Wang C, Lu Y, Zeng Z, Qu J, Lou M, Chen Y, An L, et al: Incidence and risk factors for hepatocellular carcinoma in primary biliary cirrhosis. Clin Rev Allergy Immunol. 48:132–141. 2015. View Article : Google Scholar : PubMed/NCBI

9 

Toyoda H, Kumada T, Tada T, Kiriyama S, Tanikawa M, Hisanaga Y, Kanamori A, Kitabatake S and Ito T: Risk factors of hepatocellular carcinoma development in non-cirrhotic patients with sustained virologic response for chronic hepatitis C virus infection. J Gastroenterol Hepatol. 30:1183–1189. 2015. View Article : Google Scholar : PubMed/NCBI

10 

Zhang XX, Wang LF, Jin L, Li YY, Hao SL, Shi YC, Zeng QL, Li ZW, Zhang Z, Lau GK and Wang FS: Primary biliary cirrhosis-associated hepatocellular carcinoma in Chinese patients: Incidence and risk factors. World J Gastroenterol. 21:3554–3563. 2015. View Article : Google Scholar : PubMed/NCBI

11 

Sacco R, Gadaleta-Caldarola G, Galati G, Lombardi G, Mazza G and Cabibbo G: EASL HCC summit: Liver cancer management. Future Oncol. 10:1129–1132. 2014. View Article : Google Scholar : PubMed/NCBI

12 

Fitzmorris P, Shoreibah M, Anand BS and Singal AK: Management of hepatocellular carcinoma. J Cancer Res Clin Oncol. 141:861–876. 2015. View Article : Google Scholar : PubMed/NCBI

13 

Park JW, Chen M, Colombo M, Roberts LR, Schwartz M, Chen PJ, Kudo M, Johnson P, Wagner S, Orsini LS and Sherman M: Global patterns of hepatocellular carcinoma management from diagnosis to death: The BRIDGE Study. Liver Int. 35:2155–2166. 2015. View Article : Google Scholar : PubMed/NCBI

14 

Scaggiante B, Farra R, Dapas B, Baj G, Pozzato G, Grassi M, Zanconati F and Grassi G: Aptamer targeting of the elongation factor 1A impairs hepatocarcinoma cells viability and potentiates bortezomib and idarubicin effects. Int J Pharm. 506:268–279. 2016. View Article : Google Scholar : PubMed/NCBI

15 

Alter MJ: Epidemiology of hepatitis C virus infection. World J Gastroenterol. 13:2436–2441. 2007. View Article : Google Scholar : PubMed/NCBI

16 

Smith DW, Frankel LR, Mathers LH, Tang AT, Ariagno RL and Prober CG: A controlled trial of aerosolized ribavirin in infants receiving mechanical ventilation for severe respiratory syncytial virus infection. N Engl J Med. 325:24–29. 1991. View Article : Google Scholar : PubMed/NCBI

17 

Harada N, Hiramatsu N, Oze T, Morishita N, Yamada R, Hikita H, Miyazaki M, Yakushijin T, Miyagi T, Yoshida Y, et al: Risk factors for hepatocellular carcinoma in hepatitis C patients with normal alanine aminotransferase treated with pegylated interferon and ribavirin. J Viral Hepat. 21:357–365. 2014. View Article : Google Scholar : PubMed/NCBI

18 

Liu CJ, Chu YT, Shau WY, Kuo RN, Chen PJ and Lai MS: Treatment of patients with dual hepatitis C and B by peginterferon alpha and ribavirin reduced risk of hepatocellular carcinoma and mortality. Gut. 63:506–514. 2014. View Article : Google Scholar : PubMed/NCBI

19 

Honda T, Ishigami M, Masuda H, Ishizu Y, Kuzuya T, Hayashi K, Itoh A, Hirooka Y, Nakano I, Ishikawa T, et al: Effect of peginterferon alfa-2b and ribavirin on hepatocellular carcinoma prevention in older patients with chronic hepatitis C. J Gastroenterol Hepatol. 30:321–328. 2015. View Article : Google Scholar : PubMed/NCBI

20 

Krastev Z, Jelev D, Antonov K, Petkova T, Atanasova E, Zheleva N, Tomov B, Boyanova Y and Mateva L: Ombitasvir, paritaprevir, ritonavir, dasabuvir and ribavirin in cirrhosis after complete destruction of hepatocellular carcinoma. World J Gastroenterol. 22:2630–2635. 2016. View Article : Google Scholar : PubMed/NCBI

21 

Thomas E, Feld JJ, Li Q, Hu Z, Fried MW and Liang TJ: Ribavirin potentiates interferon action by augmenting interferon-stimulated gene induction in hepatitis C virus cell culture models. Hepatology. 53:32–41. 2011. View Article : Google Scholar : PubMed/NCBI

22 

Burchard J, Zhang C, Liu AM, Poon RT, Lee NP, Wong KF, Sham PC, Lam BY, Ferguson MD, Tokiwa G, et al: microRNA-122 as a regulator of mitochondrial metabolic gene network in hepatocellular carcinoma. Mol Syst Biol. 6:4022010. View Article : Google Scholar : PubMed/NCBI

23 

Liu AM, Yao TJ, Wang W, Wong KF, Lee NP, Fan ST, Poon RT, Gao C and Luk JM: Circulating miR-15b and miR-130b in serum as potential markers for detecting hepatocellular carcinoma: A retrospective cohort study. BMJ Open. 2:e0008252012. View Article : Google Scholar : PubMed/NCBI

24 

Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U and Speed TP: Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics. 4:249–264. 2003. View Article : Google Scholar : PubMed/NCBI

25 

Diboun I, Wernisch L, Orengo CA and Koltzenburg M: Microarray analysis after RNA amplification can detect pronounced differences in gene expression using limma. BMC Genomics. 7:2522006. View Article : Google Scholar : PubMed/NCBI

26 

Dennis G Jr, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC and Lempicki RA: DAVID: Database for annotation, visualization, and integrated discovery. Genome Biol. 4:P32003. View Article : Google Scholar : PubMed/NCBI

27 

Lewis BP, Burge CB and Bartel DP: Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell. 120:15–20. 2005. View Article : Google Scholar : PubMed/NCBI

28 

Smith CA, Smith G and Wolf CR: Genetic polymorphisms in xenobiotic metabolism. Eur J Cancer. 30A:1921–1935. 1994. View Article : Google Scholar : PubMed/NCBI

29 

Agundez JA, Olivera M, Ladero JM, Rodriguez-Lescure A, Ledesma MC, Diaz-Rubio M, Meyer UA and Benítez J: Increased risk for hepatocellular carcinoma in NAT2-slow acetylators and CYP2D6-rapid metabolizers. Pharmacogenetics. 6:501–512. 1996. View Article : Google Scholar : PubMed/NCBI

30 

Farker K, Schotte U, Scheele J and Hoffmann A: Impact of N-acetyltransferase polymorphism (NAT2) in hepatocellular carcinoma (HCC)-an investigation in a department of surgical medicine. Exp Toxicol Pathol. 54:387–391. 2003. View Article : Google Scholar : PubMed/NCBI

31 

Zhang J, Xu F and Ouyang C: Joint effect of polymorphism in the N-acetyltransferase 2 gene and smoking on hepatocellular carcinoma. Tumour Biol. 33:1059–1063. 2012. View Article : Google Scholar : PubMed/NCBI

32 

Farker K, Schotte U, Scheele J and Hoffmann A: Assessment of frequencies of lifestyle factors and polymorphisms of drug-metabolizing enzymes (NAT2, CYP2E1) in human hepatocellular carcinoma (HCC) patients in a department of surgical medicinx10-a pilot investigation. Int J Clin Pharmacol Ther. 40:120–124. 2002. View Article : Google Scholar : PubMed/NCBI

33 

Huang YS, Chern HD, Wu JC, Chao Y, Huang YH, Chang FY and Lee SD: Polymorphism of the N-acetyltransferase 2 gene, red meat intake, and the susceptibility of hepatocellular carcinoma. Am J Gastroenterol. 98:1417–1422. 2003. View Article : Google Scholar : PubMed/NCBI

34 

Blum HE: Hepatocellular carcinoma: Susceptibility markers. IARC Sci Publ. 154:241–244. 2001.PubMed/NCBI

35 

Gelatti U, Covolo L, Talamini R, Tagger A, Barbone F, Martelli C, Cremaschini F, Franceschi S, Ribero ML, Garte S, et al: N-Acetyltransferasx10-2, glutathione S-transferase M1 and T1 genetic polymorphisms, cigarette smoking and hepatocellular carcinoma: A casx10-control study. Int J Cancer. 115:301–306. 2005. View Article : Google Scholar : PubMed/NCBI

36 

Xu B, Wang F, Song C, Sun Z, Cheng K, Tan Y, Wang H and Zou H: Largx10-scale proteome quantification of hepatocellular carcinoma tissues by a three-dimensional liquid chromatography strategy integrated with sample preparation. J Proteome Res. 13:3645–3654. 2014. View Article : Google Scholar : PubMed/NCBI

37 

Gütgemann I, Lehman NL, Jackson PK and Longacre TA: Emi1 protein accumulation implicates misregulation of the anaphase promoting complex/cyclosome pathway in ovarian clear cell carcinoma. Mod Pathol. 21:445–454. 2008. View Article : Google Scholar : PubMed/NCBI

38 

Zhao Y, Tang Q, Ni R, Huang X, Wang Y, Lu C, Shen A, Wang Y, Li C, Yuan Q, et al: Early mitotic inhibitor-1, an anaphasx10-promoting complex/cyclosome inhibitor, can control tumor cell proliferation in hepatocellular carcinoma: Correlation with Skp2 stability and degradation of p27(Kip1). Human Pathol. 44:365–373. 2013. View Article : Google Scholar

39 

Feng YM, Feng CW, Chen SY, Hsieh HY, Chen YH and Hsu CD: Cyproheptadine, an antihistaminic drug, inhibits proliferation of hepatocellular carcinoma cells by blocking cell cycle progression through the activation of P38 MAP kinase. BMC Cancer. 15:1342015. View Article : Google Scholar : PubMed/NCBI

40 

Wang Z, Wei W, Sun CK, Chua MS and So S: Suppressing the CDC37 cochaperone in hepatocellular carcinoma cells inhibits cell cycle progression and cell growth. Liver Int. 35:1403–1415. 2015. View Article : Google Scholar : PubMed/NCBI

41 

Deng L, Yang J, Chen H, Ma B, Pan K, Su C, Xu F and Zhang J: Knockdown of TMEM16A suppressed MAPK and inhibited cell proliferation and migration in hepatocellular carcinoma. Onco Targets Ther. 9:325–333. 2016.PubMed/NCBI

42 

Milovanovic P, Rakocevic Z, Djonic D, Zivkovic V, Hahn M, Nikolic S, Amling M, Busse B and Djuric M: Nano-structural, compositional and micro-architectural signs of cortical bone fragility at the superolateral femoral neck in elderly hip fracture patients vs. healthy aged controls. Exp Gerontol. 55:19–28. 2014. View Article : Google Scholar : PubMed/NCBI

43 

Wilson JM, Kunnimalaiyaan S, Gamblin TC and Kunnimalaiyaan M: MK2206 inhibits hepatocellular carcinoma cellular proliferation via induction of apoptosis and cell cycle arrest. J Surg Res. 191:280–285. 2014. View Article : Google Scholar : PubMed/NCBI

44 

Jiang W, Huang H, Ding L, Zhu P, Saiyin H, Ji G, Zuo J, Han D, Pan Y, Ding D, et al: Regulation of cell cycle of hepatocellular carcinoma by NF90 through modulation of cyclin E1 mRNA stability. Oncogene. 34:4460–4470. 2015. View Article : Google Scholar : PubMed/NCBI

45 

Liu YS, Tsai YL, Yeh YL, Chung LC, Wen SY, Kuo CH, Lin YM, Padma VV, Kumar VB and Huang CY: Cell cycle regulation in the estrogen receptor beta (ESR2)-overexpressing hep3b hepatocellular carcinoma cell line. Chin J Physiol. 58:134–140. 2015.PubMed/NCBI

46 

Meng X, Franklin DA, Dong J and Zhang Y: MDM2-p53 pathway in hepatocellular carcinoma. Cancer Res. 74:7161–7167. 2014. View Article : Google Scholar : PubMed/NCBI

47 

Sun J, Wang B and Liu Y, Zhang L, Ma A, Yang Z, Ji Y and Liu Y: Transcription factor KLF9 suppresses the growth of hepatocellular carcinoma cells in vivo and positively regulates p53 expression. Cancer Lett. 355:25–33. 2014. View Article : Google Scholar : PubMed/NCBI

48 

Li X, Yu J, Brock MV, Tao Q, Herman JG, Liang P and Guo M: Epigenetic silencing of BCL6B inactivates p53 signaling and causes human hepatocellular carcinoma cell resist to 5-FU. Oncotarget. 6:11547–11560. 2015.PubMed/NCBI

49 

Wang P, Cui J, Wen J, Guo Y, Zhang L and Chen X: Cisplatin induces HepG2 cell cycle arrest through targeting specific long noncoding RNAs and the p53 signaling pathway. Oncol Lett. 12:4605–4612. 2016. View Article : Google Scholar : PubMed/NCBI

50 

Baik SH, Lee J, Lee YS, Jang JY and Kim CW: ANT2 shRNA downregulates miR-19a and miR-96 through the PI3K/Akt pathway and suppresses tumor growth in hepatocellular carcinoma cells. Exp Mol Med. 48:e2222016. View Article : Google Scholar : PubMed/NCBI

51 

Wang TH, Yeh CT, Ho JY, Ng KF and Chen TC: OncomiR miR-96 and miR-182 promote cell proliferation and invasion through targeting ephrinA5 in hepatocellular carcinoma. Mol Carcinog. 55:366–375. 2016. View Article : Google Scholar : PubMed/NCBI

52 

Chen Y, Dong X, Yu D and Wang X: Serum miR-96 is a promising biomarker for hepatocellular carcinoma in patients with chronic hepatitis B virus infection. Int J Clin Exp Med. 8:18462–18468. 2015.PubMed/NCBI

53 

Liu Y, Wu C, Wang Y, Wen S, Wang J, Chen Z, He Q and Feng D: MicroRNA-145 inhibits cell proliferation by directly targeting ADAM17 in hepatocellular carcinoma. Oncol Rep. 32:1923–1930. 2014. View Article : Google Scholar : PubMed/NCBI

54 

Ding W, Tan H, Zhao C, Li X, Li Z, Jiang C, Zhang Y and Wang L: MiR-145 suppresses cell proliferation and motility by inhibiting ROCK1 in hepatocellular carcinoma. Tumour Biol. 37:6255–6260. 2016. View Article : Google Scholar : PubMed/NCBI

55 

Ju BL, Chen YB, Zhang WY, Yu CH, Zhu DQ and Jin J: miR-145 regulates chemoresistance in hepatocellular carcinoma via epithelial mesenchymal transition. Cell Mol Biol (Noisy-le-grand). 61:12–16. 2015.PubMed/NCBI

56 

Wang G, Zhu S, Gu Y, Chen Q, Liu X and Fu H: MicroRNA-145 and MicroRNA-133a Inhibited proliferation, migration, and invasion, while promoted apoptosis in hepatocellular carcinoma cells via targeting FSCN1. Dig Dis Sci. 60:3044–3052. 2015. View Article : Google Scholar : PubMed/NCBI

57 

Li J, Fu H, Xu C, Tie Y, Xing R, Zhu J, Qin Y, Sun Z and Zheng X: miR-183 inhibits TGF-beta1-induced apoptosis by downregulation of PDCD4 expression in human hepatocellular carcinoma cells. BMC Cancer. 10:3542010. View Article : Google Scholar : PubMed/NCBI

58 

Liang Z, Gao Y, Shi W, Zhai D, Li S, Jing L, Guo H, Liu T, Wang Y and Du Z: Expression and significance of microRNA-183 in hepatocellular carcinoma. ScientificWorldJournal. 2013:3818742013. View Article : Google Scholar : PubMed/NCBI

59 

Li ZB, Li ZZ, Li L, Chu HT and Jia M: MiR-21 and miR-183 can simultaneously target SOCS6 and modulate growth and invasion of hepatocellular carcinoma (HCC) cells. Eur Rev Med Pharmacol Sci. 19:3208–3217. 2015.PubMed/NCBI

Related Articles

Journal Cover

June-2018
Volume 15 Issue 6

Print ISSN: 1792-1074
Online ISSN:1792-1082

Sign up for eToc alerts

Recommend to Library

  • Journal Metrics
  • Impact Factor: 2.5
  • Ranked Oncology
    (total number of cites)
  • CiteScore: 5.7
  • CiteScore Rank: Q2: #139/404 Oncology
  • Source Normalized Impact per Paper (SNIP): 0.574
  • SCImago Journal Rank (SJR): 0.644
International Symposium on Molecular Medicine Conference Banner
The Cancer Story
World Congress on Advances in Oncology and International Symposium on Molecular Medicine Conference Banner
World Academy Of Sciences Banner
Heyday Road Banner

0

Copy and paste a formatted citation
x
Spandidos Publications style
Xu C, Luo L, Yu Y, Zhang Z, Zhang Y, Li H, Cheng Y, Qin H, Zhang X, Ma H, Ma H, et al: Screening therapeutic targets of ribavirin in hepatocellular carcinoma. Oncol Lett 15: 9625-9632, 2018.
APA
Xu, C., Luo, L., Yu, Y., Zhang, Z., Zhang, Y., Li, H. ... Li, Y. (2018). Screening therapeutic targets of ribavirin in hepatocellular carcinoma. Oncology Letters, 15, 9625-9632. https://doi.org/10.3892/ol.2018.8552
MLA
Xu, C., Luo, L., Yu, Y., Zhang, Z., Zhang, Y., Li, H., Cheng, Y., Qin, H., Zhang, X., Ma, H., Li, Y."Screening therapeutic targets of ribavirin in hepatocellular carcinoma". Oncology Letters 15.6 (2018): 9625-9632.
Chicago
Xu, C., Luo, L., Yu, Y., Zhang, Z., Zhang, Y., Li, H., Cheng, Y., Qin, H., Zhang, X., Ma, H., Li, Y."Screening therapeutic targets of ribavirin in hepatocellular carcinoma". Oncology Letters 15, no. 6 (2018): 9625-9632. https://doi.org/10.3892/ol.2018.8552