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Research ArticleIdentification of Key Genes and Prognostic Value Analysis inHepatocellular Carcinoma by Integrated Bioinformatics Analysis
Meng Wang ,1 Licheng Wang ,2 Shusheng Wu ,3 Dongsheng Zhou ,3,4
and Xianming Wang 3,4
1General Surgery Department, Ward 3, Central Hospital of Zi Bo, Shandong 255000, China2Department of Urology, Tongji Hospital, Tongji University School of Medicine, Shanghai 200065, China3Department of General Surgery, Shandong Provincial Qianfoshan Hospital, Shandong University, Shandong 250014, China4Department of General Surgery, The First Affiliated Hospital of Shandong First Medical University, Shandong 250014, China
Correspondence should be addressed to Dongsheng Zhou; gong04diqin@163.comand Xianming Wang; wangxianmingts@163.com
Received 2 July 2019; Revised 7 August 2019; Accepted 20 August 2019; Published 22 November 2019
Academic Editor: Byung-Hoon Jeong
Copyright © 2019 Meng Wang et al. This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Emerging evidence indicates that various functional genes with altered expression are involved in the tumor progression of humancancers. This study is aimed at identifying novel key genes that may be used for hepatocellular carcinoma (HCC) diagnosis,prognosis, and targeted therapy. This study included 3 expression profiles (GSE45267, GSE74656, and GSE84402), which wereobtained from the Gene Expression Omnibus (GEO). GEO2R was used to analyze the differentially expressed genes (DEGs)between HCC and normal samples. The functional and pathway enrichment analysis was performed by the Database forAnnotation, Visualization and Integrated Discovery. A protein-protein interaction (PPI) network of the identified DEGs wasconstructed using the Search Tool for the Retrieval of Interacting Gene, and hub genes were identified. ONCOMINE and CCLEdatabases were used to verify the expression of the hub genes in HCC tissues and cells. Kaplan-Meier plotter was used to assessthe effects of the hub genes on the overall survival of HCC patients. A total of 99 DEGs were identified from the 3 expressionprofiles. These DEGs were enriched with functional processes and pathways related to HCC pathogenesis. From the PPInetwork, 5 hub genes were identified. The expression of the 5 hub genes was all upregulated in HCC tissues and cells comparedwith the control tissues and cells. Kaplan-Meier survival curves indicated that high expression of cyclin-dependent kinase(CDK1), cyclin B1 (CCNB1), cyclin B2 (CCNB2), MAD2 mitotic arrest deficient-like 1 (MAD2L1), and topoisomerase IIα(TOP2A) predicted poor overall survival in HCC patients (all log-rank P < 0:01). These results revealed that the DEGs mayserve as candidate key genes during HCC pathogenesis. The 5 hub genes, including CDK1, CCNB1, CCNB2, MAD2L1, andTOP2A, may serve as promising prognostic biomarkers in HCC.
1. Introduction
Hepatocellular carcinoma (HCC) remains a serious healthburden and is the second leading cause of cancer mortalityworldwide [1]. Statistical data have indicated that the mor-bidity and mortality of HCC have been increasing in recentyears, mainly due to the increased infection of hepatitis Cvirus [2]. Researchers have identified several established riskfactors for the occurrence of HCC, such as liver cirrhosis, viralinfection, metabolic disorder, and heavy alcohol consump-
tion [3]. Despite advances in various therapeutic strategies,such as surgery, chemotherapy, radiotherapy, and biologics,the prognosis and outcomes remain poor in patients sufferingfrom HCC [4]. Therefore, efficient diagnosis and prognosisremain great challenges for HCC treatment.
It is generally considered that tumorigenesis is a complexprocess with a wide spectrum of genetic alterations [5]. Thesegenes typically exhibit aberrant expression patterns and haveclinical significance in cancer diagnosis and prognosis [6].Currently, some molecules have been recognized as diagnos-
HindawiInternational Journal of GenomicsVolume 2019, Article ID 3518378, 21 pageshttps://doi.org/10.1155/2019/3518378
tic and prognostic biomarkers in HCC. For example, the highexpression of peroxiredoxin 1 (Prdx1) is associated withtumor development and overall survival of HCC patientsand serves as a candidate biomarker for the screening andprediction of this malignancy [7]. Sulfite oxidase (SUOX)expression is downregulated during tumorigenesis of HCCand is correlated with HCC diagnosis and prognosis [8].Upregulated expression of distal-less homeobox gene 4(DLX4) in HCC samples has been shown to be associatedwith poor prognosis of HCC patients [9]. Similarly, thealtered alpha-fucosidase (AFU) expression has significantprognostic value in HCC patients and acts as a potential tar-get for HCC-targeted therapy [10]. However, the availablebiomarkers are not suitable for all the HCC cases due to thelimitations of sensitivity and specificity. Accordingly, theidentification of novel functional genes may contribute tothe understanding of tumor pathogenesis and the improve-ment of diagnosis and prognosis of HCC.
In recent research, differentially expressed genes(DEGs) in tumor samples compared with normal samplescan be identified using gene expression profiling arrays[11, 12]. Some key molecules have also been reported inHCC using bioinformatics analysis [13, 14]. However, thenumber of the identified functional genes is far from suf-ficient to explain the mechanisms underlying the patho-genesis of HCC. Thus, this study used bioinformaticsanalyses to further identify key genes in HCC progressionfrom 3 gene expression profiles from the Gene ExpressionOmnibus (GEO) database and assessed the clinical signifi-cance of the DEGs in HCC prognosis. The expression andprognostic value of the identified key genes were furtherverified using the data from The Cancer Genome Atlas(TCGA) database.
2. Materials and Methods
2.1. Data Collection. In this study, we firstly downloaded 3gene expression profiles from GEO database (http://www.ncbi.nlm.nih.gov/geo), including GSE45267, GSE74656,and GSE84402. The inclusion criteria for the expressionprofiles were as follows: (1) the samples detected are tis-sues, (2) all tissues are diagnosed with HCC tissues andnormal tissues, (3) gene expression profiling of mRNA,(4) samples collected from the same racial population,(4) probes can be converted into the corresponding genesymbols, and (5) complete information for our analyses.The array data of GSE45267 included 49 HCC tumor tis-sues and 38 normal tissues. GSE74656 contained 10 sam-ples, including 5 HCC tumors and 5 adjacent normaltissues. GSE84402 was comprised of 14 tumor tissuesand 14 adjacent noncancerous tissues [15].
2.2. Data Processing. The DEGs between the HCC samplesand normal samples were analyzed by GEO2R (http://www.ncbi.nlm.nih.gov/geo/geo2r), which is a built-in online toolof GEO [16]. Adjusted P value and |log fold change|(|log FC|) were used to evaluate the significance of DEGs,and adjusted P < 0:05 and ∣ log FC∣ > 2 were set as thecutoff criteria.
2.3. Functional and Pathway Enrichment Analysis. TheDatabase for Annotation, Visualization and IntegratedDiscovery (DAVID, http://david.ncifcrf.gov/) is an essen-tial program for the comprehensive gene function analysis,which aids the researchers to understand the biologicalsignificance of abundant genes [17]. Gene ontology (GO)analysis and Kyoto Encyclopedia of Genes and Genome(KEGG) pathway enrichment analysis were performed forthe obtained DEGs. A result with a P < 0:05 was consid-ered statistically significant.
2.4. PPI Network Construction and Module Selection. Sincethe interactions between proteins represent the pivotal eventsduring cellular biological processes, we constructed aprotein-protein interaction (PPI) network of the identifiedDEGs using the Search Tool for the Retrieval of InteractingGene (STRING, http://string.embl.de/) database [18]. ThePPI network was visualized using Cytoscape (version 3.7.0)[19], and a confidence score ≥ 0:7 was used as the cutoff cri-terion. Subsequently, the modules of the PPI network werescreened by the Molecular Complex Detection (MCODE)with the following parameters: degree cutoff = 2, node scorecutoff = 0:2, k‐core = 2, and maximum depth = 100 [20].
2.5. Expression Analysis. The mRNA expression levels of thehubgenesbetweenHCCtissues andnormal controlswere ana-lyzed using the ONCOMINE (http://www.oncomine.org)database [21], and the data were collected from three litera-tures [22–24]. In addition, the expression results were furtherconfirmed in HCC cells by the CCLE (http://portals.broadinstitute.org/ccle/home) database [25]. The differencesbetween two groups were analyzed using Student’s t-test.P < 0:05 and fold changes > 2 were set as the cutoff criteria.
2.6. Survival Analysis of DEGs. The prognostic value of theidentified hub genes in HCC was further assessed by theKaplan-Meier plotter (KM plotter, http://www.kmplot.com/analysis/) [26]. The analysis included 364 patients, and theirKM survival curves were conducted. In addition, the KM sur-vival curves for patients with different tumor stages were sep-arately plotted. However, only 5 patients were diagnosed withtumor stage 4, and the curve could not be performed due tothe limited sample size. The actual number in the other stages
GSE45267 GSE74656
GSE84402
210
121
99
25
11220
105
Figure 1: DEG identification in 3 mRNA expression profiles(GSE45267, GSE74656, and GSE84402). A total of 99 DEGs wereidentified from the 3 expression profiles. DEGs: differentiallyexpressed genes.
2 International Journal of Genomics
can be lower due to missing expression values and/or incom-plete survival data. The gene expression was grouped using acutoff value that is located between the lower and upper quar-tiles and computed by the Kaplan-Meier plotter with a bestperforming threshold. The log-rank P value for the differentsurvival distribution between the low and high expressiongroup was assessed, and the hazard radio (HR) with 95%confidence interval (95% CI) was calculated and plotted onthe webpage.
2.7. Verification of Expression and Prognostic Value of DEGsUsing TCGA Data. To confirm the clinical significance ofthe 5 hub genes in the prognosis of HCC, data from TCGAdatabase were further assessed using the Gene ExpressionProfiling Interactive Analysis (GEPIA), which is a web-based tool to deliver fast and customizable functionalitiesbased on TCGA data [27]. The expression patterns in HCCtissues and the Kaplan-Meier survival curves were all per-formed using the GEPIA.
3. Results
3.1. Identification of DEGs in HCC. According to the GEO2Ranalysis, a total of 352, 249, and 455 DEGs were, respectively,identified in GSE45267, GSE74656, and GSE84402. Amongthese DEGs, 99 genes with significant aberrant expressionwere extracted from all the three datasets (Figure 1),including 38 upregulated genes and 61 downregulatedgenes (Table 1).
3.2. GO Analysis and Pathway Enrichment Analysis of DEGsin HCC. The potential biological function of the identified 99DEGs was assessed using GO analysis. As shown in Table 2,these genes were mainly enriched in biological processesrelated to cell division and mitotic nuclear division. More-over, the potential signaling pathways which these DEGsinvolved were examined using KEGG analysis. From theresults in Table 2, we found that the DEGs were mostlyenriched in cell cycle and mineral absorption processes.
Table 1: Upregulated and downregulated DEGs.
DEGs Gene name
Upregulated
ACSL4, ANLN, ASPM, BUB1, BUB1B, CCNB1, CCNB2, CDC20, CDC6, CDK1,CDKN3, CENPF, CKS2, CRNDE, CTHRC1, DLGAP5, DTL, ECT2, GINS1, GPC3,
IGF2BP3, KIAA0101, KIF20A, MAD2L1, NCAPG, NDC80, NUF2, PBK, PEG10, PRC1,RACGAP1, RRM2, SULT1C2, TOP2A, TPX2, TTK, UBE2C, UBE2T
Downregulated
AADAT, ACSM3, ADH1B, ADH1C, AGXT2, AKR1D1, ALDH8A1, ALDOB, APOF,BCHE, CFHR3, CFHR4, CFP, CLEC1B, CLEC4G, CLEC4M, CLRN3, CNDP1, CRHBP,
CXCL2, CYP1A2, CYP26A1, CYP2B6, CYP2C9, CYP2C19, CYP2C18, ESR1, FCN2, FCN3,GBA3, GNMT, GYS2, HAMP, HAO2, HGFAC, KCNN2, KLKB1, KMO, LCAT, MARCO,
MASP2, MT1E, MT1F, MT1G, MT1H, MT1M, MT1X, NAT2, OIT3, PBLD, PCK1,PGLYRP2, PLG, SLC22A1, SLC25A47, STAB2, THRSP, TMEM27, TTC36, VIPR1, XDH
99 DEGs were identified from the three profile datasets, including 38 upregulated genes and 61 downregulated genes in the HCC tissues compared with thenormal controls. The bold genes are hub genes. DEGs: differentially expressed genes.
Table 2: Functional and pathway enrichment analyses of DEGs in HCC.
Term Description P value Count Gene name
GO:0045926 Negative regulation of growth 9.99E-07 7MT1M, GPC3, MT1E, MT1H,
MT1X, MT1G, MT1F
GO:0051301 Cell division 1.30E-05 15CDC6, CDK1, NUF2, TPX2, CENPF,
NDC80, CDC20, UBE2C, CCNB1, CCNB2,MAD2L1, NCAPG, BUB1, CKS2, BUB1B
GO:0007067 Mitotic nuclear division 2.01E-05 13CDK1, CDC6, CCNB2, NUF2, BUB1, TPX2, CENPF,
BUB1B, NDC80, ANLN, CDC20, PBK, ASPM
GO:0071276 Cellular response to cadmium ion 4.32E-05 6 MT1E, CYP1A2, MT1H, MT1X, MT1G, MT1F
GO:0071294 Cellular response to zinc ion 8.05E-05 6 MT1M, MT1E, MT1H, MT1X, MT1G, MT1F
GO:0007094 Mitotic spindle assembly checkpoint 6.21E-03 5 MAD2L1, BUB1, CENPF, TTK, BUB1B
GO:0000922 Spindle pole 2.80E-02 7CCNB1, CDC6, MAD2L1, PRC1,
TPX2, CENPF, CDC20
GO:0007062 Sister chromatid cohesion 3.61E-02 7MAD2L1, NUF2, BUB1, CENPF, BUB1B,
NDC80, CDC20
KEGG:hsa04110 Cell cycle 4.15E-03 9CCNB1, CDK1, CDC6, MAD2L1, CCNB2,
BUB1, TTK, BUB1B, CDC20
KEGG:hsa04978 Mineral absorption 2.54E-02 5 MT1M, MT1E, MT1H, MT1X, MT1G, MT1F
DEGs: differentially expressed genes; GO: gene ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes. The bold genes are hub genes.
3International Journal of Genomics
DTL
ZWINT CENPF ANLN
NUSAP1TPX2
ECT2
UBE2CBUB1BUB1B
MAD2L1
NDC80
RRM2
TTK
CKS2
DLGAP5
CDKN3
PRC1
PBK
EZH2
AADATAGXT2PLG
IGFBP3 GNMT KMO
NCAPG
KIF20AASPMNUF2
CCNB1 CDC20 CDK1CCNB2
CDC6
ADH1B
CYP2C9ADH6
CYP26A1CYP2B6
CYP2C18
RACGAP1
TOP2A
KIAA0101
FCN3 MASP1
FCN2
MASP2
XDH
NAT2
CYP1A2
CYP2C19
(a)
TPX2ZWINT
ANLN
ASPM
NUSAP1
CENPF
DLGAP5
ECT2
NUF2
RRM2 BUB1B
RACGAP1 PBK
KIAA0101
CCMB2
CDC6
CKS2
CDKN3 UBE2C
TTK
NCAPG
EZH2PRC1
KIF20A
NDC80
BUB1
CCNB1
CDC20
MAD2L1
CDK1
TOP2A
(b)
Figure 2: A PPI network of the DEGs and a significant module in the PPI network. (a) DEG PPI network contained 99 nodes and 298 edges.(b) The significant module obtained from the PPI network contained 32 nodes and 78 edges. The red, orange, and yellow nodes representedtop 5 hub genes in the network.
4 International Journal of Genomics
3.3. PPI Network Construction and Significant ModuleAnalysis. The 99 identified DEGs were all filtered into thePPI network complex, which contained 99 nodes and 298edges (Figure 2(a)). Furthermore, the most significant mod-ule was extracted from the PI network, containing 32 nodesand 78 edges (Figure 2(b)). In this module, 5 nodes with adegree > 10 were identified as hub genes, including cyclin-dependent kinase (CDK1), cyclin B1 (CCNB1), cyclin B2(CCNB2), MAD2 mitotic arrest deficient-like 1 (MAD2L1),and topoisomerase IIα (TOP2A) (Table 3).
3.4. CDK1 Expression Validation and Prognostic Value inHCC. To further confirm the expression patterns of the 5hub genes in HCC, we obtained 4 datasets from the ONCO-MINE database to analyze the differential expressionbetween HCC tissues and normal tissues. As shown inFigures 3(a)–3(d), the expression of CDK1 was significantlyupregulated in HCC tissues compared with the normal con-trols in each dataset (all P < 0:05), and this difference was alsostatistically significant combined with the 4 datasets(P < 0:001, Figure 3(e)). Additionally, the mRNA expressionof CDK1 in HCC cells was also analyzed using the CCLEdatabase. The results shown in Figure 3(f) revealed that theCDK1 expression was also elevated in HCC cells. Further-more, the Kaplan-Meier survival curves were constructedbased on CDK1 expression in HCC patients. As shownin Figure 3(g), we considered that the high CDK1 expres-sion was associated with poor overall survival comparedwith the low CDK1 expression in HCC patients (log-rankP < 0:001). In addition, survival curves for HCC patientswith different tumor stages were also plotted, which showedthat the high CDK1 predicts poor overall survival in patientswith tumor stage 2 (log-rank P = 0:0016, Figure 3(i)) andtumor stage 3 (log-rank P = 0:013, Figure 3(j)). However,no significantly different survival times were observedbetween patients with high CDK1 expression levels andpatients with low CDK1 expression levels at tumor stage 1(log-rank P = 0:077, Figure 3(h)).
3.5. CCNB1 Expression Validation and Prognostic Value inHCC. According to the expression investigation, we observedthat the expression of CCNB1 was upregulated in both tis-sues and cells compared with the control tissues and cells(all P < 0:05, Figures 4(a)–4(e)). Furthermore, the prognosticvalue of CCNB1 was examined using the KM plotter. Asshown in Figure 4(f), we considered that patients with thehigh CCNB1 expression level had poor overall survival com-
pared with those with low CCNB1 expression level (log-rankP < 0:001). Moreover, the survival analysis for patients withdifferent tumor stage revealed that the high CCNB1 expres-sion level was associated with shorter survival time comparedwith the low CCNB1 expression level in HCC patients withtumor stage 1 (log-rank P = 0:0088, Figure 4(g)), tumor stage2 (log-rank P = 0:0071, Figure 4(h)), and tumor stage 3(log-rank P = 0:0048, Figure 4(i)).
3.6. CCNB2 Expression Validation and Prognostic Value inHCC. The expression patterns of CCNB2 in both HCCtissues and cells were greater than those in the normalcontrol tissues and cells (all P < 0:05, Figures 5(a)–5(e)).To investigate the clinical significance of CCNB2 in HCCprognosis, the KM plotter was used to plot the survivalcurves for HCC patients. Patients with low CCNB2expression had longer survival times compared with thosewith high CCNB2 expression (log-rank P = 0:0013,Figure 5(f)). Additionally, the effect of CCNB2 on overallsurvival of patients with different tumor stages were alsoassessed. The curves indicated that the high CCNB2expression was associated with shorter survival times com-pared with the low CCNB2 expression in HCC patientswith tumor stage 2 (log-rank P = 0:022, Figure 5(h)) andtumor stage 3 (log-rank P = 0:011, Figure 5(i)). However,no significantly different survival times were observedbetween patients with high CCNB2 expression levels andpatients with low CCNB2 expression levels at tumor stage1 (log-rank P = 0:073, Figure 5(g)).
3.7. MAD2L1 Expression Validation and Prognostic Valuein HCC. The expression of MAD2L1 was analyzed usingthe ONCOMINE database and the CCLE database andwas proved to be upregulated in HCC tissues and cellscompared with the control tissues and cells (all P < 0:05,Figures 6(a)–6(f)). Furthermore, the KM plotter was usedto plot survival curves based on MAD2L1 expression inHCC patients. As shown in Figure 6(g), HCC patientswith high MAD2L1 expression had poor overall survivalcompared with those with low MAD2L1 expression (log-rank P < 0:001). To explore the effect of MAD2L1 expres-sion on HCC tumors with different tumor stages, survivalanalysis was performed for patients with tumor stages 1-3.The results indicated that the high MAD2L1 expressionpredicted shorter survival times compared with the lowMAD2L1 expression in HCC patients with tumor stage1 (log-rank P = 0:0072, Figure 6(h)), tumor stage 2 (log-rank P = 0:022, Figure 6(i)), and tumor stage 3 (log-rankP = 0:0015, Figure 6(j)).
3.8. TOP2A Expression Validation and Prognostic Value inHCC. The expression of TOP2A was investigated using theONCOMINE database and the CCLE database, and theexpression level was observed to be elevated in both HCC tis-sues and cells compared with the normal control tissues andcells (all P < 0:05, Figures 7(a)–7(f)). The survival analysisindicated that HCC patients with high TOP2A expressionhad poor overall survival compared with those with lowTOP2A expression (log-rank P = 0:00012, Figure 7(g)).
Table 3: Top 5 hub genes in the PPI network.
Rank Gene name Score
1 CDK1 29
2 CCNB1 26
3 CCNB2 25
4 MAD2L1 24
5 TOP2A 23
PPI: protein-protein interaction.
5International Journal of Genomics
CDK1 expression in chen liverHepatocellular carcinoma vs. normal
0.5
P value: 6.41E-29
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t-test: 13.870Fold change: 4.148
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CDK1 expression in chen liverHepatocellular carcinoma vs. normal
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P value: 9.36E-103.02.5
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P value: 1.05E-84
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Comparison of CDK1 across 4 analysesOver-expression
Median rank P value Gene61.0
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The rank for a gene is the median rank for that gene acrosseach of the analyses.The P value for a gene is its P value for the median-rankedanalysis.
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1 2 3 4CDK12.87E-10
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Figure 3: Continued.
6 International Journal of Genomics
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0 20 40 60 80 100 120
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Prob
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Number at risk258 138 65 33 13 5 0low106 44 19 9 6 1 1high
HR = 2.15 (1.52 − 3.06)logrank P = 1.1e−05
(g)
ExpressionLowHigh
0 20 40 60 80 100 120
0.0
0.2
0.4
0.6
0.8
1.0
Time (months)
Prob
abili
ty
Number at risk127 68 36 17 7 2 0low43 23 15 8 2 0 0high
HR = 1.74 (0.93 − 3.25)logrank P = 0.077
(h)
Figure 3: Continued.
7International Journal of Genomics
Moreover, we also found that the high TOP2A expressionpredicted poor overall survival compared with the lowTOP2A expression in HCC patients with tumor stage 2(log-rank P = 0:0073, Figure 7(i)) and tumor stage 3 (log-rank P = 0:00066, Figure 7(j)). In HCC patients with tumorstage 1, we observed that the trend of the high TOP2Aexpression was related to a shorter survival time, butthe difference was not statistically significant (log-rank P= 0:1, Figure 7(h)).
3.9. Expression and Prognostic Value Verification UsingTCGA Data. By using the GEPIA, the expression patternsand prognostic value of the 5 hub genes were verifiedbased on the data from TCGA database. Consistent withthe expression results analyzed by ONCOMINE, theexpression levels of CDK1, CCNB1, CCNB2, MAD2L1,and TOP2A assessed by TCGA data were all upregulatedin tumor tissues compared with the normal controls (allP < 0:05, Figure 8(a)). Furthermore, the survival curvesshown in Figure 8(b) indicated that high CDK1, CCNB1,MAD2L1, and TOP2A expression predicted poor overallsurvival (all log-rank P < 0:05). Although high expressionof CCNB2 was also associated with shorter survival time,the difference of survival distribution between high and
low expression groups was not statistically significant(log-rank P = 0:052).
4. Discussion
Accurate diagnosis and prognosis remain the great chal-lenges for the improvement of HCC outcomes. To meet theclinical requirements of HCC treatment, various therapeu-tic methods have been developed in recent decades [28].Moreover, targeted therapy, which is mainly dependenton genes that have pivotal roles during tumor pathogene-sis, has attracted increasing attention [29]. These key genesare involved in tumor progression and typically have con-siderable clinical significance in the diagnosis and progno-sis of various human cancers, including HCC. Ba andcolleagues have reported that the serum expression ofGolgi protein-73 (GP73) was higher in HCC patients com-pared with healthy individuals and serves as a novel bio-marker for HCC diagnosis [30]. Similarly, upregulatedexpression of lysine specific demethylase 1 (LSD1) hasbeen proven to be associated with poor prognosis ofHCC [31]. To identify novel key genes that might beinvolved in HCC pathogenesis, we performed a systematicanalysis of 3 expression profiles from GEO database using
ExpressionLowHigh
0 20 40 60 80 100
0.0
0.2
0.4
0.6
0.8
1.0
Time (months)
Prob
abili
ty
Number at risk56 29 13 7 5 2Low27 11 3 2 1 0High
HR = 3.35 (1.51 − 7.43)logrank P = 0.0016
(i)
ExpressionLowHigh
0 20 40 60 80 100 120
0.0
0.2
0.4
0.6
0.8
1.0
Time (months)
Prob
abili
tyNumber at risk
40 19 8 4 1 1 0Low43 14 6 3 3 1 1High
HR = 2.14 (1.15 − 3.95)logrank P = 0.013
(j)
Figure 3: Expression and prognostic value of CDK1 in HCC. (a–e) The expression data collected from 4 datasets from ONCOMINEindicated that CDK1 expression was upregulated in HCC tissues compared with the normal control tissues and cells (all P < 0:05). (f)Expression of CDK1 was increased in HCC cells based on the data from CCLE. (g) The Kaplan-Meier survival curves revealed that thehigh CDK1 expression predicted worse overall survival compared with the low CDK1 expression in HCC patients (log-rank P < 0:001).(h) No significantly different survival times were observed between patients with high CDK1 expression and patients with low CDK1expression at tumor stage 1 (log-rank P = 0:077). (i) High CDK1 expression predicted worse overall survival compared with the lowCDK1 expression in HCC at tumor stage 2 (log-rank P = 0:0016). (j) High CDK1 expression predicted worse overall survival comparedwith the low CDK1 expression in HCC patients at tumor stage 3 (log-rank P = 0:013).
8 International Journal of Genomics
log2
med
ian-
cent
ered
inte
nsity 3.0
2.5
1.5
0.5
–1.0
1.0
0.0–0.5
–1.5
2.0
3.5
P value: 5.31E-8t-test: 7.428
Fold change: 3.901
1. Liver (21)2. Hepatocellular carcinoma (22)
1 2
CCNB1 expression in roessler liverHepatocellular carcinoma vs. normal
(a)
log2
med
ian-
cent
ered
inte
nsity P value: 3.45E-88
Fold change: 5.783t-test: 30.468
1. Liver (220)2. Hepatocellular carcinoma (225)
1 2
CCNB1 expression in roessler liver 2Hepatocellular carcinoma vs. normal
4.04.5
3.02.5
1.5
0.5
–1.0
1.0
0.0–0.5
–1.5
2.0
3.5
(b)
log2
med
ian-
cent
ered
inte
nsity P value: 6.06E-14
t-test: 10.729Fold change: 10.827
1. Liver (10)2. Hepatocellular carcinoma (35)
1 2
CCNB1 expression in wurmbach liverHepatocellular carcinoma vs. normal
4.04.04.55.05.56.06.57.0
3.02.51.50.5
–1.0
1.00.0
–0.5
2.0
3.5
(c)
Comparison of CCNB1 across 3 analysesOver-expression
Median rank P value Gene25.0
Legend
Roessler liver, cancer res, 2010
Roessler Liver 2, Cancer Res, 2010
1. Heparocellular carcinoma vs. normal
1
The rank for a gene is the median rank for that gene acrosseach of the analyses.The P value for a gene is its P value for the median-rankedanalysis.
5 5 1Not measured
10 1025 25
2. Heparocellular carcinoma vs. normal
Wurmbach Liver, Hepatology, 2010
3. Heparocellular carcinoma vs. normal
1 2 3CCNB13.45E-88
%
(d)
mRN
A ex
pres
sion
leve
l (RM
A, l
og2)
12
CCNB1-Entrez ID: 891
11
10
8
7
9
Soft_
tissu
e (21
)En
dom
etriu
m (2
7)M
esot
helio
ma (
11)
Panc
reas
(44)
Oste
osar
com
a (10
)U
pper
_aer
odig
estiv
e (32
)T-
cell_
ALL
(16)
Urin
ary_
trac
t (27
)O
vary
(51)
Live
r (28
)Iu
ng_N
SC (1
31)
Glio
ma (
62)
Ewin
gs_s
arco
ma (
12)
Col
orec
tal (
61)
Iym
phom
a_ot
her (
28)
Pros
tate
(7)
Lung
_sm
all_
cell
(53)
Men
ingi
oma (
3)Br
est (
58)
Iym
phom
a_D
LBCL
(18)
Med
ullo
blas
tom
a (4)
Esop
hagu
s (25
)Th
yroi
d (1
2)Iy
mph
oma_
burk
itt (1
1)St
omac
h (3
8)Ch
ondr
osar
com
a (4)
Mela
nom
a (61
)N
euro
blas
tom
a (17
)Iy
mph
oma_
hodg
kin
(12)
Kidn
ey (3
4)M
ultip
le_m
yelo
ma (
30)
Oth
er (1
5)Bi
le_d
uct (
8)B-
cell_
ALL
(15)
AM
L (3
4)CM
L (1
5)Le
unem
ia_o
ther
(1)
(e)
Figure 4: Continued.
9International Journal of Genomics
0 20 40 60 80 100 120
0.0
0.2
0.4
0.6
0.8
1.0
Time (months)
Prob
abili
ty
Number at risk124 79 36 19 7 2 0Low240 103 48 23 12 4 1High
HR = 2.34 (1.55 − 3.54)logrank P = 3.4e−05
ExpressionLowHigh
(f)
0 20 40 60 80 100 120
0.0
0.2
0.4
0.6
0.8
1.0
Time (months)
Prob
abili
tyNumber at risk
67 39 23 13 6 1 0Low103 52 28 12 3 1 0High
HR = 2.52 (1.23 − 5.14)logrank P = 0.0088
ExpressionLowHigh
(g)
0 20 40 60 80 100
0.0
0.2
0.4
0.6
0.8
1.0
Time (months)
Prob
abili
ty
Number at risk35 21 9 4 3 1Low48 19 7 5 3 1High
HR = 3.27 (1.31 − 8.17)logrank P = 0.0071
ExpressionLowHigh
(h)
0 20 40 60 80 100 120
0.0
0.2
0.4
0.6
0.8
1.0
Time (months)
Prob
abili
ty
Number at risk20 14 5 3 0 0 0Low63 19 9 4 4 2 1High
HR = 3.26 (1.37 − 7.76)logrank P = 0.0048
ExpressionLowHigh
(i)
Figure 4: Expression and prognostic value of CCNB1 in HCC. (a–d) The expression data collected from 3 datasets from ONCOMINEindicated that CCNB1 expression was upregulated in HCC tissues compared with the normal controls (all P < 0:05). (e) Expression ofCCNB1 was increased in HCC cells based on the data from CCLE. (f) The Kaplan-Meier survival curves revealed that the high CCNB1expression predicted worse overall survival compared with the low CCNB1 expression in HCC patients (log-rank P < 0:001). (g) HighCCNB1 expression predicted worse overall survival compared with the low CCNB1 expression in HCC patients at tumor stage 1 (log-rankP = 0:0088). (h) High CCNB1 expression predicted worse overall survival compared with the low CCNB1 expression in HCC patients attumor stage 2 (log-rank P = 0:0071). (i) High CCNB1 expression predicted worse overall survival compared with the low CCNB1expression in HCC patients at tumor stage 3 (log-rank P = 0:0048).
10 International Journal of Genomics
log2
med
ian-
cent
ered
inte
nsity 3.0
2.5
1.5
0.5
–1.0
1.0
0.0–0.5
–1.5
2.0
3.5
P value: 5.31E-8t-test: 7.428
Fold change: 3.901
1. Liver (21)2. Hepatocellular carcinoma (22)
1 2
CCNB2 expression in roessler liverHepatocellular carcinoma vs. normal
(a)
log2
med
ian-
cent
ered
inte
nsity
3.02.5
1.5
0.5
–1.0
1.0
0.0–0.5
–1.5
2.0
3.5
P value: 4.51E-61t-test: 21.273
Fold change: 2.669
1. Liver (21)2. Hepatocellular carcinoma (22)
1 2
CCNB2 expression in roessler liver 2Hepatocellular carcinoma vs. normal
(b)
log2
med
ian-
cent
ered
inte
nsity 6.0
5.55.04.54.03.53.02.5
1.5
0.51.0
0.0
2.0
6.5
P value: 1.29E-9t-test: 7.847
Fold change: 4.744
1. Liver (10)2. Hepatocellular carcinoma (35)
1 2
CCNB2 expression in wurmbach liverHepatocellular carcinoma vs. normal
(c)
Comparison of CCNB2 across 3 analysesOver-expression
Median rank P value Gene137.0
Legend
Roessler liver, cancer res, 2010
Roessler Liver 2, Cancer Res, 2010
1. Heparocellular carcinoma vs. normal
1
The rank for a gene is the median rank for that gene acrosseach of the analyses.The P value for a gene is its P value for the median-rankedanalysis.
5 5 1Not measured
10 1025 25
2. Heparocellular carcinoma vs. normal
Wurmbach Liver, Hepatology, 2010
3. Heparocellular carcinoma vs. normal
1 2 3CCNB21.22E-9
%
(d)
mRN
A ex
pres
sion
leve
l (RM
A, l
og2)
12
13CCNB2-Entrez ID: 9133
11
10
8
9
Soft_
tissu
e (21
)O
steos
arco
ma (
10)
Lung
_sm
all_
cell
(53)
Neu
robl
asto
ma (
17)
B-ce
ll_A
LL (1
5)T-
cell_
ALL
(16)
Urin
ary_
trac
t (27
)Le
ukem
ia_o
ther
(27)
CML
(15)
Upp
er_a
erod
iges
tive (
32)
Iym
phom
a_D
LBCL
(18)
Glio
ma (
62)
Ewin
gs_s
arco
ma (
12)
Iym
phom
a_ot
her (
28)
Panc
reas
(44)
Men
ingi
oma (
11)
Col
orec
tal (
61)
Iym
phom
a_ho
dgki
n (1
2)Bi
le_d
uct (
8)Lu
ng_N
SC (1
31)
Endo
met
rium
(27)
Esop
hagu
s (25
)Th
yroi
d (1
2)
Iym
phom
a_bu
rkitt
(11)
Mul
tiple
_mye
lom
a (30
)Ch
ondr
osar
com
a (4)
Brea
st (5
8)A
ML
(34)
Ova
ry (5
1)M
elani
ma (
61)
Stom
ach
(38)
Live
r (28
)Ki
dney
(34)
Chon
dros
arco
ma (
4)M
enin
giom
a (3)
Oth
er (1
5)M
edul
lobl
asto
ma (
4)
(e)
Figure 5: Continued.
11International Journal of Genomics
0 20 40 60 80 100 120
0.0
0.2
0.4
0.6
0.8
1.0
Time (months)
Prob
abili
ty
Number at risk122 72 33 17 7 2 0Low242 110 51 25 12 4 1High
HR = 1.91 (1.28 − 2.87)logrank P = 0.0013
ExpressionLowHigh
(f)
0 20 40 60 80 100 120
0.0
0.2
0.4
0.6
0.8
1.0
Time (months)
Prob
abili
tyNumber at risk
65 36 20 10 5 1 0Low105 55 31 15 4 1 0High
HR = 1.86 (0.93 − 3.71)logrank P = 0.073
ExpressionLowHigh
(g)
0 20 40 60 80 100
0.0
0.2
0.4
0.6
0.8
1.0
Time (months)
Prob
abili
ty
Number at risk29 18 8 3 2 1Low54 22 8 6 4 1High
HR = 2.97 (1.12 − 7.88)logrank P = 0.022
ExpressionLowHigh
(h)
0 20 40 60 80 100 120
0.0
0.2
0.4
0.6
0.8
1.0
Time (months)
Prob
abili
ty
Number at risk47 23 9 4 1 1 0Low36 10 5 3 3 1 1High
HR = 2.13 (1.17 − 3.87)logrank P = 0.011
ExpressionLowHigh
(i)
Figure 5: Expression and prognostic value of CCNB2 in HCC. (a–d) The expression data collected from 3 datasets from ONCOMINEindicated that CCNB2 expression was upregulated in HCC tissues compared with the normal controls (all P < 0:05). (e) Expression ofCCNB2 was increased in HCC cells based on the data from CCLE. (f) The Kaplan-Meier survival curves revealed that the high CCNB2expression predicted worse overall survival compared with the low CCNB2 expression in HCC patients (log-rank P = 0:0013). (g) Nosignificantly different survival times were observed between patients with high CDK1 expression and patients with low CDK1 expressionat tumor stage 1 (log-rank P = 0:073). (h) High CCNB2 expression predicted worse overall survival compared with the low CCNB2expression in HCC patients at tumor stage 2 (log-rank P = 0:022). (i) High CCNB2 expression predicted worse overall survival comparedwith the low CCNB2 expression in HCC patients at tumor stage 3 (log-rank P = 0:011).
12 International Journal of Genomics
0.0–0.5–1.0–1.5–2.0–2.5–3.0–3.5–4.0–4.5–5.0–5.5–6.0
–7.0–6.5lo
g2 m
edia
n-ce
nter
ed in
tens
ity
P value: 6.77E-20t-test: 10.263
Fold change: 2.430
1 2
MAD2L1 expression in Chen liverHepatocellular carcinoma vs. normal
1. Liver (75)2. Hepatocellular carcinoma (103)
(a)
3.02.52.01.51.00.50.0
–0.5–1.0–1.5–2.0
log2
med
ian-
cent
ered
inte
nsity P value: 2.40E-7
t-test: 6.718Fold change: 2.548
1 2
MAD2L1 expression in Roessler liverHepatocellular carcinoma vs. normal
1. Liver (21)2. Hepatocellular carcinoma (22)
(b)
3.03.54.0
2.52.01.51.00.50.0
–0.5–1.0–1.5–2.0
log2
med
ian-
cent
ered
inte
nsity P value: 3.63E-56
t-test: 20.301Fold change: 2.884
1 2
MAD2L1 expression in Roessler liver 2Hepatocellular carcinoma vs. normal
1. Liver (220)2. Hepatocellular carcinoma (225)
(c)
3.03.54.04.5
2.52.01.51.00.50.0
–0.5–1.0–1.5–2.0
log2
med
ian-
cent
ered
inte
nsity P value: 3.52E-6
t-test: 5.131Fold change: 3.669
1 2
MAD2L1 expression in Wurmbach liver 2Hepatocellular carcinoma vs. normal
1. Liver (10)2. Hepatocellular carcinoma (35)
(d)
Comparison of MAD2L1 across 4 analysesOver-expression
Median Rank
1 2 3 4
P value Gene394.5 1.20E-7 MAD2L1
Legend
Hepatocellular carcinoma vs.normalChen Liver, Mol Biol Cell, 2002
1.
Hepatocellular carcinoma vs.normalRoessler Liver, Cancer Res, 2010
2. Hepatocellular Carcinoma vs.normalWumbach liver, Hepatology, 2007
4.
Hepatocellular carcinoma vs.normalRoessler Liver 2, Cancer Res, 2010
3.
The rank for a gene is the median rank for that gene acrosseach of the analyses.The P value for a gene is its P value for the median-rankedanalysis.
Not measured1 15 510 1025 25
%
(e)
Figure 6: Continued.
13International Journal of Genomics
12
11
10
9
8
mRN
A ex
pres
sion
leve
l (RM
A, l
og2)
MAD2L1 - Entrez ID: 4058
T-ce
ll_A
ll (1
6)B-
cell_
All
(15)
Neu
robl
asto
ma (
17)
Lym
phom
a_bu
rkitt
(11)
Lym
phom
a_ot
her (
28)
Lym
phom
a_D
LBCL
(18)
Mul
tiple
_mye
lom
a (30
)En
dom
etriu
m (2
7)Lu
ng_s
mal
l_ce
ll (5
3)CM
L (1
5)Pr
osta
te (7
)Ly
mph
oma_
hodg
kin
(12)
AM
L (3
4)O
steos
arco
ma (
10)
Col
orec
tal (
61)
Soft_
tissu
re (2
1)St
omac
h (3
8)M
enin
giom
a (3)
Esop
hagu
s (25
)Lu
ng)N
SC (1
31)
Gilo
ma (
62)
Thyr
oid
(12)
Ova
ry (5
1)Le
ukem
ia_o
ther
(1)
Panc
reas
(44)
Mes
othe
liom
a (11
)Li
ver (
28)
Ewin
gs_s
arco
ma (
12)
Brea
st (5
8)U
rinar
y_tr
act (
27)
Mela
nom
a (61
)Bi
le_d
uct (
8)Ki
dney
(34)
Med
ullo
blas
tom
a (4)
Upp
er_a
erod
iges
tive (
32)
Oth
er (1
5)Ch
ondr
osar
com
a (4)
(f)
0 20 40 60 80 100 120
0.0
0.2
0.4
0.6
0.8
1.0
Time (months)
Prob
abili
ty
Number at risk152 83 49 26 10 4 0Low212 99 35 16 9 2 1High
HR = 2.25 (1.54 − 3.28)logrank P = 1.7e−05
ExpressionLowHigh
(g)
0 20 40 60 80 100 120
0.0
0.2
0.4
0.6
0.8
1.0
Time (months)
Prob
abili
ty
Number at risk70 36 25 16 6 2 0Low
100 55 26 9 3 0 0High
HR = 2.55 (1.26 − 5.16)logrank P = 0.0072
ExpressionLowHigh
(h)
Figure 6: Continued.
14 International Journal of Genomics
bioinformatics analysis. The DEGs in the expression pro-files and the prognostic value of the key genes wereassessed in the present study.
A total of 68 HCC samples and 57 normal controlsamples were included in the 3 expression profiles, and99 DEGs were screened for further analyses. Accordingto the functional and pathway enrichment analysis, theidentified DEGs were shown to be enriched in biologicalprocesses that related to cell division and mitotic nucleardivision and in signaling pathways that associated with cellcycle and mineral absorption. It is generally consideredthat cell division, mitotic nuclear division, and cell cycleare important cell processes in both normal and tumorcells [32]. Tumor-related key genes are typically involvedin tumor progression by the regulation of these cell pro-cesses [33, 34].
Two interesting results were presented in our study.Firstly, the 99 DEGs included 6 members of the cytochromeP450 proteins (CYPs), including CYP1A2, CYP26A1,CYP2B6, CYP2C9, CYP2C19, and CYP2C18. CYPs repre-sent a large group of enzymes with critical roles in themolecular metabolism [35]. They act critical roles in thedevelopment of various human cancers, including HCC,and mediate the metabolism of most of the procarcinogens[36]. The members of CYPs in our study were found to be
downregulated in HCC tissues, indicating that the CYPswere involved in the progression of HCC and mightinhibit the drug sensitivity. Secondly, the DEGs werefound to be enriched in the mineral absorption pathwayin this study. A previous research has demonstrated thatmineral supplementation could improve the status ofessential trace elements in biological samples collectedfrom patients with liver cirrhosis and cancer [37]. There-fore, we speculated that the mineral absorption pathwaymight have effects on the maintenance of HCC tumormicroenvironment, which needs to be analyzed and con-firmed in future studies.
Furthermore, the PPI network of the DEGs was con-structed, and 5 hub genes were extracted from a significantmodule, including CDK1, CCNB1, CCNB2, MAD2L1, andTOP2A. A study by Xing et al. [38] also focused on the DEGsin HCC tissues compared with the normal controls, and asame expression profile GSE45267 was analyzed and CCNB2and TOP2A were identified as two of the hub genes, whichwas consistent with our corresponding data. Furthermore,the expression patterns of the 5 hub genes were found allupregulated in HCC tissues and cells compared with the nor-mal controls, and their prognostic value was evaluated byplotting the Kaplan-Meier survival curves. The analysisresults indicated that the expression levels of CDK1, CCNB1,
0 20 40 60 80 100
0.0
0.2
0.4
0.6
0.8
1.0
Time (months)
Prob
abili
ty
Number at risk30 19 10 5 3 1Low53 21 6 4 3 1High
HR = 2.81 (1.12 − 7.04)logrank P = 0.022
ExpressionLowHigh
(i)
0 20 40 60 80 100 120
0.0
0.2
0.4
0.6
0.8
1.0
Time (months)
Prob
abili
tyNumber at risk
54 24 12 7 4 2 1Low29 9 2 0 0 0 0High
HR = 2.66 (1.42 − 4.98)logrank P = 0.0015
ExpressionLowHigh
(j)
Figure 6: Expression and prognostic value of MAD2L1 in HCC. (a–e) The expression data collected from 4 datasets from ONCOMINEindicated that MAD2L1 expression was upregulated in HCC tissues compared with the normal controls (all P < 0:05). (f) Expression ofMAD2L1 was increased in HCC cells based on the data from CCLE. (g) The Kaplan-Meier survival curves revealed that the highMAD2L1 expression predicted worse overall survival compared with the low MAD2L1 expression in HCC patients (log-rank P < 0:001).(h) High MAD2L1 expression predicted worse overall survival compared with the low MAD2L1 expression in patients with HCC attumor stage 1 (log-rank P = 0:0072). (i) High MAD2L1 expression predicted worse overall survival compared with the low MAD2L1expression in HCC patients at tumor stage 2 (log-rank P = 0:022). (j) High MAD2L1 expression predicted worse overall survivalcompared with the low MAD2L1 expression in patients with HCC at tumor stage 3 (log-rank P = 0:0015).
15International Journal of Genomics
0.50.0
–0.5–1.0–1.5–2.0–2.5–3.0–3.5–4.0–4.5–5.0–55
–5.5
log2
med
ian-
cent
ered
inte
nsity
P value: 5.34E-13t-test: 10.968
Fold change: 2.663
1 2
TOP2A expression in Chen liverHepatocellular carcinoma vs. normal
1. Liver (76)2. Hepatocellular carcinoma (103)
(a)
3.03.54.04.55.0
2.52.01.51.00.50.0
–0.5–1.0–1.5
log2
med
ian-
cent
ered
inte
nsity
P value: 1.93E-13t-test: 10.641
Fold change: 11.236
1 2
TOP2A expression in roessler liverHepatocellular carcinoma vs. normal
1. Liver (21)2. Hepatocellular carcinoma (22)
(b)
3.03.54.04.55.05.5
2.52.01.51.00.50.0
–0.5–1.0–1.5
log2
med
ian-
cent
ered
inte
nsity
P value: 2.03E-85t-test: 25.481
Fold change: 8.298
1 2
TOP2A expression in roessler liver 2Hepatocellular carcinoma vs. normal
1. Liver (220)2. Hepatocellular carcinoma (225)
(c)
8.07.57.06.56.05.55.04.54.03.53.02.52.01.51.00.50.0
–0.5–1.0
log2
med
ian-
cent
ered
inte
nsity P value: 7.20E-13
t-test: 9.859Fold change: 13.321
1 2
TOP2A expression in wurmbach liverHepatocellular carcinoma vs. normal
1. Liver (10)2. Hepatocellular carcinoma (35)
(d)
Comparison of TOP2A across 4 analysesOver-expression
Median rank
1 2 3 4
P value Gene25.0 3.60E-13 TOP2A
Legend
Hepatocellular carcinoma vs.normalChen Liver, Mol Biol Cell, 2002
1.
Hepatocellular carcinoma vs.normalRoessler Liver, Cancer Res, 2010
2. Hepatocellular carcinoma vs.normalWumbach liver, Hepatology, 2007
4.
Hepatocellular carcinoma vs.normalRoessler Liver 2, Cancer Res, 2010
3.
The rank for a gene is the median rank for that gene acrosseach of the analyses.The P value for a gene is its P value for the median-rankedanalysis.
Not measured1 15 510 1025 25
%
(e)
Figure 7: Continued.
16 International Journal of Genomics
13
12
11
10
9
8
7
mRN
A ex
pres
sion
leve
l (RM
A, l
og2)
TOP2A - Entrez ID: 7153
T-ce
ll_A
ll (1
6)B-
cell_
All
(15)
Lung
_sm
all_
cell
(53)
Med
ullo
blas
tom
a (4)
Neu
robl
asto
ma (
17)
Lym
phom
a_bu
rkitt
(11)
Oste
osar
com
a (10
)Ly
mph
oma_
DLB
CL (1
8)Th
yroi
d (1
2)Pa
ncre
as (4
4)A
ML
(34)
CML
(15)
Leuk
emia
_oth
er (1
)Lu
ng_N
SC (1
31)
Esop
hagu
s (25
)sto
mac
h (3
8)Ly
mph
oma_
hodg
kin
(12)
Lym
phom
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28)
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ver (
28)
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t (27
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dom
etriu
m (2
7)G
liom
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ings
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a (12
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le_d
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8)M
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11)
Mul
tiple
_mye
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er (1
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e (32
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olor
ecta
l (61
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a (4)
Pros
tate
(7)
Kidn
ey (3
4)M
enin
giom
a (3)
(f)
0 20 40 60 80 100 120
0.0
0.2
0.4
0.6
0.8
1.0
Time (months)
Prob
abili
ty
Number at risk270 141 67 35 15 5 0Low94 41 17 7 4 1 1High
HR = 1.99 (1.39 − 2.86)logrank P = 0.00012
ExpressionLowHigh
(g)
0 20 40 60 80 100 120
0.0
0.2
0.4
0.6
0.8
1.0
Time (months)
Prob
abili
ty
Number at risk70 40 25 12 6 2 0Low 100 51 26 13 3 0 0High
HR = 1.7 (0.89 − 3.24)logrank P = 0.1
ExpressionLowHigh
(h)
Figure 7: Continued.
17International Journal of Genomics
CCNB2, MAD2L1, and TOP2A were associated with theoverall survival of HCC patients. Additionally, the relation-ships between the hub genes and overall survival of HCCcases at different tumor stages were also observed, suggestingthat these genes might serve as promising prognostic bio-markers in HCC.
CDK1 belongs to a serine/threonine kinase family andserves as a critical cell cycle-regulating protein. It has beenwidely investigated in human malignancies and has beenfound to be involved in tumor progression. In epithelialovarian cancer, upregulated expression of CDK1 has beenobserved in cancer cells and promotes cancer growth andhas a significant effect on the overall survival of patients[39]. In addition, a study scheduled by Luo et al. revealed thatCDK1 had comprehensive effects on gene interaction net-works in the tumor progression of cervical cancer and thusindicated the potential role of CDK1 as a therapeutic target[40]. In HCC, the aberrant CDK1 expression could regulatethe apoptin-induced apoptosis with a pivotal role in tumorprogression [41]. We also found the increased CDK1 expres-sion in HCC samples and proved its prognostic value for can-cer patients. The molecular mechanisms underlying the roleof CDK1 in human cancers await more research.
CCNB1 and CCNB2 are two important cyclins that areclosely correlated with the cell cycle and cell growth. Overex-
pression of CCNB1 and CCNB2 has been observed in somehuman cancer samples, and CCNB1 and CCNB2 possessclinical significance in the diagnosis and prognosis of variouscancers, such as lung cancer [42] and pancreatic cancer [43].Our study also showed the upregulated expression of CCNB1and CCNB2 in both HCC tissues and cells and reported theirprognostic value for the patients. However, the clinical signif-icance verification using TCGA data showed that the differ-ence between the survival distributes of low and highCCNB2 expression groups was not statistically significant,which might be due to the limited sample size and the incom-plete survival information. Thus, although CCNB1 andCCNB2 have been previously reported to act as therapeutictarget genes in HCC [44, 45], the clinical significance ofCCNB1 and CCNB2 needs to be investigated in cancer-related research.
MAD2L1 plays an important role in spindle checkpointsduring mitosis. Dysregulation of MAD2L1 induces theinstability of chromosomes and chromosomal aneuploidy,which are common events in cancer [46]. It has beendetermined as a useful prognostic biomarker in some cancers,such as breast cancer [47] and lung adenocarcinoma [48]. Anincreased expression level of MAD2L1 was observed in HCCsamples in the present study, which was consistent with theresults from a study by Li et al. [49], which also found the
0 20 40 60 80 100
0.0
0.2
0.4
0.6
0.8
1.0
Time (months)
Prob
abili
ty
Number at risk51 26 12 6 4 2Low32 14 4 3 2 0High
HR = 2.86 (1.28 − 6.38)logrank P = 0.0073
ExpressionLowHigh
(i)
0 20 40 60 80 100 120
0.0
0.2
0.4
0.6
0.8
1.0
Time (months)
Prob
abili
tyNumber at risk
53 26 11 6 3 1 0Low30 7 3 1 1 1 1High
HR = 2.71 (1.49 − 4.92)logrank P = 0.00066
ExpressionLowHigh
(j)
Figure 7: Expression and prognostic value of TOP2A in HCC. (a–e) The expression data collected from 4 datasets from ONCOMINEindicated that TOP2A expression was upregulated in HCC tissues compared with the normal controls (all P < 0:05). (f) Expression ofTOP2A was increased in HCC cells based on the data from CCLE. (g) The Kaplan-Meier survival curves revealed that the high TOP2Aexpression predicted worse overall survival compared with the low TOP2A expression in HCC patients (log-rank P = 0:00012). (h) Nosignificantly different survival times were found between patients with high TOP2A and patients with low TOP2A at tumor stage 1 (log-rank P = 0:1). (i) High TOP2A expression predicted worse overall survival compared with the low TOP2A expression in HCC patients attumor stage 2 (log-rank P = 0:0073). (j) High TOP2A expression predicted worse overall survival compared with the low TOP2Aexpression in patients with HCC at tumor stage 3 (log-rank P = 0:00066).
18 International Journal of Genomics
overexpression of MAD2L1 in HCC. Collectively, the role ofMAD2L1 in cancer pathogenesis and the related molecularmechanisms need to be assessed with in-depth studies.
TOP2A is an enzyme that is closely correlated with DNAreplication, recombination, transcription, and chromatinremodeling [50]. The functional and clinical roles of TOP2Ahave been demonstrated in human cancers, including pros-tate cancer [51], breast cancer [52], and nasopharyngeal car-cinoma [53]. This study showed elevated TOP2A expressionlevels in HCC tissues and cells and demonstrated its potentialas a prognostic biomarker in this malignancy. The upregula-tion of TOP2A and its prognostic value have been reported inHCC in previous studies, which also revealed its correlationwith tumor onset and chemoresistance [54]. Further studiesshould be carried out to explore the mechanisms underlyingthe role of TOP2A during cancer pathogenesis.
5. Conclusion
In conclusion, this study identified 99 DEGs from 3 expres-sion profiles by integrated bioinformatics analysis. TheseDEGs may contain key genes involved in HCC pathogenesis.In addition, CDK1, CCNB1, CCNB2, MAD2L1, and TOP2Awere the top five hub genes and serve as candidate prognosticbiomarkers in HCC. The results of this study further enrich
the number of key genes that may be involved in the patho-genesis of HCC and give in silico evidence for the key genesin the prognosis of HCC. However, our study fails to evaluatethe clinical significance and biological function of the keygenes in tumor samples by in vitro and in vivo analyses. Thus,further studies are needed to confirm the prognostic valueand functional roles of these key genes in HCC.
Data Availability
The data used to support the findings of this study areincluded within the article.
Conflicts of Interest
The authors declare that there are no conflicts of interestregarding the publication of this paper.
Authors’ Contributions
Meng Wang, Licheng Wang, and Shusheng Wu contributedequally to this work.
0
2
4
6
LIHC(num (T) = 369;num (N) = 160)
0
1
2
3
4
5
LIHC(num (T) = 369;num (N) = 160)
0
1
2
3
4
5
6
LIHC(num (T) = 369;num (N) = 160)
0
2
4
6
8
LIHC(num (T) = 369;num (N) = 160)
0
2
4
6
LIHC(num (T) = 369;num (N) = 160)
⁎ ⁎ ⁎ ⁎⁎
(a)
0 20 40 60 80 100 120
0.0
0.2
0.4
0.6
0.8
1.0Overall survival
Months
Perc
ent s
urvi
val
Low CDK1 TPMHigh CDK1 TPM
Low CCNB1 TPMHigh CCNB1 TPM
Low CCNB2 TPMHigh CCNB2 TPM
Low MAD2L1 TPMHigh MAD2L1 TPM
Low TOP2A TPMHigh TOP2A TPM
Logrank p = 0.00017n (high) = 182
n (low) =182
0 20 40 60 80 100 120
0.0
0.2
0.4
0.6
0.8
1.0Overall survival
Months
Perc
ent s
urvi
val
Logrank p = 0.00015 n (high) = 182
n (low) = 182
0 20 40 60 80 100 120
0.0
0.2
0.4
0.6
0.8
1.0Overall survival
Months
Perc
ent s
urvi
val
Logrank p = 0.052 n (high) = 182
n (low) = 182
0 20 40 60 80 100 120
0.0
0.2
0.4
0.6
0.8
1.0Overall survival
MonthsPe
rcen
t sur
viva
l
Logrank p = 0.0047 n (high) = 181
n (low) = 181
0 20 40 60 80 100 120
0.0
0.2
0.4
0.6
0.8
1.0Overall survival
Months
Perc
ent s
urvi
val
Logrank p = 0.0028 n (high) = 182
n (low) = 182
(b)
Figure 8: Expression and prognostic value verification using TCGA data. (a) TCGA data indicated that the expression levels of CDK1,CCNB1, CCNB2, MAD2L1, and TOP2A were all increased in tumor samples compared with normal controls (all P < 0:05; T: tumor;N: normal). (b) The patients with high CDK1, CCNB1, CCNB2, MAD2L1, and TOP2A expression had poor overall survival comparedwith those with low expression of these genes (log-rank P < 0:05 for CDK1, CCNB1, MAD2L1, and TOP2A; log-rank P = 0:052 for CCNB2).
19International Journal of Genomics
Acknowledgments
This study was funded by the National Natural ScienceFoundation (81702752) and the Shandong Province NaturalScience Foundation (ZR2017LH008).
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