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

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t-test: 13.870Fold change: 4.148

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6 International Journal of Genomics

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(g)

ExpressionLowHigh

0 20 40 60 80 100 120

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Time (months)

Prob

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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

a_ot

her (

28)

Soft_

tissu

e (21

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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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