Identification and Verification of Biomarker in Clear Cell

Research ArticleIdentification and Verification of Biomarker in Clear Cell RenalCell Carcinoma via Bioinformatics and Neural Network Model

Bin Liu,1 Yu Xiao,2 Hao Li,3 Ai-li Zhang ,1 Ling-bing Meng ,4 Lu Feng,5 Zhi-hong Zhao,1

Xiao-chen Ni,1 Bo Fan,1 Xiao-yu Zhang,1 Shi-bin Zhao,6 and Yi-bo Liu1

1Department of Urinary Surgery, The Fourth Hospital of Hebei Medical University, No. 12 Jiankang Road, 050000, China2School of Basic Medicine, Peking University, No. 38 Xueyuan Road, Haidian District, Beijing 100191, China3Department of Oncology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China4School of Basic Medical Sciences, Hebei Medical University, Shijiazhuang, Hebei, China5MOH Key Laboratory of Geriatrics, Beijing Hospital, National Center of Gerontology, Beijing, China6Department of Reproductive Medicine, The Fourth Hospital of Hebei Medical University, No. 12 Jiankang Road, 050000, China

Correspondence should be addressed to Ai-li Zhang; [email protected] and Ling-bing Meng; [email protected]

Received 20 December 2019; Revised 10 May 2020; Accepted 22 May 2020; Published 17 June 2020

Academic Editor: Paul Harrison

Copyright © 2020 Bin Liu et al. This is an open access article distributed under the Creative Commons Attribution License, whichpermits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Background. Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer, which represents the 9th mostfrequently diagnosed cancer. However, the molecular mechanism of occurrence and development of ccRCC is indistinct. Therefore,the research aims to identify the hub biomarkers of ccRCC using numerous bioinformatics tools and functional experiments.Methods. The public data was downloaded from the Gene Expression Omnibus (GEO) database, and the differently expressedgenes (DEGs) between ccRCC and normal renal tissues were identified with GEO2R. Protein-protein interaction (PPI) networkof the DEGs was constructed, and hub genes were screened with cytoHubba. Then, ten ccRCC tumor samples and ten normalkidney tissues were obtained to verify the expression of hub genes with the RT-qPCR. Finally, the neural network model wasconstructed to verify the relationship among the genes. Results. A total of 251 DEGs and ten hub genes were identified. AURKB,CCNA2, TPX2, and NCAPG were highly expressed in ccRCC compared with renal tissue. With the increasing expression ofAURKB, CCNA2, TPX2, and NCAPG, the pathological stage of ccRCC increased gradually (P < 0:05). Patients with highexpression of AURKB, CCNA2, TPX2, and NCAPG have a poor overall survival. After the verification of RT-qPCR, theexpression of hub genes was same as the public data. And there were strong correlations between the AURKB, CCNA2, TPX2,and NCAPG with the verification of the neural network model. Conclusion. After the identification and verification, AURKB,CCNA2, TPX2, and NCAPG might be related to the occurrence and malignant progression of ccRCC.

1. Introduction

Worldwide, renal cell carcinoma (RCC) represents the 9thmost frequently diagnosed cancer in men and the 10th inwomen, accounting for 5% and 3% of all oncological diagno-ses, respectively, [1]. According to the most updated dataprovided by the World Health Organization, there are morethan 140 000 RCC-related deaths yearly, with RCC rankingas the 13th most common cause of cancer death worldwide.Age and gender factors are closely related to the risk ofRCC. Other potential risk factors include lifestyle, complica-tions, drugs, and environmental factors [2]. The diagnosis

and management of RCC have changed remarkably rapidlyin the past decades through the unremitting efforts of gener-ation after generation of researchers. Despite progression incancer control and survival, locally advanced disease and dis-tant metastases are still diagnosed in a notable proportion ofpatients. Nevertheless, uncertainties, controversies, andresearch questions remain [3]. Further advances are expectedfrom the diagnosis, treatment, and prognosis evaluation.

RCC is a group of heterogeneous tumors with differentgenetic and molecular changes, clear cell renal cell carcinoma(ccRCC), papillary RCC (type 1 and type 2), and chromo-phobe RCC are the most common solid RCC, accounting

HindawiBioMed Research InternationalVolume 2020, Article ID 6954793, 24 pageshttps://doi.org/10.1155/2020/6954793

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https://doi.org/10.1155/2020/6954793

for 85.90% of all malignant RCC [4]. Among them, ccRCC isthe most common subtype of kidney cancer. Both sporadicand inherited RCC are usually associated with structuralchanges in the short arm of chromosome 3 [5]. In addition,the occurrence of RCC is related to multiple gene alterations,such as VHL, PBRM1, BAP1, SETD2, TCEB1, and KDM5C[3]. Although our understanding of the biology of RCC hasimproved, surgery is still the main treatment method ofRCC. Drugs and comprehensive therapies, identification ofnew target pathways, and optimal sequencing and combina-tion of existing targeted drugs are areas that are worthresearching [6].

Bioinformatics tools can screen differentially expressedgenes (DEGs) between diseased and normal tissues [3, 7, 8].These DEGs are related to the pathological stage, lesiongrade, and prognosis of patients. Zou et al. used microarraytechnology to identify the hub genes between malignantglioblastoma and normal brain tissue and obtained theimportant targets related to brain glioma [9]. Through aseries of bioinformatics analysis, Meng et al. concluded thatTPM2 may be an important biomarker for the occurrenceand development of atherosclerosis [10].

Therefore, this study will use bioinformatics technologyto explore the gene molecular markers of abnormal expres-sion during the occurrence of ccRCC and discuss the relatedpotential mechanisms. These differentially expressed genesmay affect the initiation and malignant progression of ccRCCand can be used as targets for diagnosis and treatment.

2. Material and Methods

2.1. Download Public Data. The Gene Expression Omnibus(GEO) database (http://www.ncbi.nlm.nih.gov/geo) is thelargest, most comprehensive, and publicly available sourceof gene expression data.

On 20 December, 2019, we set key words “(clear cell renalcell carcinoma) AND (normal kidney)” to detect the datasets,using a filter of “expression profiling by array.” The inclusioncriteria includes a diagnosis of clear cell renal cell carcinoma(data from papillary renal cell carcinoma diagnoses wereexcluded), the dataset including the gene expression profileof normal kidney (datasets which were composed of onlytumor data were excluded), a sample number of more thanforty per dataset (samples of less than forty were excluded),data from Homo sapiens (data from other species wereexcluded), and a series entry type, expression profiling byarray (data using methylation profiling only by array wereexcluded).

Therefore, GSE105288 (GPL10558, Illumina HumanHT-12V4.0 expression beadchip) and GSE66272 (GPL570(HG-U133_Plus_2) Affymetrix Human Genome U133 Plus 2.0Array) were obtained from the GEO database. A total of 44samples, including 35 ccRCC tissues and 9 normal renal tis-sues, were selected from GSE105288. A total of 53 samples,including 26 ccRCC tissues and 27 normal renal tissues, wereselected from GSE66272.

2.2. Differentially Expressed Genes (DEGs) between Normaland PCRC. GEO2R (http://www.ncbi.nlm.nih.gov/geo/

geo2r) could import data of the GEO database into the R lan-guage and perform differential analysis, essentially throughthe following two R packages, including limma packagesand GEOquery. Therefore, through the GEO2R tool, DEGswere identified between the normal and ccRCC groups. TheP values < 0.001 was defined as significant. The gene symbolswere necessary. SangerBox (https://shengxin.ren), one opentool, was used to draw volcano maps. Venn diagrams weredelineated using FunRich software (http://www.funrich.org/),which would visualize common DEGs shared betweenGSE105288 and GSE66272.

2.3. GO and KEGG Analysis. One online tool, DAVID(https://david.ncifcrf.gov/home.jsp) (version 6.8, Maryland,America), was applied to carry out the functional annotationfor DEGs. Gene Ontology (GO) [11] generally performsenrichment analysis of genomes. And there are mainly cellu-lar components (CC), biological processes (BP), and molecu-lar functions (MF) in the GO analysis. Kyoto Encyclopedia ofGenes and Genomes (KEGG) (https://www.kegg.jp/) [12] is acomprehensive database of genomic, chemical, and systemicfunctional information. Therefore, DAVID was used to makethe analysis of GO and KEGG. The Biological NetworksGene Oncology tool (BiNGO) (version 3.0.3) was used toanalyze and visualize the DEGs’ cellular component, biolog-ical process, and molecular function [13].

2.4. Protein-Protein Interaction (PPI) Network. The commonDEGs, shared between GSE105288 and GSE66272, wereconverted into differently expressed proteins. The STRING(Search Tool for the Retrieval of Interacting Genes) online

Table 1: Primers and their sequences for PCR analysis.

Primer Sequence (5′–3′)VEGFA-hF GGCAACTTACTTAGCCTCTT

VEGFA-hR AGGACAGTCTGAGTATGGGT

AURKB-hF GTTCGCATTCAACCTACCT

AURKB-hR GACGCCCAATCTCAAAGT

CCNA2-hF AACTGGGATAAGGAAGCT

CCNA2-hR CAGAAAGTATTGGGTAAGAA

MCM2-hF TTCTCCCTCACTTGTCCC

MCM2-hR CCTGTAATCCCAGCACTTT

MCM7-hF GGGGTAGGCAGAACTCAA

MCM7-hR CATGGAAGCGGTCTCAAA

SMC4-hF AGTGGCGTAGCACAGTAA

SMC4-hR ATTCCAAGATGATCCCTC

TPX2-hF GCAATCCTTCTGCCTTAG

TPX2-hR AGACCATCCTGGCTAACA

SLC2A1-hF GAGACGGGAAACCATCAA

SLC2A1-hR CTGCTCCTTCTTCAAACCAC

MCM5-hF GGGTGCGAGGAGAACAGT

MCM5-hR TGAGTCTGAGCCAGGGAG

NCAPG-hF AGAGTATTGTTGGCTTCC

NCAPG-hR AACTTCTGGACCATCACA

2 BioMed Research International

http://www.ncbi.nlm.nih.gov/geo

http://www.ncbi.nlm.nih.gov/geo/geo2r

http://www.ncbi.nlm.nih.gov/geo/geo2r

https://shengxin.ren

http://www.funrich.org/

https://david.ncifcrf.gov/home.jsp

https://www.kegg.jp/

database (http://string-db.org) could construct the PPI net-work, which was visualized by Cytoscape (version 2.8) [14].

2.5. Significant Module and Hub Genes. Molecular ComplexDetection tool (MCODE) (version 1.5.1) [15], an openplug-in of Cytoscape, was performed to identify tested mostsignificant module from the PPI network, and the criteriawas that the maximum depth = 100, MCODE scores > 5,

cut − off = 2, k − score = 2, and node score cut − off = 0:2.Then, cytoHubba [16], a free plug-in of Cytoscape, wasapplied to authorize the hub genes, when the degree ≥ 10.

2.6. Expression Analysis of Hub Genes. The clustering analysisof expression level of hub genes was performed using heatmaps based on the GSE105288 and GSE66272. Also, theexpression profiles of hub genes in the ccRCC and normal

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 X Y

The gene positions are based on GRCh38.p2(NCBI). 2961 genes.

The differentially expressed genes on chromosomes

Underexpressed genesOverexpressed genes

(a)

12.7

10.16

7.62

5.08

2.54

0–0.81 –0.57 –0.34 –0.1 0.14 0.37 0.61

log2 (FoldChange)

Volcano plot

–log

10 (P

val

ue)

(b)

12.93

10.34

7.76

5.17

2.59

0–10.69 –6.82 –2.96 0.9 4.77 8.63 12.5

log2 (FoldChange)

Volcano plot

–log

10 (P

val

ue)

(c)

Figure 1: (a) The differentially expressed genes on chromosomes between ccRCC and normal kidney tissue. (b) One volcano plot presents theDEGs in the GSE105288. (c) Another volcano plot presents the DEGs in the GSE66272.

3BioMed Research International

http://string-db.org

groups were analyzed using Gene Expression Profiling Inter-active Analysis (GEPIA, http://gepia.cancer-pku.cn/) [17].

2.7. Effect of Hub Gene Expression for Pathological Stage andOverall Survival. The effect of hub gene expression for path-ological stage and overall survival was analyzed by theGEPIA. Finally, the correlation and linear regression analysesbetween AURKB, CCNA2, TPX2, and NCAPG were per-formed. And the receiver operator characteristic (ROC)

curve analysis was performed to test the sensitivity and spec-ificity of the hub gene expression for the diagnosis of ccRCC.The SPSS software (version 21.0; IBM; New York; America)was used to conduct all the statistical analysis. A P value <0.05 was defined as statistically significant.

2.8. RT-qPCR Assay. A total of 10 ccRCC participates wererecruited. After surgery, 10 ccRCC tumor samples fromccRCC patients and 10 adjacent normal kidney tissues

GSE105288 GSE66272

251 19731082

(a)

COLGALT1

HEPACAM2

PNMA2TMPRSS2PACRG

LOX

CALU

STC2RAB24 P4HA2

DPP6

PRKCSHHIGD1A

COL23A1

SLC2A1

TMCC1

VEGFA

HK2

CA9

YBX3DCTN4

TYMPPDGFD

EMCNP4HB

HRGKNG1

PLG

P4HA1

BNIP3

SLC2A12

PFKFB2

NDUFA4L2

PFKFB4

PGK1COX4I2

NPHS1TPI1

ALDOA

SLC1A4

VIM

STX4UNC5B

CD40ANGPT2

EGLN3

B3GNTL1

COL1A2

TAP1TAPBP

BST2

MAP4K4

ST14CANX

CCNA2

PSMB8

GRIK5

PREX1

SLC48A1CPOX

SCN2A

TBC1D24

PARM1

GNB5

SUCNR1

NRP2

MCOLN3

BHLHE41

KCNJ10

PARP9

ACPP

IFI16DTX1

DTX3

SCAMP5PAPPA

SHMT2

AMT

SLC7A8

CACNA2D2

COQ6

CHN2

ATP1A1

APEH

CASZ1

SLC41A2

EIF3B

CTSZ

GTF3A

YIPF5

HECW2

PLEKHO1

TRIM8

UBE2D2

HACL1

KLHL3

MCM5

MCM2

SPTSSA

NEK6

TOPBP1

CDCA7

GSAP

SLC51B

GABRA2

TMEM213

VWA8

NXPH2SLCO2B1

HOXA4

ATP6V0A4

SMDT1

ATP6V1D

DNASE2

ATP6V1C2

TACSTD2

LAIR1SETBP1

ERMP1

EHBP1L1

DNASE1

MLKLTRADDFUT11

SLC4A1MCFD2

NMI

PTTG1

RNF123

BID

AURKB USP2

SVOPL

CLIC5

SLC25A35SLC29A4

RARB

RNF145

LRBA

MCM7

PALM

RFC2

HMMR

ZDHHC3

PUS7

KIF13B

SMC4

TBC1D9B

TPX2

ARL2

ENSG00000259288 CENPM

RUFY1

DLGAP5

SORT1

CKAP2

KIF20B

DCAF11

NCK1 MTDHTPP2GLDC

FSCN1PHKA2

SHC1

TBL1XR1

TNIP1

L2HGDH

BCR

TRIB3

INTS8

MBNL1

FAM167ANETO2

ASUN

PPP1CC

XPO1

SPC25 NCAPG

SND1

TLK2NCKAP1L

CNEP1R1

PRLR

ELF5

ABCA1

SPAG4

CORO1C

SYNE2

(b)

Figure 2: (a) The Venn diagrammanifested that a total of 251 DEGs exist in the two datasets (GSE105288 and GSE66272) simultaneously. (b)The PPI network of the common DEGs.


http://gepia.cancer-pku.cn/

Canonical glycolysisGlycolytic process

Peptidyl−proline hydroxylation to 4−hydroxy−L−prolineAngiogenesis

Cell proliferationFructose metabolic process

Cell divisionDNA replication initiation

Regulation of insulin secretionMitotic nuclear division

Regulation of actin cytoskeleton organizationCarbohydrate phosphorylation

Glycine catabolic processGlycine decarboxylation via glycine cleavage system

Cellular response to hypoxiaPlatelet degranulation

Sister chromatid cohesion

0.2 0.4 0.6RichFactor

0.01

0.02

0.03

0.04

P valueTop 18 of pathway enrichment

GeneNumber2.55.07.5

10.012.5

(a)

Basolateral plasma membraneEndoplasmic reticulum lumen

MembraneExtracellular exosome

MelanosomeCytosol

MCM complexPlatelet alpha granule lumen

CentrosomeIntracellular membrane−bounded organelle

Endoplasmic reticulumProcollagen−proline 4−dioxygenase complex

Lysosomal membraneCytoplasmMicrospike

TAP complexMicrovillus

0.00 0.25 0.50 0.75 1.00RichFactor

Top 18 of Pathway Enrichment

0.01

0.03

P value

0.02

GeneNumber2040

6080

(b)

Figure 3: Continued.


Protein binding

Procollagen−proline 4−dioxygenase activity

Identical protein binding

Anion transmembrane transporter activity

Apolipoprotein binding

L−ascorbic acid binding

Actin binding

GTPase activator activity

Enzyme binding

TAP2 binding

Peptide antigen−transporting ATPase activity

DNA helicase activity

ATP binding

Double−stranded RNA binding

0.0 0.2 0.4 0.6RichFactor

Top 15 of pathway enrichment

GeneNumber

50100150

0.01

0.02

0.03

0.04

P value

(c)

hsa00051:Fructose and mannose metabolism

hsa01200:Carbon metabolism

hsa04966:Collecting duct acid secretion

hsa03030:DNA replication

hsa04066:HIF-1 signaling pathway

hsa00630:Glyoxylate and dicarboxylate metabolism

hsa05211:Renal cell carcinoma

hsa00010:Glycolysis / Gluconeogenesis

hsa01230:Biosynthesis of amino acids

0.050 0.075 0.100 0.125 0.150RichFactor

GeneNumber345

67

Top 10 of Pathway Enrichment

0.025

0.02

0.075

0.050

P value

(d)



samples were obtained. The research conformed to the Dec-laration of Helsinki and was authorized by the Human Ethicsand Research Ethics Committees of the Fourth Hospital ofHebei Medical University. The informed consents wereobtained from all participates.

Total RNA was extracted from 10 ccRCC tumor samplesand 10 adjacent normal kidney tissue samples by the RNAisoPlus (TRIzol) kit (Thermo Fisher, Massachusetts, Americaand reverse transcribed to cDNA. RT-qPCR was performedusing a Light Cycler® 4800 System with specific primers forgenes. Table 1 presents the primer sequences used in theexperiments. The RQ values (2−ΔΔCt, where Ct is the thresh-old cycle) of each sample were calculated and are presentedas fold change in the gene expression relative to the controlgroup. GAPDH was used as an endogenous control.

2.9. The Confirmation Using The Cancer Genome Atlas(TCGA) Data. The gene expression dataset of ccRCC in theTCGA was downloaded using the University of CaliforniaSanta Cruz (UCSC) Xena (https://xena.ucsc.edu/welcome-to-ucsc-xena/). There were a total of 944 samples including537 ccRCC samples and 407 normal renal samples. The

IlluminaHiSeq was selected as gene expression RNAseq inthe research. In addition, the gene expression levels ofVEGFA, AURKB, CCNA2, MCM2, MCM7, SMC4, TPX2,SLC2A1, MCM5, and NCAPG between ccRCC and normalrenal samples were compared using the one-way ANOVA.

Furthermore, the effect of the gene expression of VEGFA,AURKB, CCNA2, MCM2, MCM7, SMC4, TPX2, SLC2A1,MCM5, and NCAPG on overall survival was analyzed byusing the TCGA data.

2.10. The Construction of Neural Network Model. The train-ing group was randomly divided into the calibration dataand training data according to the proportion of 3 : 7. Therewere 6 samples in the calibration data, and 20 samples inthe training data. We used MATLAB (version 8.3) toaccomplish the normalization processing of variable values,network simulation, network training, and network initiali-zation. The number of input neurons in the input layer isthe same as the number of input variables, and the numberis two. The hidden layer is designed as 1 layer, and the out-put layer is also designed for 1 layer. One output variable isthe intima-media thickness. When training to 2000 steps

Amine metabolic process

Peptidyl-proline hydroxylation

Peptidyl-proline hydroxylation to 4-hydroxy-L-proline

Peptidyl-amino acid modification

Nitrogen compound metabolic process

Cellular process

Peptidyl-proline modification

Cellular amino acid derivative metabolic process

Biological_process

Cellular metabolic process

Heterocycle metabolic process

4-hydroxyproline metabolic process

Monocarboxylic acid metabolic process

Cellular ketone metabolic process

Carboxylic acid metabolic process

Oxoacid metabolic process

Protein modification process

Protein metabolic process

Cellular protein metabolic process

Macromolecule modification

Primary metabolic process

Macromolecule metabolic process

Cellular amino acid and derivative metabolic process

Metabolic processOrganic acid metabolic process

Small molecule metabolic process

Cellular macromolecule metabolic process

(e)

Figure 3: (a) Detailed information relating to changes in the biological processes (BP) of DEGs in ccRCC and normal kidney tissue. (b)Detailed information relating to changes in the cellular components (CC) of DEGs in ccRCC and normal kidney tissue. (c) Detailedinformation relating to changes in the molecular functions (MF) of DEGs in ccRCC and normal kidney tissue. (d) KEGG pathwayanalysis for DEGs. (e) The BP analysis for DEGs via the BiNGO software.


https://xena.ucsc.edu/welcome-to-ucsc-xena/

https://xena.ucsc.edu/welcome-to-ucsc-xena/

Membrane-bounded vesicle

Organelle lumen

Organelle

Membrane-enclosed lumen

Pigment granule

Cytoplasmicmembrane-bounded vesicle

Vesicle

Melanosome Nonmembrane-boundedorganelle

Cytoplasmic vesicle

Cell part

Cell

Cellular_component

Cell projection

Membrane-bounded organelle

Intracellular organelle

Nuclear part

Nucleoplasm

Intracellularnonmembrane-bounded

organelle

Nucleus

Nuclear lumen

Intracellularmembrane-bounded organelle

Intracellular organelle lumen

Macromolecular complex

Chromosome

intracellular organelle part

Intracellular part

Organelle part

Cytoplasmic part

Microvillus membrane

Nuclear chromosome part

MHC class I peptide loading complex

Cell projection membrane

Subsynaptic reticulum

Nucleoplasm part

Protein complex

MCM complex

Microvillus

Chromosomal part

Cytoplasm

Endoplasmic reticulum

Cell projection part

Intracellular

Endoplasmic reticulum part

(a)



after repeated training, the falling gradient is 0, and thetraining speed is uniform [10]. At the same time, the train-ing error ≤ 0:05, and the R (relativity) value reached 0.9906.

3. Results

3.1. DEGs between Normal Kidney and ccRCC Samples. Thereare plenty of DEGs on all chromosomes between the ccRCCand normal samples (Figure 1(a)). One volcano plot presentsthe DEGs in the GSE105288 (Figure 1(b)), and another vol-cano plot presents the DEGs in the GSE66272 (Figure 1(c)).The Venn diagram manifested that a total of 251 DEGs existin the two datasets (GSE105288 and GSE66272) simulta-neously (Figure 2(a)).

3.2. Construction of the PPI Network. After construction ofthe PPI network for the common DEGs, there are 189 nodesand 406 edges in the PPI network (Figure 2(b)).

3.3. The Functional Enrichment Analysis of DEGs via GO andKEGG. GO analysis manifested that variations in DEGsrelated with biological processes (BP) were significantlyenriched in canonical glycolysis, glycolytic process, peptidyl-proline hydroxylation to 4-hydroxy-L-proline, angiogenesis,cell proliferation, fructose metabolic process, cell division,DNA replication initiation, regulation of insulin secretion,mitotic nuclear division, regulation of actin cytoskeleton orga-nization, carbohydrate phosphorylation, glycine catabolic pro-cess, glycine decarboxylation via glycine cleavage system,cellular response to hypoxia, and so on (Figure 3(a)). The var-iations in DEGs related with cellular components (CC) weresignificantly enriched in the basolateral plasma membrane,endoplasmic reticulum lumen, membrane, extracellular exo-some, melanosome, cytosol, MCM complex, and so on(Figure 3(b)). The variations in the DEGs related with molec-ular functions (MF) were significantly enriched in proteinbinding, procollagen-proline 4-dioxygenase activity, identical

Carbohydrate kinase activity

Kinase activity

6-Phosphofructo-2-kinaseactivity

Phosphofructokinase activity

Transferase activity, transferring phosphorus-containing groups

Phosphotransferase activity, alcohol group as acceptor

Transferase activity

Oxidoreductase activity

Catalytic activity

Procollagen-prolinedioxygenase activity

Procollagen-proline4-dioxygenase activity

Peptidyl-proline 4-dioxygenase activity

Kinase binding

Substrate-specifictransmembrane transporter

activity

Transmembrane transporter activity

Transporter activity

Substrate-specific transporter activity

Ion transmembrane transporter activity

TAP2 binding

Peptide binding

Peptide antigen binding

Antigen binding

TAP1 binding

Protein binding Molecular_function

Binding Peptidyl-proline dioxygenase activityTAP binding

Protein complex binding

Enzyme binding

Intermediate filament binding

(b)

Figure 4: (a) The CC analysis for DEGs via the BiNGO software. (b) The MF analysis for DEGs via the BiNGO software.


protein binding, anion transmembrane transporter activity,apolipoprotein binding, L-ascorbic acid binding, actin bind-ing, and so on (Figure 3(c)). The KEGG pathway enrichmentanalysis showed that the top pathways related with DEGswere fructose and mannose metabolism, carbon metabolism,collecting duct acid secretion, DNA replication, HIF-1 sig-naling pathway, and so on (Figure 3(d)).

The BP analysis for DEGs is presented in Figure 3(e)via the BiNGO software (Figure 3(e)). The CC analysis forDEGs is presented in Figure 4(a) via the BiNGO software(Figure 4(a)). The MF analysis for DEGs is presented inFigure 4(b) via the BiNGO software (Figure 4(b)).

3.4. Significant Module Network and Identification of HubGenes. A significant module was screened from the PPI

network, and one module network consisted of 14 nodesand 84 edges (Figure 5(a)). Another module network con-sisted of 15 nodes and 32 edges (Figure 5(b)). And ten hubgenes were identified, including VEGFA, AURKB, CCNA2,MCM2, MCM7, SMC4, TPX2, SLC2A1, MCM5, andNCAPG (Figure 5(c)).

3.5. Difference of Expression of Hub Genes between ccRCCand Normal Kidney Samples. Hierarchical clustering allowedfor simple differentiation of ccRCC tissues from normal colo-rectal tissues via the expression levels of hub genes in theGSE105288 and GSE66272 datasets. One heat map showedthat the expressions of all the hub genes were higher in theccRCC samples than the normal samples in the GSE105288(Figure 6(a)). Another heat map also showed that the

AURKB

SPC25

SMC4

KIF20B

HMMR

TPX2CKAP2

MCM2

CENPM

MCM7

NCAPGPTTG1

CCNA2

DLGAP5

(a)

P4HA1COL23A1

EGLN3

P4HA2

VEGFA

COL1A2

PRKCSHSTC2

CALU

HRG

BNIP3

CA9PLG

PGK1

SLC2A1

(b)

MCM5

MCM7 NCAPG

MCM2

SLC2A1

CCNA2

TPX2

AURKBSMC4

VEGFA

(c)

VEGFA

AURKB

CCNA2

MCM2

MCM7

SMC4

TPX2

SLC2A1

MCM5

NCAPG

Rank Node

(d)

Figure 5: (a) A significant module was screened from the PPI network, and one module network consisted of 14 nodes and 84 edges. (b)Another module network consisted of 15 nodes and 32 edges. (c) Ten hub genes were identified, including VEGFA, AURKB, CCNA2,MCM2, MCM7, SMC4, TPX2, SLC2A1, MCM5, and NCAPG.


expressions of all the hub genes were higher in the ccRCCsamples than the normal samples in the GSE66272(Figure 6(b)). Through the GEPIA analysis, the expressionsof hub genes in the ccRCC patients were higher than the nor-mal individuals (Figure 7(a)).

3.6. Association between Hub Gene Expression, PathologicalStage, and Overall Survival. The results of GEPIA manifestedthat the expressions of AURKB, CCNA2, TPX2, and NCAPGwere significantly positively related with pathological stage

(P < 0:05), while the expressions of VEGFA, MCM2,MCM7, SMC4, SLC2A1, and MCM5 were not(Figure 7(b)). The results showed that the expression levelof VEGFA was not related with the overall survival ofccRCC patients (P > 0:05, Figure 8(a)). The overall sur-vival analysis showed that ccRCC patients with highexpression levels of AURKB (Figure 8(b)) and CCNA2(Figure 8(c)) had poorer overall survival times than thosewith low expression levels (P < 0:05). The expression levelof MCM2 was not related with the overall survival of

GSM

2825

240

GSM

2825

242

GSM

2825

243

GSM

2825

252

GSM

2825

255

GSM

2825

263

GSM

2825

265

GSM

2825

275

GSM

2825

277

GSM

2825

239

GSM

2825

241

GSM

2825

244

GSM

2825

245

GSM

2825

246

GSM

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GSM

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2825

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258

GSM

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GSM

2825

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2825

261

GSM

2825

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GSM

2825

264

GSM

2825

266

GSM

2825

267

GSM

2825

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GSM

2825

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GSM

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270

GSM

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GSM

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GSM

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GSM

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GSM

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GSM

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GSM

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GSM

2825

282

VEGFA

SLC2A1

SMC4

MCM5

MCM7

MCM2

CCNA2

TPX2

AURKB

NCAPG−3

−2

−1

0

1

2

3

Normal ccRCC

(a)

GSM

1618

388

GSM

1618

390

GSM

1618

392

GSM

1618

394

GSM

1618

396

GSM

1618

398

GSM

1618

400

GSM

1618

402

GSM

1618

404

GSM

1618

406

GSM

1618

408

GSM

1618

410

GSM

1618

412

GSM

1618

414 G

SM1618416 G

SM16

1841

8G

SM16

1842

0G

SM16

1842

2G

SM16

1842

4G

SM16

1842

6G

SM16

1842

8G

SM16

1843

0G

SM16

1843

2G

SM16

1843

4G

SM16

1843

6G

SM16

1843

8G

SM16

1844

0G

SM16

1838

7G

SM16

1838

9G

SM16

1839

1G

SM16

1839

3G

SM16

1839

5G

SM16

1839

7G

SM16

1839

9G

SM16

1840

1G

SM16

1840

3G

SM16

1840

5G

SM16

1840

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SM16

1840

9G

SM16

1841

1G

SM16

1841

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SM16

1841

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SM16

1841

9G

SM16

1842

1G

SM16

1842

3G

SM16

1842

5G

SM16

1842

7G

SM16

1842

9G

SM16

1843

1G

SM16

1843

3G

SM16

1843

5G

SM16

1843

7G

SM16

1843

9

SMC4

VEGFA

SLC2A1

MCM5

TPX2

NCAPG

AURKB

MCM7

CCNA2

MCM2

Normal ccRCC

−4

–2

0

2

4

(b)

Figure 6: (a) One heat map showed that the expressions of all the hub genes were higher in the ccRCC samples than the normal samples in theGSE105288. (b) Another heat map also showed that the expressions of all the hub genes were higher in the ccRCC samples than the normalsamples in the GSE66272.


ccRCC patients (P > 0:05, Figure 8(d)). The expression levelof MCM7 was not related with the overall survival of ccRCCpatients (P > 0:05, Figure 8(e)). The expression level of SMC4was not related with the overall survival of ccRCC patients(P > 0:05, Figure 8(f)). The overall survival analysis showedthat ccRCC patients with high expression levels of TPX2had poorer overall survival times than those with low expres-

sion levels (P < 0:05, Figure 9(a)). The expression levels ofSLC2A1 (Figure 9(b)) and MCM5 (Figure 9(c)) were notrelated with the overall survival of ccRCC patients (P > 0:05).The overall survival analysis showed that ccRCC patientswith high expression levels of NCAPG had poorer overallsurvival times than those with low expression levels(P < 0:05, Figure 9(d)).

0

2

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12

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5

VEGFA AURKB CCNA2 MCM2 MCM7

SMC4 TPX2 SLC2A1 MCM5 NCAPG

KIRC(num(T)=523; num(N)=100)










⁎ ⁎ ⁎

⁎ ⁎ ⁎

(a)

4

6

8

10

12

Stage I Stage II Stage III Stage IV0

1

2

3

4

5

6

7

Stage I Stage II Stage III Stage IV

1

2

3

4

5

6


1

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5


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8


1

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7


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7


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10


2

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5

6

7


1

2

3

4

5

6


VEGFA AURKB CCNA2 MCM2 MCM7

SMC4 TPX2 SLC2A1 MCM5 NCAPG

F value = 2.67Pr(>F) = 0.0471

F value = 20.8Pr(>F) = 1.05e−12

F value = 13Pr(>F) = 3.5e−086

F value = 2.19Pr(>F) = 0.0879

F value = 2.18Pr(>F) = 0.0895

F value = 14.3Pr(>F) = 5.85e−09

F value = 1.91Pr(>F) = 0.127

F value = 0.548Pr(>F) = 0.649

F value = 15.3Pr(>F) = 1.52e−09

F value = 3.26Pr(>F) = 0.0213

6

(b)

Figure 7: (a) The comparison of expressions of all hub genes between ccRCC and normal kidney samples. (b) The relationship between theexpression of hub genes and pathological stage.


0 50 100 150

0.0

0.2

0.4

0.6

0.8

1.0Overall survival

Months

Perc

ent s

urvi

val

Log-rank P = 0.2 HR(high) = 0.82

P(HR) = 0.2n(high) = 258n(low) = 258

Low VEGFA TPMHigh VEGFA TPM

(a)

0 50 100 150

0.0

0.2

0.4

0.6

0.8

1.0Overall survival

Months

Perc

ent s

urvi

val

Log-rank P = 2.8e−06 HR(high) = 2.1

P(HR) = 4.6e−06n(high) = 257n(low) = 257

Low AURKB TPMHigh AURKB TPM

(b)

0 50 100 150

0.0

0.2

0.4

0.6

0.8

1.0Overall survival

Months

Perc

ent s

urvi

val

Log-rank P = 0.0012 HR(high) = 1.7P(HR) = 0.0014

n(high) = 257n(low) = 257

Low CCNA2 TPMHigh CCNA2 TPM

(c)

0 50 100 150

0.0

0.2

0.4

0.6

0.8

1.0Overall survival

Months

Perc

ent s

urvi

val


P(HR) = 0.61 n(high) = 257n(low) = 258

Low MCM2 TPMHigh MCM2 TPM

(d)

0 50 100 150

0.0

0.2

0.4

0.6

0.8

1.0Overall survival

Months

Perc

ent s

urvi

val


P(HR)= 0.24 n(high) = 258n(low) = 258


(e)

0 50 100 150

0.0

0.2

0.4

0.6

0.8

1.0Overall survival

Months

Perc

ent s

urvi

val

Log-rank P = 0.64 HR(high) = 1.1P(HR) = 0.64

n(high) = 257n(low) = 258

Low SMC4 TPMHigh SMC4 TPM

(f)

Figure 8: The overall survival Kaplan-Meier of six hub genes. (a) VEGFA, (b) AURKB, (c) CCNA2, (d) MCM2, (e) MCM7, and (f) SMC4.


3.7. The Interaction Analysis among the Hub Genes. Throughthe Pearson correlation test, heat maps manifested that therewere strong correlations among hub genes in the GSE105288(Figure 10(a)) and GSE66272 (Figure 10(b)) datasets. Thecorrelation between AURKB, CCNA2, TPX2, and NCAPGwas strong (Figures 10(c)–10(h)).

3.8. ROC Analysis. To identify accurate thresholds for hubgenes to predict ccRCC, we constructed ROC. The expressionof all hub genes was associated with a diagnosis of ccRCC.The ROC curve of AURKB in the GSE105288 was shownin Figure 11(a). The ROC curve of CCNA2 in theGSE105288 was shown in Figure 11(b). The ROC curve ofTPX2 in the GSE105288 was shown in Figure 11(c). TheROC curve of NCAPG in the GSE105288 was shown inFigure 11(d). The ROC curve of AURKB in the GSE66272was shown in Figure 11(e). The ROC curve of CCNA2 inthe GSE66272 was shown in Figure 11(f). The ROC curve

of TPX2 in the GSE66272 was shown in Figure 11(g). TheROC curve of NCAPG in the GSE66272 was shown inFigure 11(h).

3.9. Results of RT-qPCR Analysis. As presented in Figure 12,the relative expression levels of VEGFA, AURKB, CCNA2,MCM2, MCM7, SMC4, TPX2, SLC2A1, MCM5, andNCAPG were significantly higher in the ccRCC samples,compared with the normal kidney tissues groups. The resultdemonstrated that VEGFA, AURKB, CCNA2, MCM2,MCM7, SMC4, TPX2, SLC2A1, MCM5, and NCAPG mightbe considered biomarkers for ccRCC.

3.10. The Verification by TCGA. According to the aboveexpression analysis, VEGFA, AURKB, CCNA2, MCM2,MCM7, SMC4, TPX2, SLC2A1, MCM5, and NCAPG weremarkedly upregulated in ccRCC tumor samples comparedwith the normal renal samples. After confirmation using

0 50 100 150

0.0

0.2

0.4

0.6

0.8

1.0Overall survival

Months

Perc

ent s

urvi

val

Log-rank P = 0.0041 HR(high)= 1.6P(HR) = 0.0044n(high) = 256n(low) = 257

Low TPX2 TPMHigh TPX2 TPM

(a)

0 50 100 150

0.0

0.2

0.4

0.6

0.8

1.0Overall survival

Months

Perc

ent s

urvi

val

Logrank P = 0.49 HR(high) = 0.9P(HR) = 0.49n(high) = 258n(low) = 258

Low SLC2A1 TPMHigh SLC2A1 TPM

(b)

Overall survival

0 50 100 150

0.0

0.2

0.4

0.6

0.8

1.0

Months

Perc

ent s

urvi

val


P(HR) = 0.17n(high) = 258n(low) = 258


(c)

Overall survival

0 50 100 150

0.0

0.2

0.4

0.6

0.8

1.0

Months

Perc

ent s

urvi

val

Logrank P = 0.014 HR(high) = 1.5 P(HR) = 0.015n(high) = 258n(low) = 257

Low NCAPG TPMHigh NCAPG TPM

(d)

Figure 9: The overall survival Kaplan-Meier of another four hub genes. (a) TPX2, (b) SLC2A1, (c) MCM5, and (d) NCAPG.


the TCGA data, these gene expression levels in the ccRCCsamples were also significantly higher than the normal renalsamples (P < 0:05, Figure 13).

The results showed that the expression level of VEGFA(P = 0:4946), MCM2 (P = 0:5249), MCM7 (P = 0:092),SMC4 (P = 0:856), SLC2A1 (P = 0:209), and MCM5

VEG

FA

AU

RKB

CCN

A2

MCM

2

MCM

7

SMC4

TPX2

SLC2

A1

MCM

5

NCA

PG

VEGFA

AURKB

CCNA2

MCM2

MCM7

SMC4

TPX2

SLC2A1

MCM5

NCAPG0.2

0.4

0.6

0.8

1

(a)

VEG

FA

AU

RKB

CCN

A2

MCM

2

MCM

7

SMC4

TPX2

SLC2

A1

MCM

5

NCA

PG

VEGFA

AURKB

CCNA2

MCM2

MCM7

SMC4

TPX2

SLC2A1

MCM5

NCAPG 0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

(b)

0 1 2 3 4 5 60

1

2

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4

5

log2 (AURKB TPM)

log2

(CCN

A2

TPM

)

P value = 0R = 0.82

(c)

0 1 2 3 4 5 60

1

2

3

4

5

6

log2 (AURKB TPM)

log2

(TPX

2 TP

M)

P value = 0R = 0.93

(d)

0 1 2 3 4 5 60

1

2

3

4

5

log2 (AURKB TPM)

P value = 0R = 0.83

log2

(NCA

PG T

PM)

(e)

0 1 2 3 4 50

1

2

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5

6

log2 (CCNA2 TPM)

log2

(TPX

2 TP

M)

P value = 0R = 0.9

(f)

0 1 2 3 4 50

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5

log2 (CCNA2 TPM)

log2

(NCA

PG T

PM)

P value = 0R = 0.95

(g)

0

1

2

3

4

5

log2

(NCA

PG T

PM)

0 1 2 3 4 5 6log2 (TPX2 TPM)

P value = 0R = 0.91

(h)

Figure 10: (a) Heat maps showing the correlations between hub genes in the GSE105288 datasets. (b) Heat maps showing the correlationsbetween hub genes in the GSE66272 datasets. (c) The correlation between AURKB and CCNA2. (d) The correlation between AURKB andTPX2. (e) The correlation between AURKB and NCAPG. (f) The correlation between CCNA2 and TPX2. (f) The correlation betweenAURKB and NCAPG. (g) The correlation between CCNA2 and NCAPG. (h) The correlation between TPX2 and NCAPG.


Specificity

Sens

itivi

ty

1.0 0.8 0.6 0.4 0.2 0.0

0.0

0.2

0.4

0.6

0.8

1.06.829 (0.857, 1.000)

AUC: 0.937

AURKB

(a)

Specificity

Sens

itivi

ty

1.0 0.8 0.6 0.4 0.2 0.0

0.0

0.2

0.4

0.6

0.8

1.0 7.237 (0.829, 1.000)

AUC: 0.933

CCNA2

(b)

Specificity

Sens

itivi

ty

1.0 0.8 0.6 0.4 0.2 0.0

0.0

0.2

0.4

0.6

0.8

1.0

7.209 (0.743, 0.889)

AUC: 0.895

TPX2

(c)

Specificity

Sens

itivi

ty

1.0 0.8 0.6 0.4 0.2 0.0

0.0

0.2

0.4

0.6

0.8

1.07.256 (0.829, 1.000)

AUC: 0.940

NCAPG

(d)

Specificity

Sens

itivi

ty

1.0 0.8 0.6 0.4 0.2 0.0

0.0

0.2

0.4

0.6

0.8

1.0

0.083 (0.846, 0.926)

AUC: 0.930

AURKB

(e)

Specificity

Sens

itivi

ty

1.0 0.8 0.6 0.4 0.2 0.0

0.0

0.2

0.4

0.6

0.8

1.0

−0.389 (1.000, 0.852)

AUC: 0.983

CCNA2

(f)



(P = 0:303) was not related with the overall survival of ccRCCpatients. The overall survival analysis showed that ccRCCpatients with high expression levels of AURKB (P = 0:000),CCNA2 (P = 0:000), TPX2 (P = 0:000), and NCAPG(P = 0:000) had poorer overall survival times than those withlow expression levels (Figure 14).

3.11. The Neural Network Prediction Model between AURKB,CCNA2, TPX2, and NCAPG. The mean squared error is<0.05 (Figure 15(a)). The relativity of training is 0.9906.The relativity of validation is 0.99768. The relativity of testis 0.93812. And the relativity of all procedure is 0.97977(Figure 15(b)). Through verifying the predicted value of thedata against the actual value, we found that there are onlysmall differences in the comparison chart of training results(Figure 15(c)) and error analysis diagram (Figure 15(d)).Based on the above result, we could speculate that there werestrong correlations between AURKB, CCNA2, TPX2, andNCAPG.

Through the cubic spline interpolation algorithm, wefind the high-risk warning indicator of TPX2: CCNA2 < 5:0and 5:2 < AURKB. The three-dimensional stereogram couldpresent the warning range well (Figure 15(e)). The planegraph is also shown (Figure 15(f)).

4. Discussion

RCC is a common disease in the urinary system. Accordingto the statistics of the World Health Organization in 2018,its incidence is second only to prostate cancer and bladdercancer and is increasing year by year [1]. Although manygenes are considered potential therapeutic targets and prog-nostic predictors of RCC, the molecular mechanism of theoccurrence and development of RCC remains controversial.

With the continuous progress of science, microarraytechnology, as a special data mining method, is very influen-tial at present. This revolutionary technology transformstraditional molecular research from a situation that relies

on personal experience and subjective guesses to a moreobjective science [18–20].

In this paper, bioinformatics tools are used to mine thetargeted biomarkers of ccRCC. The results showed thatAURKB, CCNA2, TPX2, and NCAPG were highly expressedin ccRCC compared with renal tissue. With the increasingexpression of AURKB, CCNA2, TPX2, and NCAPG, thepathological stage of ccRCC increased gradually. Comparedwith the individuals with low expression of AURKB,CCNA2, TPX2, and NCAPG, patients with high expressionof AURKB, CCNA2, TPX2, and NCAPG have a poor overallsurvival.

Aurora kinase B (AURKB) is a serine/threonine kinasethat participates in the regulation of chromosome arrange-ment and segregation by binding to microtubules [21].Numerous studies have found that the overexpression ofAURKB exists in a variety of cancer cell lines [22–24]. Sor-rentino et al. found that AURKB is highly expressed in thy-roid carcinoma, and its expression level is related tomalignant degree. The block of AURKB expression or byusing an inhibitor of Aurora kinase activity significantlyreduced the growth of thyroid carcinoma cells [23].Katayama et al. have similar findings in colorectal cancer[25], Smith et al. in lung cancer [22], and Chieffi et al. in pros-tate cancer [24]. Abnormal mitotic regulation can induce theproduction of aneuploid cells and act as a driving role in theprocess of malignant progression, while serine/theronineprotein kinases of the Aurora family genes play a critical rolein the regulation of key cell cycle processes. The abnormalexpression of AURKB can produce malignant and invasiveaneuploid cells. This further indicates that AURKB is relatedto tumorigenesis [26, 27]. With the discovery of abnormalexpression of AURKB in cancer cells, researchers realizedthat it may become a new target for cancer treatments. Atpresent, many AURKB inhibitors have been developed,including AZD1152, AT9283, VX-680/MK-0457, PHA-680632, AMG-900, PHA-739358, and CYC-116, and someof them have entered clinical trials [28].

Specificity

Sens

itivi

ty

1.0 0.8 0.6 0.4 0.2 0.0

0.0

0.2

0.4

0.6

0.8

1.0−0.002 (0.962, 0.926)

AUC: 0.977

TPX2

(g)

Specificity

Sens

itivi

ty

1.0 0.8 0.6 0.4 0.2 0.0

0.0

0.2

0.4

0.6

0.8

1.00.302 (0.846, 0.963)

AUC: 0.934

NCAPG

(h)

Figure 11: ROC curves of hub genes for ccRCC.


Con ccRCC

0

1

2

3VEGFA

Rela

tive e

xpre

ssio

n of

VEG

FA

⁎

(a)

Con ccRCC

0

2

4

6

8AURKB

Rela

tive e

xpre

ssio

n of

AU

RKB ⁎

(b)

Con ccRCC

0

2

4

6

8

10CCNA2

Rela

tive e

xpre

ssio

n of

CCN

A2 ⁎

(c)

Con ccRCC

0

5

10

15

20MCM2

Rela

tive e

xpre

ssio

n of

MCM

2 ⁎

(d)

Con ccRCC

0

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6

8

10MCM7

Rela

tive e

xpre

ssio

n of

MCM

7 ⁎

(e)

Con ccRCC

0

2

4

6

8

10SMC4

Rela

tive e

xpre

ssio

n of

SM

C4

⁎

(f)

Con ccRCC

0

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15

20

25TPX2

Rela

tive e

xpre

ssio

n of

TPX

2

⁎

(g)

Con ccRCC

0

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15

20SLC2A1

Rela

tive e

xpre

ssio

n of

SLC

2A1 ⁎

(h)

Con ccRCC

0

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20MCM5

Rela

tive e

xpre

ssio

n of

MCM

5 ⁎

(i)

Con ccRCC

0

5

10

15

20NCAPG

Rela

tive e

xpre

ssio

n of

NCA

PG

⁎

(j)

Figure 12: Relative expression of VEGFA, AURKB, CCNA2, MCM2, MCM7, SMC4, TPX2, SLC2A1, MCM5, and NCAPG by RT-qPCRanalysis. ∗P < 0:05, compared with normal kidney tissues.


The proteins encoded by CyclinA2 (CCNA2) belong to ahighly conserved cyclin family, which promotes cell transfor-mation by binding and activating cyclin-dependent kinases(CDKs) through G1/S and G2/M [29]. Previous studies havefound that the overexpression of CyclinA2 occurs in lungcancer [30, 31], breast cancer [32, 33], colorectal cancer[34], and other tumors and related to poor prognosis ofcancer patients. Aaltomaa et al. found that CyclinA2 wasexpressed in the cytoplasm of RCC but not in the normaltissue near the tumor, and the overexpression of CyclinA2was related to the survival time of patients with RCC,suggesting that it may be a prognostic indicator of RCC[35]. The increase of the CyclinA2 expression is related tothe uncontrolled and accelerated cell cycle, which leads togene amplification and chromosome ectopia. Gopinathanet al. found that knockout CyclinA2 in mice can inhibittumorigenesis [36]. Liang et al. found that the increasedexpression of sclerostin domain-containing protein1(SOSTDC1) can inhibit CyclinA2, while SOSTDC1 caninhibit tumor growth [37]. CyclinA2 can not only be usedas a predictor of prognosis and survival in patients withRCC but also has great potential in cancer treatment.

TPX2 microtubule nucleation factor (TPX2) encodes amicrotubule-associated protein that activates cell cycle kinasecalled Aurora A and regulates mitotic spindles. The overex-pression of TPX2 is related to the genesis of different can-cers and is closely related to chromosome instability. Theuncontrolled expression of TPX2 may eventually becomethe driving force of cancer development by inducing aneu-ploidy [38]. Zhang et al. found that compared with humanbronchial epithelial cells (16HBE), TPX is overexpressed inmalignant transformed 16HBE cells(16HBE-C) throughanti-benzo[a]pyrene-trans-7,8-dihydrodiol-9,10-epoxide, inwhich TPX2 RNA interference (RNAi) can lead to S-phasearrest, inhibit cell proliferation, and induce cell apoptosis.

TPX2 is tyrosine phosphorylated in malignant transformed16HBE-C, and this phosphorylation may be involved in themalignant proliferation of cancer cells [39]. Ma et al. foundthat the level of the TPX2 protein in normal bronchial epithe-lium and alveoli was very low, while the level of TPX2 proteinincreased gradually in squamous metaplasia, dysplasia, andcarcinoma in situ and invasive tumor. The immunohisto-chemical labeling index of TPX2 was related to the degreeof differentiation, stage, and lymph node metastasis of lungsquamous cell carcinoma, and the overexpression of TPX2was significantly correlated with the decrease of 5-year sur-vival rate [40]. Similar results were found in a variety ofcancers, such as colorectal cancer [41], cervical cancer[42], and prostate cancer [43]. The expression of TPX2 inRCC was significantly higher than that in normal renal tis-sue, and it was related to tumor size, histological grade,tumor stage, and poor prognosis [44–47]. This may bedue to the significant upregulation of TPX2 in RCC tissues,thus increasing the proliferation and invasive ability ofrenal cancer cells. From this point of view, TPX2 can notonly become a target for RCC treatment but also play a roleas an independent prognostic factor of RCC.

Non-SMC condensin I complex subunit G (NCAPG)coding a condensed protein complex subunit is responsiblefor chromosome condensation and stabilization duringmitosis and meiosis [48]. In recent years, there are moreand more studies on the abnormal expression of NCAPG inprostate cancer [49], lung cancer [50], breast cancer [51],and other cancers. In the study of Liu et al., NCAPG wasfound to be overexpressed in hepatocellular carcinoma com-pared with the adjacent normal tissue, and high levels ofNCAPG expression were found to significantly correlate withrecurrence, the time of recurrence, metastasis, differentiation,and TNM stage. The knockdown of NCAPG expression alsoinhibited tumor cell migration and the cell invasive capacity

Gene symbol

ccRCC

Normal

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Figure 13: The confirmation of gene expression level using The Cancer Genome Atlas (TCGA) data. The gene expression levels of VEGFA,AURKB, CCNA2, MCM2, MCM7, SMC4, TPX2, SLC2A1, MCM5, and NCAPG in ccRCC samples were significantly higher than the normalrenal samples.


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P = 0.4946Log-rank test statistics = 0.4666



P = 0.5249Log-rank test statistics = 0.4042 P = 0.092

Log-rank test statistics = 2.831P = 0.856Log-rank test statistics = 0.033





LowHigh

Figure 14: The effect of gene expression on overall survival by using the TCGA data. The expression level of VEGFA, MCM2, MCM7, SMC4,SLC2A1, and MCM5 was not related with the overall survival of ccRCC patients. The ccRCC patients with high expression levels of AURKB,CCNA2, TPX2, and NCAPG had poorer overall survival times than those with low expression levels.


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in vitro [52]. Through genome-wide functional knockoutscreen, Wang et al. believe that NCAPG is a necessaryclinical-related target for the growth of hepatocellularcarcinoma cells [53]. Ai et al. found that microRNA-181c(miR-181c) inhibits cancer by downregulating the expressionof NCAPG, affecting the infiltration, migration, proliferation,and apoptosis of hepatoma cells [54]. In the study of Araiet al., microRNA-99a-3p downregulated the expression ofNCAPG, thereby inhibiting cancer cell invasion incastration-resistant prostate cancer [49]. In conclusion,NCAPG represents a promising novel target and a prognos-tic biomarker for clinical management.

However, this study also has some shortcomings.Although the core genes screened in this study may play animportant role in the occurrence of ccRCC, more clinicalsamples and patient prognosis information are still neededfor verification.

5. Conclusion

To sum up, 251 differentially expressed genes and 10 hubgenes (especially AURKB, CCNA2, TPX2, and NCAPG)were screened from ccRCC and normal renal tissues bymicroarray technology, which could be used as diagnosticand therapeutic biomarkers for ccRCC. AURKB, CCNA2,TPX2, and NCAPG which might be related to the occurrenceand malignant progression of ccRCC.

Data Availability

The datasets used and/or analyzed during the current studyare available from the corresponding author on reasonablerequest.

Disclosure

The authors ensure that questions related to the accuracy orintegrity of any part of the work are appropriately investi-gated and resolved.

Conflicts of Interest

The authors declare that they have no conflicts of interests.

Authors’ Contributions

Ai-li Zhang and Ling-bing Meng contributed equally to thiswork.

Acknowledgments

We gratefully thank the Department of Urinary Surgery inThe Fourth Hospital of Hebei Medical University for thetechnical assistance.

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