Research Article Comprehensive Multiomics Analysis Reveals Potential Diagnostic and Prognostic Biomarkers in Adrenal Cortical Carcinoma

Xiunan Li,1 Jiayi Li DD,2 Leizuo Zhao,3,4 Zicheng Wang,5 Peizhi Zhang ,3 Yingkun Xu D, and Guangzhen Wu DD1 1

1Department of Urology, The First Affiliated Hospital of Dalian Medical University, Dalian 116011, China

2School of Business, Hanyang University, Seoul 15588, Republic of Korea

3Department of Urology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250021, China 4 Department of Urology, Dongying People’s Hospital, Dongying 257000, China

5 Department of Urology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, China

6Department of Breast and Thyroid Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400042, China

Correspondence should be addressed to Yingkun Xu; yingkunxu@hotmail.com and Guangzhen Wu; wuguang0613@hotmail.com

Received 22 April 2022; Revised 6 July 2022; Accepted 9 July 2022; Published 8 August 2022

Academic Editor: Lei Chen

Copyright @ 2022 Xiunan Li 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.

Adrenal cortical carcinoma (ACC) is a severe malignant tumor with low early diagnosis rates and high mortality. In this study, we used a variety of bioinformatic analyses to find potential prognostic markers and therapeutic targets for ACC. Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) data sets were used to perform differential expressed analysis. WebGestalt was used to perform enrichment analysis, while String was used for protein-protein analysis. Our study first detected 28 up-regulation and 462 down-regulation differential expressed genes through the GEO and TCGA databases. Then, GO functional analysis, four pathway analyses (KEGG, REACTOME, PANTHER, and BIOCYC), and protein-protein interaction network were performed to identify these genes by WebGestalt tool and KOBAS website, as well as String database, respectively, and finalize 17 hub genes. After a series of analyses from GEPIA, including gene mutations, differential expression, and prognosis, we excluded one candidate unrelated to the prognosis of ACC and put the remaining genes into pathway analysis again. We screened out CCNB1 and NDC80 genes by three algorithms of Degree, MCC, and MNC. We subsequently performed genomic analysis using the TCGA and cBioPortal databases to better understand these two hub genes. Our data also showed that the CCNB1 and NDC80 genes might become ACC biomarkers for future clinical use.

1. Introduction

Adrenal cortical carcinoma (ACC) originates from the adre- nal cortex and is a rare clinical malignant endocrine tumor [1], with a population incidence of 0.001 [2]. Still, it is also the most common primary malignant tumor of the adrenal gland [3] and is the second most common malignant tumor of the endocrine organ after thyroid cancer [4]. ACC can occur at any age, with two peaks in childhood and between 50 and 70, and is more common in women [5-7]. The clinical manifestations of ACC are diverse and prone to invasion and metastasis. Due to the low early diag- nosis rate and high mortality, the survival period is generally

less than three years [8], and the 5-year survival rate is only 10% to 20% [9], which greatly threatens the life and health of patients. There is currently no effective early diagnosis and late treatment for ACC, and complete surgical resection is the only possible cure for ACC [10-13]. Therefore, finding novel biomarkers for efficient screening in the early stages of ACC may be valuable for long-term survival.

It is also worth noting that adrenocortical adenocarcino- mas have distinct gene expression profiles from adrenocorti- cal adenomas. The most widely recognized gene at present is the gene IGF2. The expression of IGF2 in adrenocortical adenocarcinoma is higher than that in adrenocortical ade- noma. However, the differential diagnosis of adrenocortical

FIGURE 1: The process of identifying DEGs in ACC. (a-b) Volcano maps based on GSE10927 and GSE19750 data sets. (c) Schematic representation of differentially expressed genes on chromosomes. (d) Venn diagram based on DEGs in GSE10927, GSE19750, and TCGA data.

GSE19750

GSE10927

Log,FC

Log_FC

-2

-5

-LogFDR

(a)

(b)

The differentially expressed gene on chromosome

Over-expressed genes

Under-expressed genes

(c)

Up-regulated DEGs

Down-regulated DEGs

GSE10927

GSE19750

GSE10927

GSE19750

294

100

242

7234

462

109

102

804

415

1209

TCGA

(d)

adenocarcinoma and adrenocortical adenoma cannot be accurately performed by only using IGF2 as an indicator [14-16]. In recent years, research on differential genetic screening of adrenal tumors has been on the ascendant. It has been reported that the combination of IGF2 and Ki-67 has high specificity and sensitivity in identifying benign and malignant adrenal cortical tumors [12, 14, 17]. Another

study reported that the most differentially significant genes were TOP2A, IGF2, CCNB2, CDC2, CDC25C, and CDKN1C [18]. The correlation between the differential gene expression fold and the survival time of patients with adre- nocortical adenocarcinoma has also been confirmed [19], so it is possible to judge the prognosis of patients according to the gene expression level. In addition, steroidogenic factor

TABLE 1: 490 DEGs were identified from TCGA and GEO data sets, including 28 up-regulated and 285 down-regulated genes in ACC compared with normal tissues.
DEGs	Genes name
Up-regulated genes (n =28)	GGH TPX2, CCNB1, PLA2G1B, ANLN, MND1, FOXM1, KIF11, RACGAP1, CENPH, RRM2, TOP2A, ZNF367, CENPU, APOBEC3B, GPX8, MAD2L1, GAS2L3, KIF4A, KIF20A, CENPK, PDE8B, CDC20, NDC80, PBK, NUF2, NCAPG, ESM1
Down-regulated genes (n =462)	CLMP, FSTL1, MMP2, RALYL, NR4A2, SERPINF1, SUGCT, SLCO2B1, TEK, NEFH, GYPC, LINC00924, EMILIN1, ID1, CELA1, IGSF11, SLC9A3R1, FHL1, IRX3, IFITM10, BTK, SYTL5, USP9Y, AQP11, ZBED6CL, FAM49A, HOXA5, TAC1, HOTAIRM1, EPB41L3, TSTD1, ALAS1, DAAM2, SMOC2, MAN1A1, NKAIN1, CSDC2, LRRC32, EMB, AXL, SHE, TCEAL2, IL10, ALOX5AP, FMO3, ABCA6, NBEA, DDX3Y, MCOLN3, SLC16A4, MC2R, WISP1, BRE, SRPX, ZNF204P, ADAP2, EIF1AY, LRRN4CL, RARRES1, CLEC5A, MARCO, TIMP4, KCNMB4,C9orf3, AOX1, CYR61,TYMP, GGT5, APOC1, FLVCR2, DLGAP1- AS1, CHRDL1, LAMA2, C1QC, CD55, PLN, RERG, PLTP, MRPL33, PON1, DNASE1L3, RNASE2, ERMP1, SLC47A1, ABCB1, THBD, CHKB, TH, MAP3K8, SPON1, PLA2G4A, ABCC3, EDNRB, EGFLAM, DPYS, ADAMTSL2, C7, S100A8, NPY5R, ITGAM, FOSL2, SKAP1, CCR1, HTR2B, PYGL, HIBCH, COL4A4, SPOCK2, GPR34, CORO1A, EFEMP2, AEBP1, JAM2, RASD1, CYP11B1, GPRASP1, CDKN1C, TXLNGY, IL33, GPX3, NOV, GPM6B, AMT, HSD11B1, KCNJ5, ACOX2, ERN1, PTH1R, PHYHD1, NXPH1, DAPL1, NPC1, PARM1, MS4A14, FBLN5, FIBIN, MUM1L1, IGF1, CERK, SUSD2, CSF3R, SCUBE3, SERPINB9, GATA6, MRAP, SERPING1, PDZRN3, MFAP5, COLEC11, MGST1, STON1, PAX8-AS1, CEBPD, NGFR, NEDD4L, PDGFD, SGK1, KRT8, NFKBIZ, SLC25A34, PLCXD3, RAMP3, TINAGL1, S100A16, TNFSF13, EFEMP1, LUM, C1S, FCGRT, NGEF, PLAT, SRPX2, IGFBP6, SLC37A2, AKAP12, HSD3B2, APOD, AKR1B1, MAPK13, TNFRSF14, ARFGAP3, CYP17A1, IL4R, OLFML3, FXYD1, FCER1G, C11orf96, RSPO3, CCDC159, SREBF1, C2orf40, KCNJ8, CFD, C1QTNF1, AS3MT, PITPNM1, ACTR3C, ANKS1A, SYNPO2, ALPK3, NR2F1, EPHA2, FAM150B, RASGRP2, PTPRB, PNMAL2, ECM1, DNALI1, STEAP4, LILRA2, B4GALT6, TTC39C, STX11, ACO1, SFRP4, FAM166B, DNAJC12, RXFP1, RAPGEF4, EPHX2,CCDC68, DUOX1, ACSM5, PLIN1, SULT1E1, RARRES2, ADAMTS1, TMEM173, GLUL, RASSF2, AVPR1A, TCIRG1, NPR2, ZEB2, PYY2, FMO2, MEST, SCNN1A, FAM65C, IGFBP4, NANOS1, RETSAT, OLFML1, NCF4, SIRPB2, KCNK3, FGR, HEPH, PHYHIP, C5AR1, APOE, GKN1, CRHBP, THRB, MCOLN2, LONRF2, SORBS2, MTMR6, PACSIN3, OMD, TCF21, SLA, KLF2, ACADVL, SLC16A2, SIGLEC1, MGP, ECHDC3, CPA4, GIMAP6, MYC, GPM6A, STARD8, FNDC4, F13A1, GIPC2, OGN, SLC44A3, CXCL2, STAB1, THBS1, AMDHD1, TMEM200C, SYBU, FILIP1L, SCN7A, MCF2, PDGFRA, SLC27A2, CPE, DHRS1, DCN, CYB5A, C1orf162, CYP4B1, COL12A1, HOPX, EIF2D, ARHGEF10L, ZDHHC2, ST6GALNAC5, FCGR2B, MAP3K5, ACRC, CRYAB, PARVA, C8orf4, SLC40A1, CORO2B, ITGA8, IL1RL1, MS4A6A, IFITM2, BHMT2, FRMD6, GBP2, ATP1B2, LINC01314, USP53, G0S2, C10orf10, SHC3, CTGF, CD163, DPT, PTGDS, IGSF10, NKD2,CXCL12, ALDH1A1, CARTPT, PPAP2B, C1QB, CBLN4, BRINP2, IFI35, TLR4, ZNF185, C9orf24, TMOD1, LPAR1, ADAMTSL3, CRISPLD2, SELENBP1, FOSL1, CNTN6, S100A9, KLHL2, LRFN5, GLT8D2, CNN1, SIGLEC9, ALDH3A2, PLEKHO1, SLCO2A1, MEIS2, PRPS2, TLE2, ACSF2, HCK, CSRP1, MAP7, DGAT1, NPY1R, TPD52L1, SHISA8, GPR182, MRC1, IGFBP5, PTGER4, KCNQ1, ANGPTL1, IGSF21, CD14, TRIP6, KLHDC8A, EMCN, SLC27A6, ISLR, DKK3, MS4A4A, MYLK, ACSBG1, PID1, ADORA3, RAI2, GCKR, FBP1, ST3GAL4-AS1, VASN, ALDH1A3, DOK2, SELM, BOC, TMEM61, PRELP, WFDC1, CYBRD1, PLEKHA6, HGF, CYP1B1, VAMP8, C1R, SQRDL, TRIM22, CD33, NR1H3, AADAC, CACHD1, GSTA4, ABCA1, C3AR1, CFH, CHGA, SLC1A5, MT1M, TNNC1, DUSP26, FBLN1, SLC16A9, CD248, LMOD1, ZRANB1, LAT2, VSIG4, THRSP, FMO1, ARNTL, CCL2, FAM179A, RBKS, TAGLN, KCNK2, MOXD1, MFAP4, DLG2, ARHGAP9, PLEK2, RBP4, S100A4, PROK1, ACKR1, CREG1, FNDC5, PCDH10, NEXN, GATA5, BICC1, INMT, ITM2A, MPDZ, TMEM220, ADH1B, CAB39L, FSTL3, FCN3, GATA6-AS1, GAREM, KDM5D, VIPR1, GRAMD3, HCLS1

100

200

300

400

500

All

469

Biological regulation

326

Response to stimulus

289

Metabolic process

274

Multicellular organismal process

231

(a)

Localization

221

100

200

300

400

500

Developmental process

204

Cell communication

203

All

469

Cellular component organization

177

Protein binding

317

Cell proliferation

Ion binding

164

Multi-organism process -

FIGURE 2: Continued.

Hydrolase activity

Nucleotide binding

Reproduction -

Transferase activity

Growth

Molecular transducer activity

Transporter activity

Unclassified

(c)

Nucleic acid binding

Lipid binding

100

200

300

400

500

Structural molecule activity

Enzyme regulator activity

Carbohydrate binding

All

469

Molecular adaptor activity

Membrane

249

Antioxidant activity

Endomembrane system

152

Chromatin binding

Vesicle

147

Extracellular space

141

Oxygen binding

Nucleus

134

Electron transfer activity

Cytosol

122

Translation regulator activity

Membrane-enclosed lumen

109

Unclassified

Protein-containing complex

102

(b)

Endoplasmic reticulum

Cell projection -

Cytoskeleton -

Golgi apparatus

Extracellular matrix

Mitochondrion

Vacuole

Envelope

Endosome

Chromosome

Microbody

Lipid droplet -

Ribosome

Unclassified

FIGURE 2: GO analysis was performed for DEGs in ACC. (a-c) Histograms show the results of GO analysis. (d-e) Hierarchical plots show the results of the GO analysis.

Movement of cell or subcellular component

Defense response

Secretion

Response to inorganic substance

Regulation of response to external stimulus

Inflammatory response

Regulation of inflammatory response

Extracellular matrix

Regulation of acute inflammatory response

Collagen-containing extracellular matrix

(d)

(e)

1 (SF-1), another gene that plays an essential role in promot- ing the occurrence and development of adrenal tumors, is of great significance to the growth and migration of adrenal tumor cells. In vivo experiments have proved that overex- pression of SF-1 promotes the proliferation and migration of adrenocortical adenocarcinoma cells [20]. In addition, multiple studies have also confirmed that SF-1 has a high value in the diagnosis of adrenocortical carcinoma and the prognosis evaluation of patients [21-23], and it has been reported that SF-1 overexpression is associated with a low survival rate in patients with adrenocortical carcinoma. In addition, Snail is closely related to the metastasis and prog- nosis of adrenocortical carcinoma. The relevant research results show that more than 95% of the clinical stage III and IV adrenocortical carcinoma tumors have positive Snail expression [24]; ER-negative expression adrenal cortical car- cinoma patients have a lower 5-year survival rate than those with ER-positive expression and have a greater chance of distant metastasis [25, 26]. In addition, the simultaneous high expression of BUB1B and PINK1 in tumor tissue may indicate a good prognosis in patients [27]. Therefore, the study of these differential gene expression profiles through

bioinformatics analysis plays a crucial role in understanding the pathogenesis of adrenocortical adenocarcinoma and the molecular signaling pathways involved [28].

We first downloaded raw data from GEO and TCGA databases in this study to obtain differentially expressed genes (DEGs) in ACC. Then, we performed gene ontology, pathway enrichment analysis, and protein-protein interac- tion (PPI) network. GEPIA was adopted to observe these genes’ mutations, differential expression, and prognostic characteristics. Besides, TCGA and cBioPortal were used to determine the distribution in pan cancers, pathway enrich- ment, the features in pathological parameters, and the rela- tionship with other genes. We attempted to seek specific hub genes that may serve as influential biomarkers for ACC.

2. Materials and Methods

2.1. GEO Database. GEO is a gene expression database cre- ated and maintained by the National Center for Biotechnol- ogy Information NCBI. The database was built in 2000 and contains high-throughput gene expression data from research institutions worldwide. In this study, GEO database

FIGURE 3: Continued.

RXFP1

AVPR1A

ADORA3

PTGER4

VIPR1

NPY5R

ADH1B

PTH1R

GPX8

ACKR1

TAC1

EDNRB

EPHA2

Neuroactive ligand-receptor interaction

GSTA4

HSD11B1

HGF

MC2R

RRM2

Metabolism of xenobiotics bx cytochrome P450

TEK

PDGFRA

LPAR1

CCL2

NPY1R

MGST1

Glutathione metabolism

RAPGEF4

GPX3

ALDH1A3

Rap 1/signaling pathway

Malarla

Staphylococcus

C5AR1C3AR1

ID1

NCF4 aureus infection

MARCO

GGT5

CYP1B1

PDGFD

MROI

IL1D

ITGAM

CD55

THBS1

Complement and coagulation

ÍGF1

ILRI

C1R

VSIG4

cascades

SERPING

Phagosome

THBD

HSD3B2

Perfussls

FCGR2B

C1QB C1QC

CFH

Ovarian steroidogenesis

COLEC11

C1S

CFD

F13A1

CD14

PLA2G4A

CYP17A1

(a)

KEGG

-log10 (P value)

Tuberculosis

Steroid hormone biosynthesis

Staphylococcus aureus infection

Ras signaling pathway

Rap1 signaling pathway

10.0

Proteoglycans in cancer

PPAR signaling pathway

Pathway name

PI3K-Akt signaling pathway

Phagosome

Pertussis

7.5

Neuroactive ligand-receptor interaction

Metabolic pathways

Malaria

Glutathione metabolism

Focal adhesion

Drug metabolism - cytochrome P450

5.0

Cytokine-cytokine receptor interaction

Complement and coagulation cascades

Aldosterone-regulated sodium reabsorption

@@Arachidonic acid metabolism

0.0000 0.0005 0.0010 0.0015

P value

Count

(b)

FIGURE 3: Continued.

BIOCYC

-log10 (P value)

Superpathway of tryptophan utilization

Superpathway of steroid hormone biosynthesis

Stearate biosynthesis

Serotonin degradation

Retinoate biosynthesis I

Reactive oxygen species degradation

Noradrenaline and adrenaline degradation

Pathway name

Nicotine degradation IV

Glutathione-mediated detoxification

Gamma-linolenate biosynthesis

Fatty acid beta-oxidation (peroxisome)

Fatty acid beta-oxidation

Fatty acid alpha-oxidation III

Fatty acid alpha-oxidation

Fatty acid activation

BMP signalling pathway

Bile acid biosynthesis, neutral pathway

Androgen biosynthesis

Acetone degradation I (to methylglyoxal)

4-hydroxy-2-nonenal detoxification

0.00 0.01 0.02 0.03 0.04

P value

Count

(c)

REACTOME

-log10 (P value)

Transmembrane transport of small molecules

Signal transduction

Phase 1 - functionalization of compounds

Peptide ligand-binding receptors

Molecules associated with elastic fibres

Metabolism of lipids and lipoproteins

Metabolism

Innate immune system

Initial triggering of complement

Immune system

Hemostasis

GPCR ligand binding

Extracellular matrix organization

Elastic fibre formation

Defective ST3GAL3 causes MCT12 and EIEE15

Creation of C4 and C2 activators

Complement cascade

Classical antibody-mediated complement activation

Class A/1 (Rhodopsin-like receptors)

Biological oxidations

0e+00 2e-05 4e-05 6e-05

P value

Count

(d)

Pathway name

FIGURE 3: Pathway enrichment analysis was performed for DEGs in ACC. (a) Interaction plot showing the results of pathway enrichment analysis. (b-e) Bubble plots show KEGG, BIOCYC, REACTOME, and PANTHER pathway enrichment analysis results.

PANTHER

-log10 (P value)

Oxidative stress response

2.2

Nicotine degradation

2.0

Pathway name

Integrin signaling pathway

Inflammation mediated by chemokine and cytokine signaling pathway

1.8

Dopamine receptor mediated signaling pathway

1.6

Angiogenesis

5-hydroxytryptamine degradation

1.4

0.01

0.02

0.03

0.04

P value

Count

☒ 6

☒ 4

☒ 7

☒ 5

☒ 8

(e)

(http://www.ncbi.nlm.nih.gov/geo/) [29] was used for gene expression data sets between ACC tissues and normal tis- sues. Then, we further evaluated the complete information about the relevant data sets. Finally, in line with the Affyme- trix Human Genome (GPL570) platform, two data sets (GSE19750 and GSE10927) were chosen for subsequent analysis. The GSE19507 data set contained 44 ACC and 4 normal samples [30, 31], and the GSE10927 data set included 33 ACC and 10 normal samples [32].

2.2. Differential Expression Analysis. R language was used to analyze GEO data and drew volcano maps and heat maps, and these two data sets were employed to get differential expressed genes (DEGs). |Log2FC|>1, P-value <0.05 was considered the cutoff criterion. Besides, we put on these data to cross with TCGA data [33]. Then, an online tool, Bioin- formatics & Evolutionary Genomics, was used to draw the Venn diagram for up-regulated and down-regulated DEGs (http://bioinformatics.psb.ugent.be/webtools/Venn/) [34].

2.3. Gene Ontology and Pathway Enrichment Analysis. The up-regulated and down-regulated DEGs were integrated into the WEB-based Gene Set Analysis Toolkit (webgestalt) (http://www.webgestalt.org/) [35] for Gene Ontology (GO) functional annotation enrichment analysis. Furthermore, we performed KEGG pathway analysis for DEGs through the ClueGO plugin in Cytoscape software [36]. The KEGG [37], REACTOME [38], PANTHER [39], and BIOCYC

[40] pathways were downloaded from the KOBAS website [41]. A P-value of <0.05 was considered statistically significant.

2.4. Protein-Protein Interaction (PPI) Network and Identification of Hub Genes. String database is a database that can be used to search for interactions between known and predicted proteins. In addition to generating beautiful protein-protein-interaction (PPI) maps of these proteins, an analysis of imported proteins is also provided. In this study, PPI network between DEGs was built by String data- base (http://stringdb.org/) [42]. First, entered the DEGs into the database and set the confidence score ≥0.7. Then, removed unlinked DEGs and arranged the remaining DEGs protein interaction data and photos. The data acquired by String website was substituted into the Cytoscape software and the hub genes were captured through the cytoHubba plugin. Afterward, the top 20 genes were collected by three algorithms of Degree, MCC, and MNC [43]. The Venn dia- gram of these hub genes was gathered using the online tool Bioinformatics & Evolutionary Genomics.

2.5. Gene Expression Analysis and Survival Analysis. GEPIA (http://gepia.cancerpku.cn/detail.php) [44] is a newly devel- oped interactive web server for analyzing RNA sequencing expression data of 9736 tumors and 8587 normal samples in TCGA and GTEX projects. Based on GEPIA database, we checked the differences in hub gene expression between

FIGURE 4: Continued.

ARFGAP3

KIF11

RACGAP1

RRM2

KCNQ1

CD55

TOP2A

ANLN

KIF20A

MND1

SCNN1A

KIF4A2

NDC80

NEDD4L

CENPU

SGK1

NCAPG

MAD2L1

CENPK

TPX2

CDC20

CENPH

KLHL2

FXYD1

CCNB

DKK3

TMOD1

TNNC1

FOXMI-

DUOX1

ACRC

SFRP4

IFI35

PLN

TRIM22

ATP1B2

GBP2

RAPGEF4

SLC1A5

FBLNS

NCF4

FOSL2

NR2F1

EFEMP2

IFITM2

NEFH

FOSL1

MFAP4

KLF2

MFAPS

ID1

PON1

MAPK13

FBIN1

ABCA1

NPC1

MYC

PLTP

IGFBP6 SPON1

EFEMP1

MRPL.33

APOC

NR1H ERN1

ADAMTSL2

CHRDL1

MAP3K5

THBD

ADAMTSI

SL3

IGFBP5

ADAMTS1

NOV

FSTL1

APOE

SREBFIP

GFBP4

ANGPTL1

THBS1 TIMP4

ECM1

FST1.3

LMOD1

MYLK

RARRES2

DOK2

IGF1

SCUBE3

COL12A1

CYR61

ILAR

PLAT

LAMA2

CFD

NGFR

COL4A4

HIBCH

WISP1

PPAP2B

CSF3R

PDGER

IL1RL1

ITGA8

TEK

ISLR

IL10

LUM

B4GALT6

F13AT DCN

ACADVL

AM2

11.33”

CTGĘ

PDGERA

PRELP

EPHX2

ACKR1

DPT

OGN

DGAT1

ACSBG1

ITGAM

CC12

EPHA2

SERPING1

TNFRSF1

OMD

ACOX2

PTPRB

MRC1

SHC3

RBKS

AOX1

RETSAT

STXI

SLC27A2

CLECSA

SIGLEC1

TER4

NGER

CD163

AXL

PRPS2

STON1:

CD33

C1R

HCK

CYBRD1

VAMP8

CCR1

C1QC

CD14 CXCLI

C1S

PACSIN3

C3AR

CXCL2

ACO1

ADH1B

FGR

S100A9

SLCAOA1

ALDH1A1

TPD52L1

FCGR2B

FCER1G

BTK

WWV CIQB

COLEC11

FCN3

HEPH

ALDH1A3

ALDH3A2

NPYSR

NPY JR

S100A8

SLA

GLUL

DORA3 7

STAB1

LILRA2

CSAR1

VSIG4

GATA6

GATA5

GPX3

MGST1

MS4A6A

HCLS1

SLC9A3R1

MARCO

GSTA4

AKR1B1

LPAR1

PLA2G4A

MRAP

GGT5

LAT2

SLCO2B1

CYB5A

GPX8

CYP1B1

C1orf162

MS4A14

CFH

MC2R

AMT

MS4A4A

GGH

TAC1

APOD

HTR2B

RXFP1

CYP11B1

CYP17A1

EDNRB

PLA2G1B

GREG1

ALOXSAP

AVPR1A

PROK1

PTH1R

RNASE2

HSD11B1

HSDB2

NR4A2

PTGER4

CDKN1C

RAMP3

VIPRIS

(a)

Top20 hub genes (degree)

KIF11

KIF4A

PBK

TPX2

C3AR1

ITGAM

NUF2

FOXM1

IGF1

NDC80

CDC20

CENPU

TOP2A

CCNB1

MAD2L1

RRM2

CXCL12

*=== RACGAP1

-4

KIF20A

NCAPG

(b)

FIGURE 4: Protein-protein interaction analysis and screening of hub genes of DEGs. (a) The protein-protein interaction network of these DEGs molecules. (b) Top 20 hub genes screened by Degree algorithm. (c) Top 20 hub genes screened by MCC algorithm. (d) Top 20 hub genes screened by MNC algorithm. (e) A Venn diagram is drawn based on the hub genes obtained by Degree, MCC, and MNC algorithms.

Top20 hub genes (MCC)

KIF11

RRM2

CXCL12

CDC20

ANLN

PBK

NCAPG

NUF2

KIF20A

CCNB1

CCR1

TOP2A

NDC80

CENPU

MAD2L1

RACGAP1

FOXM1

KIF4A

C3AR1

TPX2

(c)

Top20 hub genes (MNC)

RACGAP1

ANLN

CENPU

Degree

MCC

KIF20A

IGF1

FOXM1

CCNB1

NDC80

KIF11

RRM2

CDC20

ITGAM

NCAPG

TPX2

C3AR1

MAD2L1

PBK

KIF4A

TOP2A

NUF2

MNC

(d)

(e)

ACC and normal tissues. The predictive value of these genes in ACC was analyzed using the GEPIA database, and the cutoff value was set to 50%. The website automatically calcu- lated the hazard ratio (HR) of 95% confidence interval and log-rank P-value and displayed it directly on the web page. P-value <0.05 was considered statistically significant.

2.6. TCGA and cBioPortal Data. The cancer genome map included sequencing and pathology data for 30 different cancers. The ACC (TCGA, Provisional) data set was selected, comprising data from 92 pathology reports. These DEGs were further conducted via cbioportal (http://www .cbioportal.org/index.do) [45]. The genomic analysis is covered with mutations and co-expression analysis. The co-expression and networking were calculated based on

cbioportal’s online instructions. P-value <0.05 was consid- ered statistically significant.

2.7. Statistical Analysis. Statistical analyses of all data were performed using statistical software from all online data- bases. Statistical significance of differences between and among groups was assessed using the t-test. Statistical signif- icance was set at *P< 0.05; ** P < 0.01; and *** P < 0.001.

3. Results

3.1. DEGs in ACC. In recent decades, differentially expressed genes have been the focus of research in the field of cancer research. DEGs in ACC were identified by examining two GEO data sets and TCGA data (Figures 1(a) and 1(b)). 490 DEGs consisting of 28 up-regulated genes and 462 down-

Profiled in mutations Profiled in protein expression Z-scores (RPPA)		Variation of 17 Hub genes

Profiled in putative copy-number alterations from GISTIC
Profiled in mRNA Expression z-scores (RNA seq V2 RSEM)
CDC20	4%
C3AR1	7%
NDC80	9%
CCNB1	9%
MAD2L1	10%
NUF2	8%
NCAPG	9%
PBK	11%
RACGAP1	7%
KIF20A	2.2%
KIF11	7%
TOP2A	9%
KIF4A	9%
RRM2	9%
FOXM1	12%
TPX2	5%
CENPU	13%

Genetic alteration

Missense mutation (unknown significance)

Truncating mutation (unknown significance)

Amplification

Deep deletion

mRNA high

Protein high

No alterations

Profiled in mutations	Yes	No
Profiled in protein expression Z-scores (RPPA)	Yes	No
Profiled in putative copy-number alterations from GISTIC	Yes	No
Profiled in mRNA expression z-scores (RNA seq V2 RSEM)	Yes	No

(a)

	ACC	KICH	KIRC	KIRP	PAAD	BLCA
CDC20 (N)
C3AR1 (N)
NDC80 (N) -
CCNB1 (N) -
MAD2L1 (N) -
NUF2 (N) -
NCAPG (N) -
PBK (N) -
ACGAP1 (N) -
KIF20A (N) -
KIF11 (N) -
TOP2A (N) -
KIF4A (N) -
RRM2 (N) -
FOXM1 (N) -
TPX2 (N) -
CENPU (N) - (N) -

FIGURE 5: Continued.

(b)

C3AR1

CCNB

CDC20

⁎

Expression -log, (TPM + 1)

. ***

ACC

(num (T) = 77; num (N) = 128)

ACC (num (T) = 77; num (N) = 128)

CENPU

FOXM1

KIF4A

Expression -log2 (TPM + 1)

2 -

1 -

0 -

ACC (num (T) = 77; num (N) = 128)

(c)

FIGURE 5: Continued.

FIGURE 5: Overall variation and mRNA expression of 17 hub genes in urological tumors. (a) Overall variation of 17 hub genes in ACC. (b) Expression of 17 central genes in urologic tumors. (c-d) mRNA differential expression of 17 hub genes in ACC.

KIF11

KIF20A

MAD2L1

NCAPG

NDC80

NUF2

⁎

Expression -log, (TPM + 1)

“

Y’,

”

ACC

(num (T) = 77; num (N) = 128)

PBK

PACGAP1

RRM2

TOP2A

TPX2

⁎

Expression -log, (TPM + 1)

ACC

(num (T) = 77; num (N) = 128)

(num (T) =77; num (N) = 128)

(num (T) = 77; num (N) = 128)

ACC (num (T) = 77; num (N) = 128)

(d)

regulated genes were finally obtained in our work (Figure 1(d), Table 1). In addition, to show the distribution of these DEGs on human chromosomes more specifically, we draw the corresponding heatmaps. The results showed that over-expressed genes were mainly distributed on chro- mosomes 5, 7, and 12 (Figure 1(c)).

3.2. Functional Enrichment of DEGs. GO functional enrich- ment analysis was performed on these DEGs, demonstrating that biological regulation, membrane, and protein binding of most genes were enriched in terms of BP, CC, and MF, respec- tively (Figures 2(a)-2(e)). Four pathway databases with KEGG, BIOCYE, REACTOME, and PANTHER revealed that ACC-related DEGs mainly concentrated on complement and coagulation cascades, metabolic pathways, malaria, ovarian steroidogenesis, and so on (Figures 3(a)-3(e)).

3.3. Identification of ACC-Associated Hub Gene. String data- base was applied to analyze the protein interactions of DEGs and make a PPI network (Figure 4(a)). The top 20 ACC- related hub genes were screened through three algorithms involving Degree, MCC, and MNC. After taking the inter- section of these three data sets, 17 hub genes containing C3AR1, CCNB1, CDC20, CENPU, FOXM1, KIF4A, KIF11, KIF20A, MAD2L1, NCAPG, NDC80, NUF2, PBK, RAC- GAP1, RRM2, TOP2A, and TPX2 were collected for further study (Figures 4(b)-4(e)).

3.4. Hub Gene Expression and Prognosis in ACC. To better make out the 17 hub genes, we analyzed the mutations of

17 hub genes. The results showed that CENPU, FOXM1, and PBK had higher mutation rates accounting for 13%, 12%, and 11%, respectively (Figure 5(a)). Subsequently, we detected the expression of these hub genes in six tumors, including ACC, KICH, KIRC, KIRP, PAAD, and BLCA. CCNB1, MAD2L1, ACGAP1, and CENPU were signifi- cantly higher expressed in all six tumors (Figure 5(b)). Another discovery is that the expression analysis of these genes in ACC manifested that except for C3AR1, which was down-regulated in ACC, the other 16 genes were up- regulated in ACC (Figures 5(c) and 5(d)). In addition, we also found no significant correlation between C3AR1 and the prognosis of patients with ACC. Still, the rest of the hub genes had a great connection with an unfavorable prog- nosis (Figures 6(a)-6(q) and 7(a)-7(q)).

3.5. Functional Enrichment of Hub Genes. In cancer research, gene function enrichment analysis has become a routine method for high-throughput omics data analysis, which is of great significance for revealing biomedical molec- ular mechanisms. To better understand these hub genes’ function, pathway enrichment analysis was performed on these 16 hub genes again, which suggested that hub genes were mainly associated with classical tumor-associated path- ways, such as the P53 signaling pathway, and cell cycle- related signaling pathways (Figures 8(a)-8(d)).

3.6. Identification of Two ACC Core Genes CCNB1 and NDC80. By duplicating protein interaction analysis on these

FIGURE 6: Continued.

1.0

Logrank p = 0.75

Logrank p = le-04

0.8

HR (high) = 1:1

P.(HR) = 0.74

0.8

HR (high) = 4.9

n (high) = 38

p (HR) = 0.00038

n (high) = 38

Percent survival

n (low) = 38

0.6

Percent survival

n (low) = 38

0.6

0.4

0.2

0.0

C3AR1

0.0

CCNB1

100

150

100

150

Months

Low C3AR1 group

Low CCNB1 group

High C3AR1 group

High CCNB1 group

(a)

(b)

1.0

Logrank p = 2.9e-06

Logrank p = 2.8e-05

0.8

HR (high) = 7.4

p (HR) = 5.8e-05

0.8

HR (high) = 5.6

In (high) = 38

P (HR) = 0.00014

n (low) = 38

n (high) = 38

Percent survival

” (low) = 38

0.6

Percent survival

0.6

0.4

0.2

0.0

CDC20

0.0

CENPU

100

150

100

150

Months

Low CDC20 group

Low CENPU group

High CDC20 group

High CENPU group

(c)

(d)

1.0

Logrank p = 0.00011

Logrank p = 1.5e-06

0.8

HR (high) = 4.9

p (HR) = 4e-04

0.8

HR (high) = 7.8

n (high) = 38

p (HR) = 3.8e-05

n (high) = 38

Percent survival

n (low) = 38

0.6

Percent survival

n (low) = 38

0.6

0.4

0.2

0.0

FOXM1

0.0

KIF4A

100

150

100

150

Months

Low FOXM1 group

Low KIF4A group

High FOXM1 group

High KIF4A group

(e)

(f)

FIGURE 6: Continued.

1.0

Logrank p = 3.3e-08

Logrank p = 1.1e-05

0.8

HR (high) = 13

p (HR) = 5.8e-06

0.8

HR (high) = 6.4

n (high) = 38

p (HR) = 9.4e-05

n (high) = 38

Percent survival

n (low) = 38

0.6

Percent survival

n (low) = 38

0.6

0.4

0.2

0.0

KIF11

0.0

KIF20A

100

150

100

150

Months

Low KIF11 group

Low KIF20A group

High KIF11 group

High KIF20A group

(g)

(h)

1.0

Logrank p = 0.00038

0.8

HR (high) = 4.4

Logrank p = 2e-05

p.(HR) = 0.001

0.8

HR (high) = 6

n (high) = 38

p (HR) = 0.00015

n (high) = 38

Percent survival

n (low) = 38

0.6

Percent survival

0.6

0.4

0.2

0.0

MAD2L1

0.0

NCAPG

100

150

100

150

Months

Low MAD2L1 group

Low NCAPG group

High MAD2L1 group

High NCAPG group

(i)

(j)

1.0

Logrank p = 8.8e-05

Logrank p = 4.6e-07

0.8

HR (high) = 4.9

P (HR) = 0.00034

0.8

HR (high) = 8.9

n (high) = 38

p (HR) = 1.7e-05

+ni(high) - 38

Percent survival

n (low) = 38

0.6

Percent survival

n (low) = 37

0.6

0.4

0.2

0.0

NDC80

0.0

NUF2

100

150

100

150

Months

Low NDC80 group

Low NUF2 group

High NDC80 group

High NUF2 group

(k)

(l)

FIGURE 6: Overall survival analysis. (a-q) Survival graphs showing the overall survival of these 17 hub genes in ACC, in order of C3AR1, CCNB1, CDC20, CENPU, FOXM1, KIF4A, KIF11, KIF20A, MAD2L1, NCAPG, NDC80, NUF2, PBK, RACGAP1, RRM2, TOP2A, and TPX2.

1.0

Logrank p = 7.5e-05

Logrank p = 7.3e-06

0.8

HR (high) = 5.3

₱ (HR) = 0.00036

0.8

HR (high) = 7.1

p (HR) =9.8e-05

n (high) = 38

Percent survival

n (low) = 38

0.6

Percent survival

0.6

0.4

0.2

0.0

PBK

RACGAP1

0.0

100

150

100

150

Months

Low PBK group

Low RACGAP1 group

High PBK group

High RACGAP1 group

(m)

(n)

1.0

Logrank p = 0.00033

HR (high) = 4.4

Logrank p = 0.00014

0.8

HR (high) = 4.7

₱ (HR) = 0.00092

0.8

n (high) = 38

P (HR) = 0.00047

n (high) = 38

Percent survival

n (low) = 38

0.6

Percent survival

0.6

0.4

0.2

RRM2

TOP2A

0.0

100

150

100

150

Months

Low RRM2 group

Low TOP2A group

High RRM2 group

High TOP2A group

(o)

(p)

1.0

Logrank p = 0.00053

0.8

HR (high) = 4.1

P (HR) = 0.0012

n (high) = 38

Percent survival

# (low) =38

0.6

0.4

0.2

TPX2

0.0

100

150

Months

Low TPX2 group

High TPX2 group

(q)

FIGURE 7: Continued.

1.0

Logrank p = 0.24

1.0

Logrank p = 0.0095

HR (high) = 0.67

HR (high) = 2.4

₱ (HR) = 0.24

p (HR) = 0.011

0.8

n (high) = 38

0.8

n (high). = 38

n (low) = 38

Percent survival

0.6

Percent survival

0.6

0.4

0.2

C3AR1

CCNB1

0.0

100

150

100

150

Months

Low C3AR1 group

Low CCNB1 group

High C3AR1 group

High CCNB1 group

(a)

(b)

1.0

Logrank p = 2e-04

1.0

Logrank p = 0.0065

HR (high) = 3.6

HR (high) = 2.6

P(HR)= 0.00046

p (HR) = 0.0083

0.8

n (high) = 38

0.8

n (high) = 38

n (low) = 38

Percent survival

0.6

Percent survival

0.6

0.4

0.2

CDC20

CENPU

0.0

100

150

100

150

Months

Low CDC20 group

Low CENPU group

High CDC20 group

High CENPU group

(c)

(d)

1.0

Logrank p = 0.00091

1.0

Logrank p = 0.0016

HR (high) = 3.2

HR (high) = 2.9

0.8

p.(HR). = 0.0016

₱ (HR) = 0.0024

n (high) = 38

0.8

n (high) = 38

n (low) = 38

Percent survival

0.6

Percent survival

0.6

0.4

0.2

FOXM1

KIF4A

0.0

100

150

100

150

Months

Low FOXM1 group

Low KIF4A group

High FOXM1 group

High KIF4A group

(e)

(f)

FIGURE 7: Continued.

1.0

Logrank p = 2.8e-06

1.0

Logrank p = 0.004

HR (high) = 5.3

HR (high) = 2.7

P (HR) =1.8e-05

₱ (HR) = 0.0055

0.8

n (high) = 38

0.8

n (high) = 38

n (low) = 38

Percent survival

0.6

Percent survival

0.6

0.4

0.2

KIF11

KIF20A

0.0

100

150

100

150

Months

Low KIF11 group

Low KIF20A group

High KIF11 group

High KIF20A group

(g)

(h)

1.0

Logrank p = 0.0046

1.0

Logrank p = 0.00025

HR (high) = 2.6

HR (high) = 3.6

₱ (HR) = 0.0061

p.(HR) =0.00056

0.8

n (high) = 38

0.8

n (high) = 38

n (low) = 38

Percent survival

0.6

0.4

0.2

MAD2L

NCAPG

0.0

100

150

100

150

Months

Low MAD2L group

Low NCAPG group

High MAD2L group

High NCAPG group

(i)

(j)

1.0

Logrank p = 0.00044

1.0

Logrank p = 3.6e-05

HR (high) = 3.4

HR (high) = 4.3

p.(HR) = 0.00087

P(HR) = 0.00013

0.8

n (high) = 38

0.8

n (high) = 38

n (low) = 38

Percent survival

0.6

Percent survival

0.6

0.4

0.2

NDC80

NUF2

0.0

100

150

100

150

Months

Low NDC80 group

Low NUF2 group

High NDC80 group

High NUF2 group

(k)

(l)

FIGURE 7: Disease-free survival analysis. (a-q) Survival graphs show the disease-free survival of these 17 hub genes in ACC, followed by C3AR1, CCNB1, CDC20, CENPU, FOXM1, KIF4A, KIF11, KIF20A, MAD2L1, NCAPG, NDC80, NUF2, PBK, RACGAP1, RRM2, TOP2A, and TPX2.

1.0

Logrank p = 0.0077

1.0

Logrank p = 7.6e-06

HR (high) = 2.5

HR (high) = 5

p (HR) = 0.0097

0.8

n (high) =38

P (HR) = 4.5e-05

0.8

n (low) = 38

n (high) = 38

# (low) =3&#

Percent survival

0.6

Percent survival

0.6

0.4

+ +

0.4

0.2

PBK

0.0

RACGAP1

0.0

100

150

Months

100

150

Low PBK group

Months

High PBK group

Low RACGAP1 group

High RACGAP1 group

(m)

(n)

1.0

Logrank p = 0.00042

1.0

Logrank p = 0.0036

HR (high) = 3.4

HR (high) = 2.7

p.(HR) =0.00085

p.(HR) = 0.0051

0.8

n (high) = 38

0.8

n (high) = 38

n (low) = 38

Percent survival

0.6

Percent survival

0.6

0.4

0.2

RRM2

TOP2A

0.0

100

150

100

150

Months

Low RRM2 group

Low TOP2A group

High RRM2 group

High TOP2A group

(o)

(p)

1.0

Logrank p = 0.00069

HR (high) = 3.2

P. (HR) = 0.0013

0.8

n (high) = 38

n (low) = 38

Percent survival

0.6

0.4

0.2

TPX2

0.0

100

150

Months

Low TPX2 group

High TPX2 group

(q)

FIGURE 8: Continued.

Pathway enrichment

-log10 (P value)

Spindle

Sister chromatid segregation

Single-organism organelle organization

Regulation of cell cycle process

20.0

Organelle organization

Organelle fission

Nuclear division

Pathway name

Nuclear chromosome segregation

17.5

Mitotic spindle organization

Mitotic nuclear division

Mitotic cell cycle process

Mitotic cell cycle

15.0

Microtubule cytoskeleton

Cytoskeletal part

Chromosome segregation

Cellular component organization or biogenesis

12.5

Cellular component organization

Cell division

Cell cycle process

Cell cycle

10.0

0e+00

1e-10

2e-10

P value

Count

☒ 10

☒ 15

(a)

Pathway enrichment

-log10 (P value)

Transcriptional misregulation in cancer

Staphylococcus aureus infection

Rap1 signaling pathway

Progesterone-mediated oocyte maturation

Pathway name

p53 signaling pathway

Oocyte meiosis

HTLV-I infection

FoxO signaling pathway

Complement and coagulation cascades

Cell cycle

Aldosterone-regulated sodium reabsorption

0.00

0.01

0.02

0.03

0.04

P value

Count

☒ 2

☒ 3

☒ 4

(b)

FIGURE 8: After removing the C3AR1 gene with no prognostic significance in ACC, pathway enrichment analysis was performed in ACC for the remaining 16 hub genes. (a) KEGG pathway. (b) BIOCYC pathway. (c) REACTOME pathway. (d) PANTHER pathway.

Pathway enrichment

-log10 (P value)

Signaling by Rho GTPases

Separation of sister chromatids

15.0

RHO GTPases activate formins

RHO GTPases effectors

Resolution of sister chromatid cohesion

Phosphorylation of Emi1

12.5

Pathway name

Mitotic prometaphase

Mitotic metaphase and anaphase

Mitotic anaphase

MHC class II antigen presentation

10.0

M phase

Kinesins

Intra-golgi and retrograde golgi-to-ER traffic

Immune system

7.5

Hemostasis

Golgi-to-ER retrograde transport

Factors involved in megakaryocyte development and platelet production

COPI-dependent golgi-toER retrograde traffic

Cell cycle, mitotic

5.0

Cell cycle

0.000000 0.000025 0.000050 0.000075 0.000100 0.000125

P value

Count

· 2

☒ 8

· 4

☒ 10

☒

(c)

Pathway enrichment -log10 (P value)

P53 pathway

2.2

Insulin/IGF pathway-protein kinase B signaling cascade

Pathway name

2.0

Insulin/IGF pathway-mitogen activated kinase kinase/MAP

kinase cascade

DNA replication

1.8

De novo pyrimidine deoxyribonucleotide biosynthesis

1.6

De novo purine biosynthesis

1.4

0.01 0.02 0.03 0.04

P value

Count

· 1.00

· 1.25

☒ 1.50

☒ 1.75

☒

2.00

(d)

16 hub genes and narrowing the core gene range, we derived two core genes, CCNB1 and NDC80 (Figure 9(a)). Then, we evaluated the expression of these two genes in pan cancers, and the consequences proved that these two genes were highly expressed in various tumors (Figures 9(b) and 10(a)). Further analysis suggested that the expression of CCNB1 and NDC80 would increase with disease progres- sion. The high expression could also predict adverse out- comes in ACC patients but has little to do with gender (Figures 9(c) and 9(d) and 10(b) and 10(c)). To improve our knowledge about the functions of the core genes CCNB1

and NDC80, ten related proteins were retrieved by the String database (Figures 9(e) and 10(d)). Later, we discov- ered that CCNB1 and NDC80 participate in the same path- way, incorporated with cell cycle, progesterone-mediated oocyte maturation, HTLV-1 infection, and oocyte meiosis (Figures 11(a) and 11(b)). CCNB1 co-expressed with its related proteins CDK1, CDK2, CCNB2, PLK1, CDC20, CDCA8, ESPL1, and FZR1 (Figures 11(c)-11(j)) in ACC patients. Pathway analysis for NDC80 showed that NDC80 was associated with Cell Cycle (Figure 12(a)). It was worth mentioning that CCNB1 and NDC80 were

CCNB1 expression level (log, RSEM)

ACC.tumor

BLCA.tumor

BLCA.normal

BRCA.tumor

BRCA.normal

BRCA-basal.tumor

BRCA-her2.tumor

BRCA-luminal.tumor

CESC.tumor

CHOL.tumor

CHOL.normal

COAD.tumor

COAD.normal

DLBC.tumor

ESCA.tumor

ESCA.normal

GBM.tumor

HNSC.tumor

HNSC.normal

Degree

HNSC-HPVpos.tumor

HNSC-HPVneg.tumor

KICH.tumor

⁎

KICH.normal

KIRC.tumor

MNC

KIRC.normal

KIRP.tumor

KIRP.normal

CCNB1

(a)

LAML.tumor

LGG.tumor

MCC

LIHC.tumor

LIHC.normal

LUAD.tumor

NDC80 CCNB1

LUAD.normal

LUSC.tumor

LUSC.normal

MESO.tumor

OV.tumor

PAAD.tumor

PCPG.tumor

PRAD.tumor

PRAD.normal

READ.tumor

READ.normal

SARC.tumor

SKCM.tumor

SKCM.metastasis

STAD.tumor

STAD.normal

TGCT.tumor

THCA.tumor

THCA.normal

THYM.tumor

UCEC.tumor

UCEC.normal

CUS.tumor

UVM.tumor

Transcript per million

100

125

150

175

(n =9)

Stage 1

FIGURE 9: Continued.

(n = 37)

Stage 2

(c)

TCGA samples

(n = 16) Stage 3

(n = 15) Stage 4

Expression of CCNB1 in ACC based on individual cancer

(b)

stages

FIGURE 9: In-depth exploration of the biological value of the core gene CCNB1. (a) Venn diagram showing the identification of the core genes CCNB1 and NDC80. (b) mRNA expression of CCNB1 in pan-cancer. (c) mRNA expression of CCNB1 in different stages of ACC. The P-value between stage 1 and stage 4 is 2.2252E-04. (d) The effect of CCNB1 mRNA expression level and patient gender on the overall survival of ACC patients. (e) PPI map between CCNB1 and the ten most closely related CCNB1 protein molecules.

Effect of CCNB1 expression level & gender on ACC patient survival

1.00

Survival probability

0.75

0.50

0.25

P < 0.0001

0.00

1000

2000

3000

4000

Time in days

Expression level, gender

++ High expression + female (n = 14)

+ High expression + male (n = 6)

+ Low/medium expression + female (n = 34)

+ Low/medium + male (n = 25)

(d)

CDK2

ESPL1

CDK1

ANAPC10

ANAPC4

CCNB1

CCNB2

FZR1

CDC20

PLK1

CDC27

(e)

consistently expressed in ACC (Figure 12(b)). Simulta- neously, the expression of NDC80 also has collinearity with several proteins, like AURKB, BUB1, SPC25, and CENPE (Figures 12(c)-12(f)).

4. Discussion

In the past 20 years, molecular biology studies on ACC have made significant progress [46, 47], but this cancer’s primary pathogenesis is still unclear. Moreover, recent epidemiologi- cal studies have shown that the incidence of ACC has increased yearly in the past 40 years, but the survival rate

of patients has not improved [3]. As a highly malignant tumor, there is an urgent need to find effective diagnostic and prognostic targets for identifying early-stage patients, developing proper treatments, and improving ACC’s poor prognosis. Therefore, using bioinformatics techniques to unravel the genomic properties of ACC at the molecular level is crucial for finding effective treatments and predict- ing patient survival and relapse risk, and there have been several successful cases of bioinformatics used in cancer research [48-51].

Our research selected GSE10927 (10 normal and 33 ACC tissues) and GSE19750 (4 normal and 44 ACC tissues)

FIGURE 10: In-depth exploration of the biological value of the core gene NDC80. (a) mRNA expression of NDC80 in pan-cancer. (b) mRNA expression of NDC80 in different stages of ACC. The P-value between stage 1 and stage 4 is 5.7562E-03. (c) The effect of NDC80 mRNA expression level and patient gender on the overall survival of ACC patients. (d) PPI map between NDC80 and the ten most closely related NDC80 protein molecules.

NDC80 expression level (log, RSEM)

NDC80

ACC.tumor

BLCA.tumor

BLCA.normal

BRCA.tumor

BRCA.normal

BRCA-basal.tumor

BRCA-her2.tumor

BRCA-luminal.tumor

CESC.tumor

CHOL.tumor

CHOL.normal

COAD.tumor

COAD.normal

DLBC.tumor

ESCA.tumor

ESCA.normal

GBM.tumor

HNSC.tumor

HNSC.normal

HNSC-HPVpos.tumor

HNSC-HPVneg.tumor

KICH.tumor

KICH.normal

KIRC.tumor

KIRC.normal

KIRP.tumor

KIRP.normal

LAML.tumor

LGG.tumor

LIHC.tumor

LIHC.normal

LUAD.tumor

LUAD.normal

LUSC.tumor

LUSC.normal

MESO.tumor

OV.tumor

PAAD.tumor

PCPG.tumor

PRAD.tumor

PRAD.normal

READ.tumor

READ.normal

SARC.tumor

SKCM.tumor

SKCM.metastasis

STAD.tumor

STAD.normal

TGCT.tumor

THCA.tumor

THCA.normal

THYM.tumor

UCEC.tumor

UCEC.normal

CUS.tumor

UVM.tumor

(a)

Expression of NDC80 in ACC based on individual cancer stages

Effect of NDC80 expression level & gender on ACC patient survival

1.00

Transcript per million

0.75

Survival probability

0.50

0.25

-5

p < 0.0001

Stage 1 (n = 9)

Stage 2 (n = 37)

Stage 3 (n = 16)

Stage 4 (n = 15)

TCGA samples

0.00

1000

2000

3000

4000

Time in days

Expression level, gender

++ High expression + female (n = 12)

++ High expression + male (n = 8)

+ Low/medium expression + female (n = 36)

+ Low/medium + male (n = 23)

(b)

(c)

SPC25

CASC5

AURKB

MAD2L1

SPC24

NDC80

BUB1B

NUF2

CENPE

BUB1

ZWINT

(d)

FIGURE 11: Continued.

CDC27

PLK1

FZR1

ANAPC4

ANAPC10

CCNB2

Cell cycle

Progesterone-mediated

ESPL1

oocyte

CCNB1

maturation

CDC20

CDK2

CDK1

Oocyte meiosis

P53 signaling pathway

(a)

Pathway enrichment

-log10 (P value)

Viral carcinogenesis

Ubiquitin mediated proteolysis

Small cell lung cancer

Prostate cancer

Pathway name

Progesterone-mediated oocyte maturation

P53 signaling pathway

Oocyte meiosis

HTLV-infection

Herpes simplex infection

Gap junction

FoxO signaling pathway

Epstein-barr virus infection

0.00

0.01

0.02

0.03

P value

Count

☒

2.5

☒

7.5

☒

5.0

☒ 10.0

(b)

FIGURE 11: Continued.

CCNB1 vs. CDK1

CCNB1 vs. CCNB2

mRNA expression (RNA seq V2 RSEM): CDK1 (log2)

mRNA expression (RNA seq V2 RSEM): CCNB2 (log2)

y = 1.3x+ -9.80

y =1.42x

.53

= 0,65

mRNA expression (RNA seq V2 RSEM): CCNB1 (log2)

Spearman: 0.89 (p = 1.20e-26)

Spearman: 0.86 (p = 1.40e-22)

Pearson: 0.88 (p = 3.60e-25)

Pearson: 0.81 (p = 2.79e-18)

(c)

(d)

CCNB1 vs. CDC20

CCNB1 vs. CDCA8

mRNA expression (RNA seq V2 RSEM): CDC20 (log2)

mRNA expression (RNA seq V2 RSEM): CDCA8 (log2)

:y = 1.41x 1 6.49

y= 113x + 3.66

R= = 0.72

R == 0.77

mRNA expression (RNA seq V2 RSEM): CCNB1 (log2)

Spearman: 0.85 (p =4.99e-22)

Spearman: 0.89 (p =7.57e-27)

Pearson: 0.85

Pearson: 0.88 (p = 2.43e-25)

(p =7.92e-22)

(e)

(f)

FIGURE 11: Functional and co-expression analysis of CCNB1. (a-b) Pathway enrichment analysis of CCNB1. (c-j) Co-expression analysis of CCNB1 and related genes.

CCNB1 vs. CDK2

CCNB1 vs. PLK1

mRNA expression (RNA seq V2 RSEM): CDK2 (log2)

11.5

mRNA expression (RNA seq V2 RSEM): CCNB2 (log2)

10.5

y = 1.42x -6.53

R= = 0.65

y = 0.45x + 4.93

=0.43

9.5

8.5

7.5

mRNA expression (RNA seq V2 RSEM): CCNB1 (log2)

Spearman: 0.65

Spearman: 0.86

(p = 3.84e-10)

(p =9.98e-23)

Pearson: 0.66

Pearson: 0.85

(p = 1.45e-10)

(p =1.00e-21)

(g)

(h)

CCNB1 vs. ESPL1

CCNB1 vs. FZR1

mRNA expression (RNA seq V2 RSEM): ESPL1 (log2)

mRNA expression (RNA seq V2 RSEM): FZR1 (log2)

1.06x +

11.5

Oy -0.1x +9.92

=: 0.05

10.5

9.5

mRNA expression (RNA seq V2 RSEM): CCNB1 (log2)

Spearman: 0.76 (p =3.10e-15)

Spearman: 0.24

Pearson: 0.73

(p = 0.0345)

(p = 6.32e-14)

Pearson: 0.22

(p = 0.0545)

(i)

(j)

from the GEO database. After analyzing R language, these results were cross-correlated with data from TCGA, and 28 up-regulated and 462 down-regulated DEGs were enrolled

for our study. Then, we carried out GO functional analysis and pathway analysis (KEGG, REACTOME, PANTHER, and BIOCYC) using WebGestalt and KOBAS websites to

FIGURE 12: Continued.

Pathway enrichment

-log10 (P value)

Progesterone-mediated oocyte

5.0

maturation

4.5

Pathway name

Oocyte meiosis

4.0

HTLC-I infection

3.5

Cell cycle

3.0

0.0000 0.0005 0.0010 0.0015 0.0020

P value

Count

2.00

2.75

2.25

3.00

2.50

(a)

NDC80 vs. CCNB1

NDC80 vs. AURKB

mRNA expression (RNA seq V2 RSEM): CCNB1 (log2)

mRNA expression (RNA seq V2 RSEM): AURKB (log2)

y = 0.57x + 5.79

.y = 1.07x 9/ 123.

0.81

-2

mRNA expression (RNA seq V2 RSEM): NDC80 (log2)

Spearman: 0.84

Spearman: 0.91

(p = 4.55e-21)

NDC80 mutated

(p = 4.53e-30)

NDC80 mutated

Pearson: 0.82

Neither mutated

Pearson: 0.90 (p = 5.31e-28)

Neither mutated

(p = 2.79e-19)

(b)

(c)

FIGURE 12: Functional and co-expression analysis of NDC80. (a) Pathway enrichment analysis of NDC80. (b-f) Co-expression analysis of NDC80 and related genes.

NDC80 vs. BUB1

NDC80 vs. SPC25

mRNA expression (RNA seq V2 RSEM): BUB1 (log2)

mRNA expression (RNA seq V2 RSEM): SPC25 (log2)

y = 1.16x+02.43

2.2

y = 0.87x + 1.47

-2

-4

-6

-2

-8

mRNA expression (RNA seq V2 RSEM): NDC80 (log2)

Spearman: 0.88

Spearman: 0.94

(p =7.61e-25)

NDC80 mutated

(p = 2.89e-35)

NDC80 mutated

Pearson: 0.89

Neither mutated

Pearson: 0.85

Neither mutated

(p = 1.00e-26)

(p =2.00e-22)

(d)

(e)

NDC80 vs. CENPE

mRNA expression (RNA seq V2 RSEM): CENPE (log2)

y -0.53x + 3.53

0.58

mRNA expression (RNA seq V2 RSEM): NDC80 (log2)

Spearman: 0.76

(p = 4.86e-15)

NDC80 mutated

Pearson: 0.76

CENPE mutated

(p = 1.41e-15)

Neither mutated

(f)

learn these candidates’ gene function and regulatory process. Moreover, PPI network analysis was used to search for the hub genes through String database, and 17 dominant genes

were considered. In addition, the cBioPortal database helped investigate the mutations in these genes. The GEPIA website was applied to assess the extent of differential expression,

overall survival (OS), and disease-free survival (DFS). After excluding genes unrelated to ACC’s prognosis, we repeated pathway analysis on the remaining genes and acquired two target genes by three different algorithms. Eventually, we demonstrated that CCNB1 and NDC80 were associated with ACC’s diagnosis and prognosis and could be considered vital biomarkers for future clinical use.

CCNB1, also known as Cyclin B1, is essential for con- trolling cell cycle during the G2/M (mitosis) transition [52]. Our results showed that the expression of CCNB1 was elevated in many cancers compared to normal cases, such as esophageal cancer, gastric cancer, colorectal cancer, liver cancer, and breast cancer [53-56]. CCNB1 was posi- tively correlated with the stage of ACC. As the degree of dis- ease increased, the expression of this gene also increased. This denoted that CCNB1 can distinguish the severity of this cancer. Ten genes (ESPL1, CDK2, CDK1, ANAPC4, FZR1, PLK1, CDC27, CDC20, CCNB2, and ANAPC10) refer to 4 pathways (P53 signaling pathway, cell cycle, progesterone- mediated oocyte maturation, and oocyte meiosis) connected with CCNB1 were filtered out by our results. CCNB2 can compensate for CCNB1 in oocyte meiosis [57] and works consistently in ACC. CCNB1 and CDK1 were co-expressed in ACC, and this action was also acknowledged in breast cancer susceptibility, progression, and survival of Chinese women [58]. Lohberger et al. proposed that CCNB1 and CDK1/2 are involved in the G2/M cell cycle checkpoint, pro- viding an inner relationship between CCNB1 and CDK fam- ily [59]. The combination of CCNB1 and CDC20 high expression could predict the poor prognosis of liver cancer [60], similar to what we got in ACC. In a word, CCNB1 was involved in the process of ACC disease progression and occupied the central position of several pathways, implying that it could become a potential gene for further study.

NDC80 is required for chromosome segregation and spindle checkpoint activity [61]. It could affect the growth of hepatocellular carcinoma [62] and promote proliferation and metastasis of colon cancer [62]. In our study, the expres- sion of NDC80 was much higher in ACC stage 4 than in stage 1-3 but had nothing to do with gender. NDC80 was mainly centralized in cell cycle pathways and had protein interaction with CASC5, SPC25, AURKB, SPC24, NUF2, BUB1, ZWINT, CENPE, BUB1B, and MAD2L1. We should pay attention to whether NDC80 and CCNB1 had a co- expression in ACC, prompting that the combined detection of these two genes can improve the diagnostic rate of ACC. NDC80 could also be a promising marker to identify ACC and estimate the prognosis of this cancer.

5. Conclusions

Based on a series of bioinformatics analyses, our study concluded that CCNB1 and NDC80 are particularly relevant for the high risk and poor prognosis of ACC in theory, sug- gesting that these two genes can be beneficial for proper diagnosis and treatment of this disease. However, more efforts should be invested in clinical experiments to learn

these genes’ biological functions and pathological evolution in ACC.

Abbreviations

ACC:	Adrenocortical carcinoma
TCGA:	The Cancer Genome Atlas
GEO:	Gene Expression Omnibus
GEPIA:	Gene expression profiling interactive analysis
C3AR1:	Complement C3a receptor 1
CCNB1:	Cyclin B1
CDC20:	Cell division cycle 20
CENPU:	Centromere protein U
FOXM1:	Forkhead box M1
KIF4A:	Kinesin family member 4A
KIF11:	Kinesin family member 11
KIF20A:	Kinesin family member 20A
MAD2L1:	Mitotic arrest deficient 2 like 1
NCAPG:	Non-SMC condensin I complex subunit G
NDC80:	NDC80 kinetochore complex component
NUF2:	NUF2 component of NDC80 kinetochore complex
PBK:	PDZ binding kinase
RACGAP1:	Rac GTPase activating protein 1
RRM2:	Ribonucleotide reductase regulatory subunit M2
TOP2A:	DNA topoisomerase II alpha
TPX2:	TPX2 microtubule nucleation factor
KICH:	Kidney chromophobe
KIRC:	Kidney renal clear cell carcinoma
KIRP:	Kidney renal papillary cell carcinoma
PAAD:	Pancreatic adenocarcinoma
BLCA:	Bladder urothelial carcinoma
ESPL1:	Extra spindle pole bodies like 1, separase
CDK2:	Cyclin-dependent kinase 2
CDK1:	Cyclin-dependent kinase 1
ANAPC4:	Anaphase promoting complex subunit 4
FZR1:	Fizzy and cell division cycle 20 related 1
PLK1:	Polo-like kinase 1
CDC27:	Cell division cycle 27
CCNB2:	Cyclin B2
ANAPC10:	Anaphase promoting complex subunit 10
SPC25:	SPC25 component of NDC80 kinetochore complex
AURKB:	Aurora kinase B
SPC24:	SPC24 component of NDC80 kinetochore complex
BUB1:	BUB1 mitotic checkpoint serine/threonine kinase
ZWINT:	ZW10 interacting kinetochore protein
CENPE:	Centromere protein E
BUB1B:	BUB1 mitotic checkpoint serine/threonine kinase B.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Authors’ Contributions

Guangzhen Wu and Yingkun Xu designed the research methods and analyzed the data. Xiunan Li and Jiayi Li par- ticipated in data collection. Leizuo Zhao, Zicheng Wang, and Peizhi Zhang drafted and revised the manuscript. All authors approved the version to be released and agreed to be responsible for all aspects of the work.

Acknowledgments

We thank Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) for providing publicly avail- able data. This project is supported by the Scientific Research Fund of Liaoning Provincial Education Depart- ment (No. LZ2020071), the Doctoral Start-up Foundation of Liaoning Province (No. 2021-BS-209), the Dalian Youth Science and Technology Star (No. 2021RQ010), and the Doctoral Research Innovation Project of the First Affiliated Hospital of Chongqing Medical University (No. CYYY- BSYJSCXXM-202213).

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Research Article Comprehensive Multiomics Analysis Reveals Potential Diagnostic and Prognostic Biomarkers in Adrenal Cortical Carcinoma

1. Introduction

2. Materials and Methods

3. Results

4. Discussion

5. Conclusions

Abbreviations

Data Availability

Conflicts of Interest

Authors’ Contributions

Acknowledgments

References

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