Identification of hub genes and pathways in adrenocortical carcinoma by integrated bioinformatic analysis
Jinshuai Guo1 İD Yinzhong Gu 2
İD Xiaoyu Ma1 1
İD Lu Zhang1 1 Huimin Li1 D İD |
Zhongyi Yan1 1
İD Yali Han’ 1
İD Longxiang Xie 1
İD
Xiangqian Guo1 İD
1Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Basic Medical Sciences, Henan University, Kaifeng, China 2Education Bureau of Longgang District, Shenzhen, China
Correspondence
Longxiang Xie and Xiangqian Guo, Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Basic Medical Sciences, Henan University, Kaifeng 475004, China. Emails: xielongxiang123@126.com (LX); xqguo@henu.edu.cn (XG)
Funding information
Kaifeng Science and Technology Major Project, Grant/Award Number: 18ZD008; Program for Science and Technology Development in Henan Province, Grant/Award Number: 162102310391; 172102210187; China Postdoctoral Science Foundation, Grant/Award Number: 2017M62237; Yellow River Scholar Program, Grant/Award Number: H2016012; Projects for College Students in Henan University, Grant/Award Number: 201819002; Supporting grants of Henan University, Grant/Award Number: 2015YBZR048; B2015151; Key Project of Science and Technology Research of Henan Provincial Department of Education, Grant/Award Number: 20A180001; National Natural
Abstract
Adrenocortical carcinoma (ACC), a rare malignant neoplasm originating from adrenal cortical cells, has high malignancy and few treatments. Therefore, it is necessary to explore the molecular mechanism of tumorigenesis, screen and verify potential bio- markers, which will provide new clues for the treatment and diagnosis of ACC. In this paper, three gene expression profiles (GSE10927, GSE12368 and GSE90713) were downloaded from the Gene Expression Omnibus (GEO) database. Differentially ex- pressed genes (DEGs) were obtained using the Limma package. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were enriched by DAVID. Protein-protein interaction (PPI) network was evaluated by STRING database, and PPI network was constructed by Cytoscape. Finally, GEPIA was used to validate hub genes’ expression. Compared with normal adrenal tissues, 74 up-regulated DEGs and 126 down-regulated DEGs were found in ACC samples; GO analysis showed that up-regulated DEGs were enriched in organelle fission, nuclear division, spindle, et al, while down-regulated DEGs were enriched in angiogenesis, proteinaceous ex- tracellular matrix and growth factor activity; KEGG pathway analysis showed that up-regulated DEGs were significantly enriched in cell cycle, cellular senescence and progesterone-mediated oocyte maturation; Nine hub genes (CCNB1, CDK1, TOP2A, CCNA2, CDKN3, MAD2L1, RACGAP1, BUB1 and CCNB2) were identified by PPI net- work; ACC patients with high expression of 9 hub genes were all associated with worse overall survival (OS). These hub genes and pathways might be involved in the tumorigenesis, which will offer the opportunities to develop the new therapeutic tar- gets of ACC.
Jinshuai Guo and Yingzhong Gu are Co-first authors.
@ 2020 The Authors. Journal of Cellular and Molecular Medicine published by Foundation for Cellular and Molecular Medicine and John Wiley & Sons Ltd
Science Foundation of China, Grant/ Award Number: 81602362; Program for Scientific and Technological Research of Henan Education Department, Grant/ Award Number: 14B520022; Program for Innovative Talents of Science and Technology in Henan Province, Grant/ Award Number: 18HASTIT048; Henan Postdoctoral Foundation, Grant/Award Number: 001702052; Supporting grant of Bioinformatics Center of Henan University, Grant/Award Number: 2018YLJC01
KEYWORDS
adrenocortical carcinoma, Gene Expression Omnibus, GEPIA, overall survival, prognostic
1 INTRODUCTION |
Adrenal gland is an important endocrine organ of the human body and is also one of the most common organs with high tumour me- tastasis rate. According to the latest edition of pathological and genetic classification criteria of adrenal tumours published by World Health Organization (WHO), adrenal tumours can be di- vided into five categories: adrenocortical tumour, adrenal medul- lary tumour, extra-adrenal paraganglioma, secondary tumour and other adrenal tumour.1 Adrenocortical tumour mainly includes adrenocortical adenoma (ACA) and adrenocortical carcinoma (ACC).2 ACC is a rare malignant tumour originating from adre- nal cortical cells,3 and its incidence ranges from 0.7/10 00 000 to 2.0/10 00 000, which is under a high degree of malignancy, aggressiveness, high recurrence rate and poor prognosis. Most of the patients are found to have metastases and relapse easily after treatment, and the overall 5-year survival rate was <35%.4 Early and accurate diagnosis is particularly important for the treatment and prognosis of ACC.5 At present, surgical resection is the only feasible method to cure ACC, but it is difficult to con- trol its quality.6 Therefore, identifying new therapeutic targets or biomarkers for prognosis, diagnosis or prediction of ACC is urgently needed.
In recently years, many microarray profiling studies have been performed in ACC,7,8 and hundreds of differentially expressed genes (DEGs) have been obtained. However, the results are lim- ited or inconsistent due to molecular heterogeneity, and the re- sults are usually generated from a single cohort study. Until now, no reliable biomarkers have been used in ACC clinics. Hence, the bioinformatics methods integrating multi-cohorts analysed by gene microarray or RNAseq will be innovative and valuable for ACC research.
In this work, we downloaded three different Gene Expression Omnibus (GEO) datasets (GSE10927, GSE12368 and GSE90713) and screened differentially DEGs using the Limma package. Then, the PPI network in STRING database was constructed to screen the hub genes and pathways, and GEPIA database was used to verify hub genes and potential pathways. This study will offer the opportunities to develop the new therapeutic targets of ACC.
2 MATERIALS AND METHODS |
The flow diagram of this study was shown in Figure 1. The raw expression data were operated through a series of databases and software. This study has been proved by the Henan University insti- tutional committee.
GEO
Download of GSE10927 GSE12368 GSE90713
Limma
Identification of DEGs
DAVID
STRING
GO analysis
PPI network
KEGG
Cytoscape
Pathway analysis
Hub genes analysis
TCGA
GEPIA
Validation
2.1 Data collection
Gene expression profiles of GSE10927,9 GSE123681º and GSE9071311 were obtained from GEO database. The GSE10927 dataset includes 55 neoplastic samples and 10 non-neoplastic sam- ples (33 cases of ACC, 22 cases of ACA and 10 cases of normal ad- renal cortex). The GSE12368 dataset is composed of 28 neoplastic samples and 6 non-neoplastic samples (12 cases of ACC, 16 cases of ACA and 6 cases of normal adrenal cortex). The GSE90713 dataset includes 58 neoplastic samples and 5 non-neoplastic samples (58 cases of ACC and 5 cases of normal adrenal cortex).
2.2 Screening differentially expressed genes
DEGs between ACC samples and non-neoplastic samples were screened by using Limma package based on R language. DEGs were defined as representing differences with |log2FC| > 1, P < . 05.12
2.3 Gene ontology and KEGG pathway enrichment analysis of DEGs
Gene ontology (GO) analysis annotates genes and gene products with functions of molecular function (MF), biological process (BP) and cellular component (CC).13,14 Kyoto Encyclopedia of Genes and Genomes (KEGG) includes a series of genomics and enzymol- ogy methods and an online database of biochemical energy.15 The Database for Annotation, Visualization and Integrated Discovery (DAVID) (https://david.ncifcrf.gov/) is an online program providing a comprehensive set of functional annotation tools for investigators to understand biological meaning behind large list of genes.16 We performed GO terms and KEGG pathway analysis of DEGs by using DAVID database.
2.4 PPI network construction and hub module selection
Search Tool for the Retrieval of Interacting Genes (STRING) database (http://www.string-db.org/) was used to evaluate protein-protein in- teraction (PPI).17 In addition, the database was used to quantify the relationships among the DEGs. Then, we used Cytoscape software to construct PPI network.18 The genes with the highest node score and the strongest connectivity were selected. P < . 05 was consid- ered to have statistical significance.
2.5 Hub genes validation
GEPIA (http://gepia.cancer-pku.cn/) is a powerful interactive web server that can analyse the RNA sequencing expression data of 9736 tumours and 8587 normal samples from the TCGA and the GTEx
projects.19 GEPIA can be directly used for tumour/normal differential expression analysis according to cancer types, and the box plot will be shown to visualize the relationship. GEPIA was used to verify the hub genes and perform validation, P < . 05 showed statistical significance.19
TCGA-ACC RNA sequencing data with patient survival data were downloaded from the University of California, Santa Cruz (UCSC) Xena browser.20 Clinicopathological parameters of ACC patients with primary tumours, including age at diagnosis, gender, pathologic stage, living status, and overall survival (OS), were used for surviv- al-curve analysis.
2.6 Statistical analyses
Clinicopathologic parameter association analysis, and univariate and multivariate Cox regression analysis of 9 hub genes were performed with SPSS 22.0. Statistical significance was set at probability values of P < . 05.
3 RESULTS
|
3.1 Identification of differentially expressed genes
Using P < . 05 and |log2FC| > 1 as cut-off, we identified 1040 up- regulated and 1821 down-regulated genes in ACCs compared with normal tissues from GSE10927 dataset (Figure S1A), 2295 up-regulated and 3300 down-regulated genes from GSE12368 dataset (Figure S1B), 487 up-regulated and 1129 down-regulated genes from GSE90713 dataset (Figure S1C). By intersecting the DEGs across three datasets, a total of 200 consistently differen- tially expressed genes including 74 up-regulated and 126 down- regulated genes were identified to be significant in all the three above gene expression profiles (Figure 2A and B). The heat map of top 20 down-regulated genes and top 20 up-regulated genes’ expression was shown in Figure 2C.
3.2 | Gene ontology analysis of differentially expressed genes
Subsequently, GO analysis of up-regulated and down-regulated DEGs was carried out by using DAVID online analysis tool.16 In terms of biological process (BP), up-regulated DEGs were significantly en- riched organelle fission, nuclear division, mitotic nuclear division, chromosome segregation, regulation of cell cycle phase transition, regulation of mitotic cell cycle phase transition, nuclear chromo- some segregation and sister chromatid segregation (Figure 3A). Down-regulated DEGs were involved in angiogenesis, regulation of cell growth, fatty acid metabolic process, ageing, developmental maturation and protein kinase B signalling (Figure 3B). In terms of cellular component (CC), the up-regulated DEGs were significantly enriched in spindle, condensed chromosome, chromosomal region,
|
A
GSE10927
GSE12368
B
GSE10927
GSE12368
439
126
196
654
236
200
74
126
33
2
144
11
53
141
GSE90713
GSE90713
C
4.37
1.95
2.47
DEPDC1B
4.81
3.48
1.75
ASPM
4
4.32
3.97
2.97
1.89
DTL
2.50
1.79
BUB1
4.09
2.29
1.52
CENPK
2
3.64
3.20
1.50
TPX2
3.57
2.70
BIRC5
3.77
3.04
2.72
1.70
1.39
ANGPT2
0
3.57
2.52
PRC1
2.62
3.27
2.56
UBE2C
2.76
2.95
2.54
ENC1
-2
3.83
3.56
3.51
3.47
2.15
CCNB2
1.89
PBK
3.83
3.13
2.39
CDKN3
-4
3.61
3.24
2.29
ANLN
3.75
3.63
2.70
2.18
STC1
3.02
1.91
HMMR
-6
4.36
3.12
2.76
TOP2A
3.72
3.46
2.98
CDK1
3.51
3.65
2.90
KIAA0101
-6.03
-4.79
-4.10
ADH1B
-7.36
-4.20
-1.90
DAPL1
-6.34
-3.24
-1.70
C2orf40
-4.99
-1.31
-3.97
CYP11B2
-5.01
-2.31
-3.77
-2.62
MUM1L1
-3.17
-2.89
WFDC1
-3.48
-3.45
-3.52
INMT
-3.78
-3.62
-3.09
CXCL12
-3.64
-3.44
-3.12
SLC16A9
-3.71
-3.43
-2.93
KCNQ1
-3.93
-2.86
-3.25
C7
-3.16
-2.93
-3.75
STEAP4
-4.33
-4.06
-2.59
FMO2
-4.62
-4.36
-3.32
-2.66
ANGPTL1
-3.58
-2.48
OGN
-4.32
-3.57
-2.28
FBLN1
-4.97
-3.25
-3.33
NPY1R
-4.72
-2.86
-3.49
SLITRK4
-5.31
-3.81
-3.25
RSPO3
-4.81
-3.91
-3.33
PDGFRA
0
G
G
00
5
m
m
10
8
2
~
w
microtubule, chromosome and centromeric region (Figure 3C), while the down-regulated DEGs were significantly enriched in proteina- ceous extracellular matrix (Figure 3D). In terms of molecular func- tion (MF), up-regulated DEGs were significantly enriched in tubulin binding, microtubule binding, drug binding and cyclin-dependent protein kinase activity (Figure 3E), while the down-regulated DEGs were significantly enriched in growth factor activity, growth factor binding and hormone binding (Figure 3F).
3.3 | KEGG pathway enrichment analysis of differentially expressed genes
KEGG pathway analysis of all DEGs showed that most of up- regulated DEGs were enriched in cell cycle, cell senescence,
progesterone-mediated oocyte maturation, oocyte, p53 signalling pathway and folic acid resistance (Figure 4), while down-regulation of DEGs did not significantly enrich KEGG pathway.
3.4 | PPI network construction and hub gene selection
Through analysing STRING database17 and constructing PPI network by Cytoscape software,18 as shown in Figure 5, the PPI network of DEGs consists of 120 nodes and 1032 edges with the highest degree of 53, including 56 up-regulated genes and 64 down-regulated genes. It was considered that top nine DEGs with high degree of connectivity as the hub genes of ACC: CCNB1 (Cyclin B1), CDK1 (cyclin-dependent kinase 1), TOP2A
|
A
B
Organelle fission
Angiogenesis
Nuclear division
Regulation of cell growth
Mitotic nuclear division
Fatty acid metabolic process
Chromosome segregation
Aging
Regulation of cell cycle phase transition
Developmental maturation
Regulation of mitotic cell cycle phase transition
p.adjust
Protein kinase B signaling
p.adjust
Nuclear chromosome segregation
1e-08
Respiratory system development-
0.04
Sister chromatid segregation
Respiratory tube development-
5e-09
0.03
Cell cycle checkpoint-
Lung development-
Mitotic sister chromatid segregation-
Phosphatidylinositol 3-kinase signaling
0.02
Mitotic cell cycle checkpoint-
Regulation of phosphatidylinositol 3-kinase signaling
Regulation of nuclear division
Count
Positive regulation of phosphatidylinositol 3-kinase signaling
Count
Regulation of mitotic nuclear division
8
Response to CAMP
12
5.0
Positive regulation of mitotic cell cycle
Substrate adhesion-dependent cell spreading
16
7.5
Regulation of chromosome segregation
Positive regulation of fibroblast proliferation
Chromosome separation
20
10.0
Sulfur compound catabolic process
Regulation of sister chromatid segregation
Icosanoid biosynthetic process
Metaphase/anaphase transition of mitotic cell cycle
Prostaglandin biosynthetic process
Regulation of metaphase/anaphase transition of cell cycle
Export across plasma membrane
Regulation of mitotic metaphase/anaphase transition
Keratan sulfate catabolic process
0.10
0.1
.15
0.20
0.25
0.30
0.35
0.04
0.06
0.08
0.10
GeneRatio
GeneRatio
C
Spindle
D
Condensed chromosome
Chromosomal region
Microtubule
Chromosome, centromeric region
p.adjust
Spindle pole
Condensed chromosome, centromeric region
0.006
Count
Midbody
0.004
11
Kinetochore
0.002
Condensed chromosome kinetochore
p.adjust
Mitotic spindle
Proteinaceous extracellular matrix
Spindle microtubule
Count
Condensed nuclear chromosome
2.5
0.004897994
5.0
Condensed nuclear chromosome, centromeric region
7.5
Cell division site
10.0
Cyclin-dependent protein kinase holoenzyme complex
12.5
Spindle midzone
Condensed chromosome outer kinetochore
Condensed nuclear chromosome kinetochore
Contractile ring
0.05
0.10
0.15
0.06
0.08
0.10
0.12
0.14
GeneRatio
GeneRatio
E
Tubulin binding
F
Growth factor activity
Microtubule binding
Drug binding
Count
Growth factor binding
Count
2
3
3
Cyclin-dependent protein kinase activity
4
Hormone binding
4
5
5
Cyclin-dependent protein serine/threonine kinase activity
6
6
Monocarboxylic acid transmembrane transporter activity
Histone kinase activity
p.adjust
p.adjust
0.04
DNA binding, bending
0.03
Insulin-like growth factor binding
0.03
0.02
0.02
Translation repressor activity, nucleic acid binding
0.01
Peptide hormone binding
0.01
Folic acid binding
Sequence-specific mRNA binding
Fibronectin binding
0.03 0.04 0.05 0.06 0.07 0.08
0.0300.0350.0400.0450.0500.055 GeneRatio
GeneRatio
FIGURE 4 Significantly enriched pathway terms of up-regulated differentially expressed genes in ACC
Cell cycle
☒
Cellular senescence
☒
Count
Progesterone-mediated oocyte maturation
☒
2
☒ 3
Oocyte meiosis
☒ 4
☒
☒ 5
☒ 6
p53 signalling pathway
☒
p.adjust
Antifolate resistance
0.04
0.03
DNA replication
0.02
0.01
Folate biosynthesis
One carbon pool by folate
0.10
0.15
0.20
GeneRatio
A
B
YBRD
MUM1
5orf3
RIP13
STOV
SMC4
TPX2
TOIS
DHAIC 2
MND1
GMNN
SC40A
ASPM
OSL
ARV
PY IR
NFIA
HF 1
JBE2
CDK1
BXAS
TYMS
AA010
FANC
THIR
PLINI
GADD45
YBL
ASP
JSPI
R2F
TLR4
TGA
ASEH
2A
12AF2
R
D51A
1
EXCL1
STATS
KIF2
MGB
CCNA2
PBK
A
NGPT
SIRT
SLC16A
CONB2
CDC6
VISP
IDC80
FEMP?
DGF
GF
VDM
Cr
PRC 1
BUB
NEXN
CONB
AURKA
LAPHE
1AD2L
CDKN3
IMP
GGH
BLN
DORA
R
R
IBE2
HOF
DOFF
KIF11
FBP
ENPK
ASPM
CDCA
CAPG
HMMR
ZWINT
TEK
OHFR
ICAPG
THRE
CONB
GFBP
SERPING
ANLN
BRE
PRC1
CTGE
RN
MPO
SMCA
REL
AAIZ
GFUP
DLG2
RFC4
BLN
D
POC
B
CEP55
AXL
RSPO
OMD
ANLN
DTL
KIF11
R
CGAP1
NF 36
OGN
MOD
TPRE
CTH
BCC
AGLI
AB39
JBE20
GLUIL
BIRC
A
DHIA
OP2A
S CO2E
1
URKA
IDC80
CADS
MYLK
ALAD
CENP
DH1
ADX1
S
HICBP1
(topoisomerase lla), CCNA2 (CyclinA2), CDKN3 (cyclin-depend- ent kinase inhibitor 3), MAD2L1 (mitosis arrest-deficient 2 like 1), RACGAP1 (Rac GTPase activating protein 1), BUB1 (benzimida- zole 1 homolog beta) and CCNB2 (Cyclin B2). In terms of biologi- cal process, these hub genes are significantly enriched in mitotic spindle assembly checkpoint (Table S1). In terms of molecular function, these hub genes are significantly enriched in ATP bind- ing (Table S1). KEGG pathway enrichment analysis showed that these hub genes are associated with progesterone-mediated
oocyte maturation, cell cycle, oocyte meiosis, p53 signal- ling pathway and progesterone-mediated oocyte maturation (Table S1).
3.5 Evaluate the prognostic value of hub genes
TCGA-ACC dataset was used to evaluate the prognostic value of nine hub genes by GEPIA. All patients with high hub gene expression
A
Overall Survival
B
Overall Survival
C
Overall Survival
1.0
Low BUB1 TPM
1.0
High BUB1 TPM
Low CCNA2 TPM
1.0
Low CCNB1 TPM
Logrank P = 2.6e-06
High CCNA2 TPM
High CCNB1 TPM
HR(high) = 7.7
Logrank P = 3e-06
Logrank P = 1e-04
0.8
p(HR) = 5e-05
0.8
HR(high) = 7.5
p(HR) = 5 8e-05
0.8
HR(high) = 4.9
Per cent survival
n(high) = 38
Per cent survival
p(HR) = 0.00038
n(low) = 37
n(high) = 38
Per cent survival
n(high) = 38
0.6
0.6
n(low) = 38
0.6
n(low) = 38
0.4
0.4
0.4
0.2
0.2
0.2
0.0
0.0
0.0
0
50
100
150
0
50
100
150
0
50
100
150
Months
Months
Months
D
Overall Survival
E
Overall Survival
F
Overall Survival
1.0
Low CCNB2 TPM
1.0
High CCNB2 TPM
Low CDK1 TPM
1.0
Low CDKN3 TPM
Logrank P = 9.3e-06
High CDK1 TPM
High CDKN3 TPM
0.8
HR(high) = 6.9
Logrank P = 7e-08
(HR) = 0.0001
0.8
HR(high) = 11
Logrank P = 2.3e-06
p(HR) = 1.20-05
0.8
HR(high) = 7.6
Per cent survival
p(HR)= 4.9e-05
n(high) = 38
Per cent survival
n(high) = 38
Per cent survival
n(high) = 38
0.6
n(low) = 38
0.6
n(low) = 38
0.6
n(low) = 38
0.4
0.4
0.4
0.2
0.2
0.2
0.0
0.0
0.0
0
50
100
150
0
50
100
150
0
50
100
150
Months
Months
Months
G
Overall Survival
H
Overall Survival
Overall Survival
1.0
Low MAD2L 1 TPM
1.0
High MAD2L1 TPM
Low RACGAP1 TPM
1.0
High
Low TOP2A
Logrank P = . 00038
HR(high) = 4.4
Logrank P = 7.3e-06
High TOP2A TPM
0.8
p(HR) = 0.001
0.8
HR(high) = 7.1
Logrank P = . 00014
HR(high) = 4.7
Per cent survival
p (HR) = 9.8e-05
0.8
p(HR) = 0.00047
n(high) =38
n(high) = 38
0.6
n(low) =38
Per cent survival
n(high) = 38
Per cent survival
0.6
n(low) = 38
0.6
n(low) = 38
0.4
0.4
0.4
0.2
0.2
0.2
0.0
0.0
0.0
0
50
100
150
0
50
100
150
0
50
100
150
Months
Months
Months
were associated with worse OS (Figure 6). The additional univariate and multivariate Cox regression analysis showed that the hub gene BUB1 (budding uninhibited by benzimidazole 1) was an independent prognostic factor for ACC patients, and BUB1 was significantly as- sociated with living status and clinical stage in TCGA data (Table 1 and Table 2). The analysis results of the remaining genes were shown in Tables S2-S17. In addition, all nine hub genes were validated to be significantly up-regulated in ACCs as they were in above three GEO datasets (Figure 7).
|
4 DISCUSSION
In this study, by integrated three expression profiling datasets from GEO, we identified 200 commonly changed DEGs (74 up- regulated and 126 down-regulated) in ACCs. These DEGs were further analysed by GO analysis (molecular function, biological process and cellular component). In terms of biological process, the up-regulated DEGs were mainly enriched in organelle fission and nuclear division, which were typical malignant indicators of
| Parameters | BUB1 mRNA expression | ||||
|---|---|---|---|---|---|
| Group | Low(n = 38) | High(n = 39) | ×2 | P value | |
| Age (Mean ± SD) | 46.63 ± 15.756 | 46.59 ± 16.106 | |||
| Gender | Female | 24 | 24 | 0.021 | 1.000 |
| Male | 14 | 15 | |||
| Clinical stage | I/II | 31 | 15 | 14.877 | .000 |
| III/IV | 7 | 24 | |||
| Recurrence status | No | 29 | 11 | 2.363 | .188 |
| Yes | 7 | 7 | |||
| Null | 2 | 21 | |||
| Living status | Living | 34 | 16 | 19.841 | .000 |
| Dead | 4 | 23 | |||
| Parameters OS | Univariate analysis | Multivariate analysis | ||||||
|---|---|---|---|---|---|---|---|---|
| HR | 95% CI | P | HR | 95% CI | P | |||
| Age ≥60 vs < 60 | 1.549 | 0.677 | 3.548 | .300 | 0.494 | 0.207 | 1.180 | .112 |
| Gender Female vs Male | 0.986 | 0.451 | 2.154 | .971 | ||||
| Clinical stage I/II vs III/IV | 6.467 | 2.702 | 15.481 | .000 | 0.208 | 0.077 | 0.558 | .002 |
| BUB1 expression Low vs High | 9.024 | 3.094 | 26.320 | .000 | 5.907 | 1.920 | 18.176 | .002 |
histopathological examination. Down-regulated DEGs were sig- nificantly enriched in angiogenesis. For example, Plk1 is closely related to a series of mitotic events such as centrosome replica- tion, spindle formation, chromosome segregation and cytokinesis and is also related to chromosome stability.21 In addition, down- regulated DEGs were significantly enriched in proteinaceous ex- tracellular matrix. In terms of molecular function, up-regulated DEGs were significantly enriched in tubulin binding and microtu- bule binding. It had been reported that STMN1 regulated micro- tubule dynamics and participates in the malignant phenotype of cancer cells.22 Down-regulated DEGs were significantly enriched in growth factor activity and growth factor binding. Some studies have shown that the stimulation of growth factors such as insulin- like growth factors may promote tumour proliferation.23 These results can help us to further understand the role of DEGs in the development and progress of ACC. The additional KEGG path- way analysis showed that up-regulated DEGs were significantly enriched in cell cycle, cell senescence, progesterone-mediated oocyte maturation, oocyte, p53 signalling pathway and folic acid resistance, further confirmed the important roles of p53 signalling pathway in ACC.24
By DEGs PPI network analysis, the hub genes with highest degree of communication were identified, and they are CCNB1,
CDK1, TOP2A, CCNA2, CDKN3, MAD2L1, RACGAP1, BUB1 and CCNB2. CCNB1, a key molecule to initiate mitosis, was associated with tumorigenesis and development.25 CCNB2 can promote cell proliferation, migration and invasion in lung adenocarcinoma 26 and hepatocellular carcinoma.27 In addition, CCNB1 and CCNB2 promoted gastric cancer cell proliferation and tumour growth.28 CDK1 and TOP2A played an important role in the regulation of cell cycle and regulated the proliferation of tumours.29,30 Glover et al found that CDK1 was up-regulated in ACC tissues com- pared with normal tissues.31 TOP2A was a potential biomarker for the progression and prognosis of various tumours.32 CCNA2 was found to promote the proliferation of breast cancer.33 Xing et al found that the expression of CDKN3 was generally increased in hepatocellular carcinoma tissues and was positively correlated with the pathological stage and differentiation of the tumours.34 MAD2L1 and BUB1 were important components of mitotic check- point complex proteins. High expression of these two genes was related to the poor disease-free survival of invasive tumours.35 RACGAP1 was found to be highly expressed in colorectal cancer36 and breast cancer. 37
Finally, we evaluated the prognostic value of 9 hub genes by using GEPIA database and found ACC patients with high expression of 9 hub genes (CCNB1, CDK1, TOP2A, CCNA2, CDKN3, MAD2L1,
WILEY
A
B
C
5
8
BUB1
*
1
CCNA2
*
CCNB1
*
4
6
6
5
0
+
4
2
0
2
2
-
-
0
0
0
ACC (num(T)=77; num(N)=128)
ACC (num(T)=77; num(N)=128)
ACC (num(T)=77; num(N)=128)
D
E
F
CCNB2
CDK1
CDKN3
6
6
6
5
5
5
4
+
4
0
3
0
~
2
~
-
-
1
0
0
0
ACC (num(T)=77; num(N)=128)
ACC (num(T)=77; num(N)=128)
ACC (num(T)=77; num(N)=128)
G
H
I
7
MAD2L1 -*
6
RACGAPI
TOP2A
*
6
5
6
5
+
+
4
3
0
2
2
2
1
1
:
₹
0
0
0
ACC
ACC (num(T)=77; num(N)=128)
ACC (num(T)=77; num(N)=128)
(num(T)=77; num(N)=128)
RACGAP1, BUB1 and CCNB2) were significantly associated with worse OS.
In conclusion, using multiple cohort profiling datasets and inte- grated bioinformatics analysis, we identified hub genes and potential pathways that may be involved in the progress of ACC.
ETHICS APPROVAL AND CONSENT TO PARTICIPATE
This study has been proved by the Henan University institutional committee.
ACKNOWLEDGEMENTS
This study was supported by Key Project of Science and Technology Research of Henan Provincial Department of Education (No.20A180001), National Natural Science Foundation of China (No.81602362), Supporting grants of Henan University (No.2015YBZR048; No.B2015151), Yellow River Scholar Program (No.H2016012) and Program for Innovative Talents of Science and Technology in Henan Province (No. 18HASTIT048), Projects for College Students in Henan University (No. 201819002), China Postdoctoral Science Foundation (No.2017M62237) and Henan Postdoctoral Foundation (No. 001702052), Program for Science and Technology Development in Henan Province (No.162102310391, No.172102210187), Program for Scientific and Technological Research of Henan Education Department (No.14B520022), Kaifeng Science and Technology Major Project (18ZD008) and Supporting grant of Bioinformatics Center of Henan University (No.2018YLJC01).
CONFLICT OF INTEREST
The authors declare that there is no conflict of interests. No animal or human studies were carried out by the authors for this article.
AUTHOR CONTRIBUTIONS
XG and LX involved in study concept and design; LX, JG, YG and XG involved in acquisition of data; JG, YG, XM, LZ, HL, ZY and YH in- volved in analysis and interpretation of data. LX, JG and XG drafted the manuscript; LX, JG and XG involved in critical revision of the manuscript for intellectual content.
DATA AVAILABILITY STATEMENT
All data generated or analysed during this study are included in this article.
ORCID
Jinshuai Guo İD https://orcid.org/0000-0002-2475-7847
Yinzhong Gu ID https://orcid.org/0000-0002-9335-0055
Xiaoyu Ma ID https://orcid.org/0000-0002-8279-6035
Lu Zhang ID https://orcid.org/0000-0001-8219-466X
Huimin Li İD https://orcid.org/0000-0001-8751-7501
Zhongyi Yan İD https://orcid.org/0000-0002-9835-5875
Yali Han İD https://orcid.org/0000-0002-5492-8117
Longxiang Xie İD https://orcid.org/0000-0001-6825-9690 Xiangqian Guo https://orcid.org/0000-0001-6645-5786 İD
REFERENCES
1. Delellis RA. Pathology and genetics of tumours of endocrine or- gans. 2004.
2. NIH state-of-the-science statement on management of the clin- ically inapparent adrenal mass (“incidentaloma”). NIH Consensus State Sci Statements. 2002;19:1.
3. Nakamura Y, Yamazaki Y, Felizola S, et al. Adrenocortical carcinoma: review of the pathologic features, production of adrenal steroids, and molecular pathogenesis. Endocrinol Metabo Clin North Am. 2015;44:399-410.
4. Bruno A, Martin F. Clinical review: adrenocortical carcinoma: clini- cal update. J Clin Endocrinol Metab. 2006;91:2027-2037.
5. Guillaume A, Eric L, Martin F, et al. Integrated genomic characteri- zation of adrenocortical carcinoma. Nat Genet. 2014;46:607-612.
6. Tobias E, Kim AC, Aaron S, et al. Adrenocortical carcinoma. Endocr Rev. 2014;35:282-326.
7. Gara SK, Lack J, Zhang L, Harris E, Cam M, Kebebew E. Metastatic adrenocortical carcinoma displays higher mutation rate and tumor heterogeneity than primary tumors. Nat Commun. 2018;9:4172.
8. Xie L, Wang Q, Nan F, et al. OSacc: Gene Expression-Based Survival Analysis Web Tool For Adrenocortical Carcinoma. Cancer Manag Res. 2019;11:9145.
9. Giordano TJ, Kuick R, Else T, et al. Molecular classification and prog- nostication of adrenocortical tumors by transcriptome profiling. Clin Cancer Res. 2009;15(2):668-676.
10. Soon PSH, Gill AJ, Benn DE, et al. Microarray gene expression and immunohistochemistry analyses of adrenocortical tumors identify IGF2 and Ki-67 as useful in differentiating carcinomas from adeno- mas. Endocr Relat Cancer. 2009;16(2):573-583.
11. Weiss ID, Huff LM, Evbuomwan MO, et al. Correction: Screening of cancer tissue arrays identifies CXCR4 on adrenocortical carcinoma: correlates with expression and quantification on metastases using (64)Cu-plerixafor PET. Oncotarget. 2018;8:73387-73406.
12. Ward A, Balwierz A, Zhang JD, et al. Re-expression of microRNA-375 reverses both tamoxifen resistance and accompanying EMT-like properties in breast cancer. Oncogene. 2013;32:1173-1182.
13. Consortium GO. The gene ontology (GO) project in 2006. Nucleic Acids Res. 2006;34:322-326.
14. Han YJ, Wang XH, Chen WC, et al. Differential expression of ca- rotenoid-related genes determines diversified carotenoid color- ation in flower petal of Osmanthus fragrans. Tree Genet Genomes. 2014;10:329-338.
15. Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, Kanehisa M. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 2000;27:29-34.
16. Dennis G, Sherman BT, Hosack DA, et al. DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol. 2003;4:P3.
17. Damian S, Andrea F, Stefan W, et al. STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 2015;43:D447.
18. Shannon P, Markiel A, Ozier O, et al. Cytoscape: a software environ- ment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498.
19. Tang Z, Li C, Kang B, Gao G, Li C, Zhang Z. GEPIA: a web server for cancer and normal gene expression profiling and interactive analy- ses. Nucleic Acids Res. 2017;45:W98-W102.
20. Goldman M, Craft B, Zhu J, Haussler D. The UCSC Xena system for cancer genomics data visualization and interpretation. AACR. 2017;77:2584.
21. Klaus S, Axel U. Targeting polo-like kinase 1 for cancer therapy. Nat Rev Cancer. 2006;6:321-330.
22. JoãO Agostinho MN, Saad STO, Fabiola T. Stathmin 1 in normal and malignant hematopoiesis. BMB Rep. 2014;47:660-665.
23. Martino MCD, Koetsveld PMV, Pivonello R, Hofland LJ. Role of the mTOR Pathway in normal and tumoral adrenal cells. Neuroendocrinology. 2010;92:28-34.
24. Faillot S, Assie G. ENDOCRINE TUMOURS: The genomics of adre- nocortical tumors. Eur J Endocrinol. 2016;174(6):R249-R265.
25. Ding K, Li W, Zou Z, Zou X, Wang C. CCNB1 is a prognostic bio- marker for ER+ breast cancer. Med Hypotheses. 2014;83:359-364.
26. Stav D, Bar I, Sandbank J. Usefulness of CDK5RAP3, CCNB2, and RAGE genes for the diagnosis of lung adenocarcinoma. Int J Biol Markers. 2007;22:108-113.
27. Gao CL, Wang GW, Yang GQ, Yang H, Zhuang L. Karyopherin sub- unit-a 2 expression accelerates cell cycle progression by upregu- lating CCNB2 and CDK1 in hepatocellular carcinoma. Oncol Lett. 2017;15:2815-2820.
28. Shi Q, Wang W, Jia Z, Chen P, Ma K, Zhou C. ISL1, a novel reg- ulator of CCNB1, CCNB2 and c-MYC genes, promotes gastric cancer cell proliferation and tumor growth. Oncotarget. 2016;7: 36489-36500.
29. Xi Q, Huang M, Wang Y, et al. The expression of CDK1 is associated with proliferation and can be a prognostic factor in epithelial ovar- ian cancer. Tumour Biol. 2015;36:4939-4948.
30. Kang X, Song C, Du X. PTEN stabilizes TOP2A and regulates the DNA decatenation. Sci Rep. 2015;5:17873.
31. Glover AR, Ting ZJ, Gill AJ, et al. microRNA-7 as a tumor suppres- sor and novel therapeutic for adrenocortical carcinoma. Oncotarget. 2015;6:36675-36688.
32. Lee YE, He HL, Lee SW, et al. AMACR overexpression as a poor prognostic factor in patients with nasopharyngeal carcinoma. Tumour Biol. 2015;35:7983-7991.
33. Tian G, Yong H, Ling Y, Sheng A, Ziyu L, Jiafu J. CCNA2 is a prognos- tic biomarker for ER+ breast cancer and tamoxifen resistance. PLoS ONE. 2014;9:e91771.
34. Xing C, Xie H, Lin Z, et al. Cyclin-dependent kinase inhibitor 3 is overexpressed in hepatocellular carcinoma and promotes tumor cell proliferation. Biochem Biophys Res Commun. 2012;420:29-35.
35. Zhanwei W, Dionyssios K, Yi S, et al. Biological and clinical sig- nificance of MAD2L1 and BUB1, genes frequently appearing in expression signatures for breast cancer prognosis. PLoS ONE. 2015;10:e0136246.
36. Hiroki I, Yuji T, Susumu S, et al. RacGAP1 expression, increasing tumor malignant potential, as a predictive biomarker for lymph node metastasis and poor prognosis in colorectal cancer. Carcinogenesis. 2015;36:346-354.
37. Pliarchopoulou K, Kalogeras KT, Kronenwett R, et al. Prognostic significance of RACGAP1 mRNA expression in high-risk early breast cancer: a study in primary tumors of breast cancer patients partici- pating in a randomized Hellenic Cooperative Oncology Group trial. Cancer Chemother Pharmacol. 2013;71:245-255.
SUPPORTING INFORMATION
Additional supporting information may be found online in the Supporting Information section.
How to cite this article: Guo J, Gu Y, Ma X, et al. Identification of hub genes and pathways in adrenocortical carcinoma by integrated bioinformatic analysis. J Cell Mol Med. 2020;24:4428-4438. https://doi.org/10.1111/jcmm.15102