CDC20 as a new therapeutic target for treating ovarian cancer: an integrated bioinformatics analysis

DOI: https://doi.org/10.21203/rs.3.rs-192561/v1

Abstract

Background

There were 313959 cases of newly diagnosed ovarian cancer (OC) and 207252 new deaths for OC in 2020 and OC lacks effective treatment options. Therefore, identifying novel therapeutic targets is imminent. Here, we use an integrated bioinformatics analysis to key genes involved in ovarian cancer and reveal potential therapeutic targets.

Methods

GSE105437, GSE14407 and GSE18520 downloaded from Gene Expression Omnibus (GEO) were used to screen differentially expressed genes (DEGs). Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to predict the potential functions of the DEGs. Protein-protein interaction network (PPI) was drawn through STRING database and select CDC20 having the highest degrees of connectivity as the potential therapeutic target. Oncomine database and quantitative Real-time RT-PCR (RT-qPCR) of the ovarian tissues were used to validate the mRNA expression of CDC20. We use Gene Set Enrichment Analysis (GSEA) software to explore the potential biological function of CDC20 in OC.

Results

A total of 821 DEGs were obtained, including 497 upregulated genes and 324 downregulated genes. Functional and pathway enrichment analyses indicated the DEGs were mainly involved in DNA-binding transcription activator activity, tubulin binding, microtubule binding, cell cycle, Wnt signaling pathway, p53 signaling pathway, and metabolism changes. Oncomine database analysis and RT-qPCR showed that CDC20 is significantly upregulated in OC tissues. GSEA analysis showed that CDC20 may regulate OC via cell cycle, citrate and TCA cycle, Oxidative phosphorylation and ubiquitin mediated proteolysis pathways.

Conclusion

The results of the present study deduced that CDC20 is overexpressed in OC and may be a promising therapeutic target for the treatment of OC.

1. Background

According to the new cancer statistics, there were 313959 cases of newly diagnosed OC and 207252 new deaths for OC in 2020 [1]. There will be approximately 22530 cancer cases diagnosed and 13980 patients will die from OC due to lack of early diagnosed markers, diagnosis at advanced stage with or without distant metastases, drug resistance and easiness to relapse [26]. Therefore, identifying novel therapeutic targets to improve the prognosis of OC patients is imminent.

We use the bioinformatical studies on OC from GEO database to study and explore the detailed molecular mechanisms of OC progression and therapy and reliable and positive new biomarkers and specific targets of OC. In this study, GSE105437, GSE14407 and GSE18520 were chosen from GEO database. First, R was used to merge and normalize the expression data of the three datasets based on batch normalization for the next DEGs screening. Subsequently, GO was used for functional annotation evaluation, and KEGG was used for pathway enrichment evaluation. Then, the PPI network of string database was used to explore the relationship between DEGs, and the molecular interaction between DEGs and tumorigenesis was found. From the PPI network, CDC20 was chosen as one of the key genes. CDC20, an importance factor in cell cycle, control the chromosome segregation and mitotic progression through interactions between spindle assembly checkpoint (SAC) and anaphase-promoting complex or cyclosome (APC/C) [7]. The knockdown of CDC20 causes different endings, including the arrest of mitosis and subsequent cell death or mitotic slippage [8, 9]. CDC20 expression level was also searched on Oncomine database and verified in patients’ ovarian cancer tissue compared to the normal ovarian tissues. In conclusion, CDC20 might be a novel anti-cancer therapeutic target in OC.

2. Materials And Methods

2.1 Data sources

NCBI-GEO (https://www.ncbi.nlm.nih.gov/geo/) is a free public database of microarray/gene profile and we obtained the gene expression profile of GSE105437, GSE14407 and GSE18520 in ovarian cancer and normal ovarian tissues. The details of each microarray study are provided in Table 1.

2.2 Integration of microarray data and differential expression analysis

Heterogeneity and potential variables are commonly recognized as major sources of bias and variability. The samples of the datasets we recruited for our multiple data sets analysis were handled on different days, in different groups or by different people. Therefore, we first integrated all samples of three data sets to improve the number of samples and avoid generating less reliable results by batch normalization in the R computing environment using sva and limma package [10, 11]. Next, we performed gene differential analysis (|LogFC|>1, adjusted P value (FDR) < 0.05) by comparing tumor tissues with normal tissues using limma R package [12]. The integrated dysregulated gene lists were saved for subsequent analysis. In addition, the expression conditions of these DEGs are shown in the heatmap and volcano. Heatmap analysis of the resulting data matrix were performed with R language (version 4.0.2) and pheatmap package (1.0.12), which is available from https://cran.r-project.org/web/packages/pheatmap/.

2.3 Functional enrichment analysis of DEGs

GO [13], KEGG [14] pathway enrichment analyses were performed to predict the potential functions of the DEGs in R using the function of clusterProfiler (version 3.16.1). The top 10 of GO and KEGG pathways were then analyzed and presented in bubble plots. These bubble charts are drawn based on P value by using ggplot2r software package and statistical software R (version 4.0.2). We consider P < 0.05 to be statistically significant.

2.4 Protein-protein Interaction

The online database STRING (v11.0, http://www.string-db.org/) was used to visualize the protein-protein Interaction (PPI) between the statistically significant DEG-encoded proteins in the resultant dataset [15]. To avoid an inaccurate PPI network, we used a confidence interaction score ≥ 0.9 to obtain the significant PPIs. We downloaded the high-resolution network from the STRING database. And 30 hub genes with the highest interactions in the network were listed, and a bar plot was drawn to show that in R language (version 4.0.2).

2.5 Gene Set Enrichment Analysis

Gene Set Enrichment Analysis (GSEA) software (Version 4.0.3) (http://www.broad.mit.edu/gsea) was used to explore the potential biological function of CDC20 in OC [16]. GSEA (version 3.0) was run for the KEGG gene sets (c2.cp.kegg.v.7.2.symbols.gmt) [17]. The number of permutations is equal to 1,000 and the phenotype labels were CDC20-high and CDC20-low. FDR < 0.25 and NOM P < 0.05 was considered as statistical significance.

2.6 Oncomine database extraction

Oncomine database (http://www.oncomine.org, accession number: [email protected]) is currently the world’s largest oncogene-chip database and integrated data‐mining platform for the purpose of mining cancer gene information. To date, the database has collected 715 gene expression data sets and 86,733 pieces of cancer tissue and normal tissue sample data. The Oncomine database was applied for differential expression classification for common cancer types, and their respective normal tissues [17–19].

2.7 Ethical statement

This study was carried out in accordance with the standards of the Helsinki Declaration of the World Medical Association [20], and approved by the Ethics Committee of China Medical University. All clinical samples were collected from the Shengjing Hospital of China Medical University with informed consent from all patients .

2.8 Tissue collection

30 cases of primary OC tissue and 30 cases of normal ovarian tissue were used in this study. All samples were collected from the patients undergoing surgical excision at the Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University. No patient received radiotherapy, chemotherapy, or hormone therapy before surgery. The histopathological diagnosis obtained from the Pathology Department according to the criteria of the World Health Organization [21].

2.9 Total RNA extraction and real-time reverse transcription PCR

RNA isolation of ovarian tissue samples were conducted through TRIzol Reagent (Invitrogen, USA). The synthesis of complementary DNA (cDNA) was conducted using PrimeScriptTM RT reagent Kit with gDNA Eraser (Takara), through reverse transcription reaction. Quantitative polymerase chain reaction for CDC20 and GAPDH were conducted in a volume of 20μL using SYBR Premix Ex Taq (Takara) in the ABI 7500 Fast (Life Technologies, Carlsbad, CA, USA). GAPDH was selected as the internal reference gene. The primer sequences were as follows: CDC20 forward 5- AGCAGCAGATGAGACCCTGAGG-3; CDC20 reverse 5- CAGCGGATGCCTTGGTGGATG-3; GAPDH forward 5- CAGGAGGCATTGCTGATGAT-3; GAPDH reverse 5- GAAGGCTGGGGCTCATTT -3. The relative levels of CDC20 expression were evaluated by the 2-ΔΔCT method using GAPDH as the control.

2.10 Statistical analysis

Statistical analysis was conducted using Graphpad Prism 8. Unpaired t test was utilized for comparing continuous variables between two groups. P-value less than 0.05 was considered to be statistically significant.

3. Results

3.1 Integration of microarray data and identification of DEGs in ovarian cancer.

Three expression profiles (GSE105437, GSE14407 and GSE18520) were obtained from the GEO database. We chose the expression data of normal ovarian epithelial cells and OC epithelial cells. In order to increase the signal and reduce the false positive rate, these data sets are standardized and combined in batches to reduce the variability.

To get differentially expressed genes of three databases, limma package was made to identify DEGs and we set the threshold of (|LogFC|>1, adjusted P value (FDR)<0.05. Finally, we got 821 DEGs, including 497 upregulated genes and 324 downregulated genes. In addition, the volcano (Figure 1A) and heatmap plots (Figure 1B) were drawn to show the expression levels of these DEGs.

3.2 GO function and KEGG pathway enrichment for the DEGs.

To further explore the function of the DEGs, GO function and KEGG pathway enrichment analysis were applied in R using the function of clusterProfiler (version 3.16.1). The top 10 of GO (Figure 2A) and KEGG (Figure 2B) pathways were then analyzed and presented in bubble plots. The detailed results are presented in Table 2 and Table 3.

3.3 PPI network and the selection of CDC20.

According to the information in string database, PPI network of DEGs protein translated in OC was constructed. Confidence score was set more than 0.9 and the high-resolution network picture (Figure 3A) and the details of the network were filtered out. Then R language was used to count the interaction number of each protein in the network and a bar plot was drawn to show 30 genes that had the highest interaction in the network (Figure 3B). As the top 2 proteins, it was identified that many OC-related studies had already well studied CDK1 through literature mining. Therefore, we chose CDC20 as the focus of following analyses.

3.4 Analysis of CDC20 gene in Oncomine database and ovarian cancer tissue.

The mRNA expression of CDC20 in the OC tissue was evaluated through the Oncomine database (Table 4). The results indicated that contrasted with the OC tissues, the CDC20 expression level is significantly decreased in the normal ovarian tissues (P<0.01; Figure 4A-I). For future verification of these results, 30 OC tissues and 30 normal ovarian tissues were evaluated by RT-qPCR. in line with the above results, the expression of CDC20 mRNA in the OC tissues remarkably increases (P<0.001) compared to the normal ovarian tissues (Figure 4J).

3.5 The mechanisms of CDC20 expression in ovarian cancer.

The analysis of single-gene differential expression in biological process research is limited [22]. The expression data of three datasets were used for GSEA to predict the gene set and signal pathway related to CDC20, so as to effectively reveal the biological function of the datasets. CDC20 may function in cell cycle, citrate and TCA cycle, Oxidative phosphorylation and ubiquitin mediated proteolysis (Figure 5A-D).

4. Discussion And Conclusion

Although the treatment technology of ovarian cancer has made great progress, the total mortality of ovarian cancer is the seventh largest cause of death of gynecological malignant tumors [1, 4]. The main causes of OC death are lack of early detection methods, high metastatic tendency and chemotherapy resistance. Therefore, for better treatment of OC, novel specific therapeutic targets should be identified.

Recently, bioinformatics is developing rapidly and microarray and sequencing data is getting more and more popular, which provide a platform for exploring the general genetic changes of tumor, identifying DEGs, and clarifying the molecular mechanism of tumor diagnosis, treatment and prognosis [23]. Therefore, GSE105437, GSE14407 and GSE18520 were downloaded from the GEO databases, from which 821 DEGs were identified, and GO function and KEGG pathway enrichment analysis showed the DEGs were mainly enriched in some vital pathways and functions, whose abnormalities may cause tumor progression and drug resistance. Activations of gene transcription such as HIF-1, STAT-3, PAX3, c-MYB, TGF-β can promote cancer progression in aspects of immune responses, hematopoiesis, neurogenesis, angiogenesis, cell survival, glucose metabolism and invasion, which can be used as therapeutic targets in OC [24-27]. Tubulin is the important component of microtubules that are an important therapeutic target in tumor cells [28]. Paclitaxel as a tubulin inhibitor is the first-line drug for treating OC, but as many as 80% of patients will eventually relapse and become paclitaxel resistant, which may cause treatment failure and poor prognosis of ovarian cancer [29-32]. Therefore, to find new targets for treatment is imminent. Cell cycle is the basic process of cell division, which is closely related to the orderly expression of related genes, cyclin-dependent kinases (CDKs), cell cycle divisions (CDCs), the tumour suppressor p53 and so on, whose abnormalities contribute to carcinogenesis and tumor progression and drugs target for these can be used to treat various cancer [33, 34]. Wnt signaling pathway can be implicated in OC stemness, carcinogenesis of many OC subtypes by regulating cell growth and apoptosis, and attaches great importance to chemoresistance, which can be targeted for chemo-sensitization in OC [35, 36]. Chen, M.W., et al. reported that the Wnt signaling pathway regulates ovarian cancer cells growth, progression, and migration through interaction with STAT3 and miRNA-92 [37]. P450 are a series of metabolic enzymes that can regulate many processes such as anticancer drugs’ pharmacokinetics. Recent studies have shown that individual forms of P450 play a role in the resistance of anticancer drugs [38, 39]. Downie, D., et al. reported that P450 enzymes are overexpressed in OC and can be markers of prognosis [40]. p53 is a famous tumor suppressor gene, which can regulate the cell cycle and avoid the occurrence of cancer. It is jokingly called "the guardian of genome". Generally, p53 gene mutation happens in more than 50% of cancer patients [41]. Chen, Y.N., et al. reported that microRNA let-7d-5p-HMGA1-p53 signaling pathway rescues OC cell apoptosis and restores chemosensitivity in ovarian cancer [42]. Metabolic reprogramming is a marker of malignancy. There are a lot of metabolic heterogeneity between cancer cells and normal tissues, but almost no metabolic activity is limited in tumors. The metabolic phenotype and metabolic dependence keep changing during the development of cancer from precancerous tissue to local invasion and metastasis [43, 44]. These findings help us better understanding the possible mechanisms in OC development, progression, and therapy.

Furthermore, a PPI network was drawn based on the DEGs, from which 30 hub genes that had the highest interactions in the network were identified. By literature mining, it was identified that many OC-related studies had already well studied CDK1. Therefore, CDC20 was selected for further research as a key gene in OC. Previous studies have suggested that CDC20 could function as tumor oncogene [45-51]. However, there lacks works done to explore CDC20 expression level, protein level and related molecular mechanisms in OC. Initially, we used Oncomine database to verify the CDC20 mRNA expression levels between OC and normal ovary tissues. What’s more, we examined the levels of CDC20 mRNA in normal OC tissues and normal ovarian tissues from patients in our hospital. All results indicated CDC20 expression in normal ovarian tissues was lower than that in OC tissues. Next, we predict gene sets and signaling pathways associated with CDC20 using the expression data of three datasets in GSEA software. CDC20 may function in cell cycle, citrate and TCA cycle, oxidative phosphorylation and ubiquitin mediated proteolysis. As SAC target, and APC/C E3 ubiquitin ligase co-activator involving in metaphase and anaphase transition during mitosis, the significance of CDC20 in cell cycle regulation is obvious [7]. The tricarboxylic acid (TCA) cycle is the final metabolic pathway and also the center of carbohydrate, lipid and amino acid metabolism. Oxidative phosphorylation is accompanied by ATP production in biological oxidation, including two types of phosphorylation: metabolite linked phosphorylation and respiratory chain linked phosphorylation. TCA cycle and oxidative phosphorylation are two main process in cells to produce energy, and metabolic reprogramming causes tumorigenesis. From this aspect, controlling cancer energy metabolism becomes a potential treatment. Besides, APC/CCDC20 as an E3 ubiquitin ligase can also promote substrate ubiquitination and their subsequent degradation by the proteasome, which can regulate metabolic signaling pathways, transcription factors, and metabolic enzymes [52]. These show that CDC20 attaches great importance to OC and can be a specific target to treat OC.

In conclusion, we deduce that CDC20 may be a promising therapeutic target for the treatment of OC. However, continued work will be necessary to define whether CDC20 can produce a marked effect and the exact role and mechanism of it in OC Yet, despite the uncertainty of these mechanism, we believe that CDC20 is an appealing potential therapeutic target for OC patients.

5. Declarations

5.1 Ethics approval and consent to participate

This study was carried out in accordance with the standards of the Helsinki Declaration of the World Medical Association, and approved by the Ethics Committee of China Medical University. All clinical samples were collected from the Shengjing Hospital of China Medical University with informed consent from all patients.

5.2 Consent for publication

Not applicable.

5.3 Availability of data and materials

NCBI-GEO: https://www.ncbi.nlm.nih.gov/geo/; accession number: GSE105437, GSE14407 and GSE18520

Oncomine database: http://www.oncomine.org, accession number: [email protected]

STRING v11.0: http://www.string-db.org/

5.4 Competing interests

The authors declare that they have no competing interests.

5.5 Funding

This work was supported by National Natural Science Foundation of China (No.81872125) and Outstanding Scientific Fund of Shengjing Hospital (No. 201704).

5.6 Authors' contributions

Xiaocui Zhang downloaded the dataset, analyzed the data, performed the RT-qPCR, and was a major contributor in writing the manuscript. Fangfang Bi collected the ovarian tissues and reviewed the manuscript together with the corresponding author Qing Yang. All authors read and approved the final manuscript.

5.7 Acknowledgments

We appreciate the Gene Expression Omnibus and the Oncomine database for the open data and the software of R language and Gene Set Enrichment Analysis.

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7. Tables

Table 1 Details of ovarian cancer studies from Gene Expression Omnibus database

No. of GEO profile

Type

Source

Case

Control

Platform

Annotation platform

GSE105437

mRNA

Ovarian cancer epithelial cells

10

5

GPL570

Affymetrix Human Genome U133 Plus 2.0 Array

GSE14407

mRNA

serous ovarian cancer epithelia samples

12

12

GPL570

Affymetrix Human Genome U133 Plus 2.0 Array

GSE18520

mRNA

high-grade papillary serous ovarian adenocarcinomas

53

10

GPL570

Affymetrix Human Genome U133 Plus 2.0 Array

 


Table 2 GO analysis of DEGs associated with ovarian cancer

ID

Description

Count

qvalue

GO:0005539

glycosaminoglycan binding

48

6.34E-06

GO:0015631

tubulin binding

59

5.87E-05

GO:0008017

microtubule binding

44

0.000919232

GO:0008201

heparin binding

33

0.00175352

GO:0019838

growth factor binding

28

0.002782876

GO:1901681

sulfur compound binding

42

0.002854375

GO:0005201

extracellular matrix structural constituent

31

0.002854375

GO:0003777

microtubule motor activity

20

0.002854375

GO:0005540

hyaluronic acid binding

9

0.003121232

GO:0019894

kinesin binding

13

0.003206683

GO:0034236

protein kinase A catalytic subunit binding

7

0.003280854

GO:0005125

cytokine activity

36

0.011876726

GO:0008009

chemokine activity

13

0.01429931

GO:0003774

motor activity

25

0.016519809

GO:0001664

G protein-coupled receptor binding

42

0.020132285

GO:0005520

insulin-like growth factor binding

9

0.023187286

GO:0042379

chemokine receptor binding

15

0.023187286

GO:0001965

G-protein alpha-subunit binding

8

0.03345559

GO:0098641

cadherin binding involved in cell-cell adhesion

7

0.034780177

GO:0016538

cyclin-dependent protein serine/threonine kinase regulator activity

12

0.034780177

GO:0001228

DNA-binding transcription activator activity, RNA polymerase II-specific

58

0.034780177

GO:0004857

enzyme inhibitor activity

51

0.034780177

GO:0001216

DNA-binding transcription activator activity

58

0.034780177

GO:0043014

alpha-tubulin binding

10

0.03544527

GO:0048018

receptor ligand activity

62

0.04033949

GO:0015267

channel activity

61

0.040507336

GO:0008574

ATP-dependent microtubule motor activity, plus-end-directed

8

0.040507336

GO:0022803

passive transmembrane transporter activity

61

0.040933939

GO:0030546

signaling receptor activator activity

62

0.0441193

GO:0005109

frizzled binding

10

0.0441193

 


Table 3 KEGG pathway analysis of DEGs associated with ovarian cancer

ID

Description

Count

pvalue

hsa04110

Cell cycle

32

7.80E-09

hsa05222

Small cell lung cancer

17

0.001821

hsa04657

IL-17 signaling pathway

17

0.002316

hsa05218

Melanoma

14

0.002748

hsa04510

Focal adhesion

29

0.003501

hsa04060

Cytokine-cytokine receptor interaction

39

0.003893

hsa04061

Viral protein interaction with cytokine and cytokine receptor

17

0.004514

hsa05219

Bladder cancer

9

0.006739

hsa04512

ECM-receptor interaction

15

0.007217

hsa05226

Gastric cancer

22

0.007825

hsa00380

Tryptophan metabolism

9

0.007943

hsa05332

Graft-versus-host disease

9

0.007943

hsa04115

p53 signaling pathway

13

0.008293

hsa00340

Histidine metabolism

6

0.008693

hsa04914

Progesterone-mediated oocyte maturation

16

0.010363

hsa00360

Phenylalanine metabolism

5

0.011713

hsa05224

Breast cancer

21

0.01325

hsa04514

Cell adhesion molecules

21

0.015298

hsa04151

PI3K-Akt signaling pathway

42

0.017768

hsa00260

Glycine, serine and threonine metabolism

8

0.018146

hsa00270

Cysteine and methionine metabolism

9

0.024243

hsa04975

Fat digestion and absorption

8

0.027313

hsa04610

Complement and coagulation cascades

13

0.027706

hsa04350

TGF-beta signaling pathway

14

0.028147

hsa05412

Arrhythmogenic right ventricular cardiomyopathy

12

0.029506

hsa00350

Tyrosine metabolism

7

0.030422

hsa05166

Human T-cell leukemia virus 1 infection

27

0.033382

hsa04550

Signaling pathways regulating pluripotency of stem cells

19

0.035238

hsa05340

Primary immunodeficiency

7

0.039696

hsa05217

Basal cell carcinoma

10

0.03998

hsa04934

Cushing syndrome

20

0.040962

hsa04931

Insulin resistance

15

0.040992

hsa04218

Cellular senescence

20

0.043385

hsa05205

Proteoglycans in cancer

25

0.044182

hsa04933

AGE-RAGE signaling pathway in diabetic complications

14

0.044634

hsa04114

Oocyte meiosis

17

0.047729

hsa04658

Th1 and Th2 cell differentiation

13

0.048575

hsa04977

Vitamin digestion and absorption

5

0.049018

hsa04152

AMPK signaling pathway

16

0.049071

hsa05418

Fluid shear stress and atherosclerosis

18

0.049156

 


Table 4 Details of ovarian cancer studies from Oncomine database

        Name of Oncomine dataset

Ovarian Cancer Type

Csases

Controls

Rank of CDC20 among the DEGs

p-vaule

FDR

Anglesio Ovarian, Mol Cancer Res, 2008

Ovarian Carcinoma

44

30

180(top 1%)

4.27E-14

4.615

Bonome Ovarian, Cancer Res, 2008

Ovarian Carcinoma

185

10

1201(top 10%)

1.21E-7

4.045

Sieben Ovarian, J Clin Oncol, 2005

Ovarian Carcinoma

25

14

51(top 1%)

8/34E-8

3.549

TCGA Ovarian, No Associated Paper, 2013

Ovarian Serous Cystadenocarcinoma

586

8

652(top 6%)

2.44E-6

11.259

Tothill Ovarian, Clin Cancer Res, 2008

Ovarian Carcinoma

 

171

18

661(top 4%)

3.93E-11

4.184

Yoshihara Ovarian, Cancer Sci, 2009

Ovarian Serous Cystadenocarcinoma

43

10

51(top 1%)

7.68E-13

11.297

Welsh Ovarian, Proc Natl Acad Sci U S A, 2001

Ovarian Serous Surface Papillary Carcinoma

28

4

29(top 1%)

1.99E-12

71.552