medicina

Article Bioinformatic Analysis for Influential Core Identification and Prognostic Significance in Advanced Serous Ovarian Carcinoma

Changho Song 1, Kyoung-Bo Kim 2 , Jae-Ho Lee 3 and Shin Kim 4,5,*

1 Department of Obstetrics and Gynecology, Keimyung University, 1095 Dalgubeol-daero, Dalseo-gu, Daegu 42601, Korea; [email protected] 2 Department of Laboratory Medicine, Keimyung University, 1095 Dalgubeol-daero, Dalseo-gu, Daegu 42601, Korea; [email protected] 3 Department of Anatomy, School of Medicine, Keimyung University, 1095 Dalgubeol-daero, Dalseo-gu, Daegu 42601, Korea; [email protected] 4 Department of Immunology, School of Medicine, Keimyung University, 1095 Dalgubeol-daero, Dalseo-gu, Daegu 42601, Korea 5 Institute of Medical Science & Institute of Cancer Research, Keimyung University, 1095 Dalguceol-daero, Dalseo-gu, Daegu 42601, Korea * Correspondence: [email protected]; Tel.: +82-53-258-7359

Abstract: Background and objectives: Ovarian cancer is one of the leading causes of death among women worldwide. Most newly diagnosed ovarian cancer patients are diagnosed in advanced stages of the disease. Despite various treatments, most patients with advanced stage ovarian  cancer, including serous ovarian cancer (the most common subtype of ovarian cancer), experience  recurrence, which is associated with extremely poor prognoses. In the present study, we aimed to Citation: Song, C.; Kim, K.-B.; Lee, identify core involved in ovarian cancer and their associated molecular mechanisms, as well J.-H.; Kim, S. Bioinformatic Analysis as to investigate related clinicopathological implications in ovarian cancer. Materials and methods: for Influential Core Gene Three gene expression cohorts (GSE14407, GSE36668, and GSE38666) were obtained from the Gene Identification and Prognostic Expression Omnibus databases to explore potential therapeutic biomarkers for ovarian cancer. Nine Significance in Advanced Serous up-regulated and six down-regulated genes were screened. Three publicly available gene expression Ovarian Carcinoma. Medicina 2021, datasets (GSE14407, GSE36668, and GSE38666) were analyzed. Results: A total of 14 differently 57, 933. https://doi.org/ 10.3390/medicina57090933 expressed genes (DEGs) were identified, among which nine genes were upregulated (BIRC5, CDCA3, CENPF, KIF4A, NCAPG, RRM2, UBE2C, VEGFA, and NR2F6) and were found to be significantly Academic Editor: Lorena Losi enriched in cell cycle regulation by analysis. Further –protein interaction network analysis revealed seven hub genes among these DEGs. Moreover, Kaplan–Meier survival Received: 19 July 2021 analysis showed that a higher expression of CDCA3 and UBE2C was associated with poor overall Accepted: 31 August 2021 patient survival regardless of tumor stage and a higher tumor histologic grade. Conclusion: Altogether, Published: 4 September 2021 our study suggests that CDCA3 and UBE2C may be valuable biomarkers for predicting the outcome of patients with advanced serous ovarian cancer. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in Keywords: serous ovarian carcinoma; prognostic biomarker; CDCA3; UBE2C published maps and institutional affil- iations.

1. Introduction Ovarian cancer is the eighth cause of cancerous incidence and death in women world- Copyright: © 2021 by the authors. wide, accounting for 3.4% of all of cancer in females and for 4.7% of female cancer deaths Licensee MDPI, Basel, Switzerland. due to a low survival rate [1]. Most ovarian cancers are asymptomatic, which contributes This article is an open access article to approximately 70% of cases being diagnosed at an already advanced stage that, in turn, distributed under the terms and will lead to a poor treatment outcome [2,3]. Recent advances in treatment strategies have conditions of the Creative Commons improved the outcome of patients with ovarian cancer; nonetheless, serous ovarian cancer, Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ which is the most common subtype of ovarian cancer, still has a poor prognosis [4,5]. The 4.0/). remarkable development of bioinformatics has recently enabled the scientific community

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to research improving treatment outcome and survival of ovarian cancer patients through identifying potential therapeutic targets and prognostic markers [6–8]. However, there are no established genetic biomarkers other than the BRCA1/2 mutation to predict the prognosis of patients with ovarian cancer [9,10]. This highlights the need for a better under- standing of the underlying features of ovarian cancer to pave the way for the identification of specific therapeutic targets and prognostic markers, as well as for the development of enhanced treatment strategies. Recently, very powerful publicly available gene expression databases such as Gene Expression Omnibus (GEO) [11] and The Cancer Genome Atlas (TCGA) cohorts [12] have been widely used. In the present study, we investigated the dysregulated genes and their prognostic significance in serous ovarian carcinoma using GEO datasets and a TCGA Ovarian Cancer (TCGA OV) cohort. First, we identified differentially expressed genes (DEGs) from three GEO datasets by applying the GEO2R online tool and Venn diagram software. Second, Gene Ontology (GO) functional enrichment analysis was conducted with the Database for Annotation, Visualization, and Integrated Discovery (DAVID), including biological process (BP), cellular component (CC), and molecular function (MF), using the identified DEGs. Third, a protein–protein interaction (PPI) network of DEGs was constructed to identify important core genes related to serous ovarian cancer by the Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape. Finally, Kaplan–Meier analysis of the selected core genes was performed to evaluate the prognostic potential of the DEGs (p < 0.05). Taken together, among the 15 dysregulated genes, 9 up-regulated DEGs (BIRC5, CDCA3, CENPF, KIF4A, NCAPG, RRM2, UBE2C, VEGFA, and NR2F6) were significantly enriched in cell cycle regulation. Moreover, higher expression of CDCA3 and UBE2C was found to be associated with poor overall survival of patients in all stages and a higher histologic grade. Thus, this bioinformatic analysis using various cohorts provides useful information of prognostic biomarkers, which could be effective targets for patients with serous ovarian cancer.

2. Materials and Methods 2.1. Microarray Data Source and Cluster Analysis The 3 ovarian cancer-gene expression microarray datasets used in this study were obtained from the publicly available GEO database (National Institutes of Health, Bethesda, MD, USA; http://www.ncbi.nlm.nih.gov/geo/, date last accessed on 4 January 2021): GSE14407 [13], GSE36668 [14], and GSE38666 [15] (Table1). The datasets were generated using the GPL570 platform (GeneChip U133 Plus 2.0 Array; Affymetrix, Santa Clara, CA, USA). GSE14407 comprised 12 normal ovarian surface epithelial sam- ples and 12 laser capture microdissected serous ovarian cancer epithelial samples, and GSE36668 comprised four surface epithelium scrapings from normal ovary samples, four serous ovarian borderline tumor samples, and four serous ovarian carcinoma samples, of which the normal and carcinoma of ovarian samples were herein analyzed. Furthermore, GSE38666 dataset comprised 45 samples consisting of eight normal stromal samples and eight matched normal ovarian surface epithelial samples from 12 individuals, along with seven cancer stromal samples and seven laser capture microdissected matched cancer epithelia from 18 ovarian cancer patients. Among these samples, data from 12 normal ovarian surface epithelial samples and 18 cancer epithelia from ovarian cancer patients were used for analysis. Cluster analysis was performed using Cluster 3.0 software [16] to classify the samples into statistically similar groups, and the resulting heatmaps were visualized using TreeView (version 1.6) [17]. The expression levels in heatmaps were log2 transformed, median-centered, and scaled for visualization. Medicina 2021, 57, 933 3 of 15

Table 1. Basic information of microarray data from NCBI GEO database.

Platform GEO Dataset Samples Reference GPL570 GSE14407 12 Normal, 12 Cancer Bowen et al. [13] GPL570 GSE36668 4 Normal, 4 Cancer Elgaaen et al. [14] GPL570 GSE38666 12 Normal, 18 Cancer Lili et al. [15] NCBI: The National Center for Biotechnology Information; GEO: Gene Expression Omnibus.

2.2. Screening for DEGs DEGs between normal ovarian surface epithelia and ovarian cancer epithelia were screened using the online analysis tool GEO2R (http://www.ncbi.nlm.nih.gov/geo/geo2 r/, date last accessed on 25 January 2021) based on an adjusted p-value of less than 0.05. The top 250 significantly dysregulated genes were screened from each dataset regardless of positive or negative value of log2 fold change (FC) in the order of the lowest to the highest adjusted p-values. Common genes among the screened DEGs from the three GEO datasets were identified using Whitehead BaRC public tools (http://barc.wi.mit. edu/tools/, date last accessed on 28 January 2021). Expressions of the DEGs between normal and tumor tissue were obtained from TNMplot.com (http://tnmplot.com, date last accessed on 15 February 2021) [18], which contains microarray data from GEO database and RNA-sequencing data from TCGA, the Therapeutically Applicable Research to Generate Effective Treatments (TARGET), and the Genotype-Tissue Expression (GTEx) repositories. In the present study, RNA-seq data from TCGA and GTEx were used to compare the expression of the identified DEGs between ovarian serous cystadenocarcinoma patients and normal samples from non-cancerous patients.

2.3. GO Analysis of DEGs The biological implications of the DEGs were analyzed with the online software The Database for Annotation, Visualization, and Integrated Discovery (DAVID) v6.8 ( http://david.ncifcrf.gov/, date last accessed on 22 February 2021) [19]. Analyzed GO terms were subcategorized into biological process (BP), cellular component (CC), and molecular function (MF). p < 0.05 was considered statistically significant.

2.4. PPI Network Analysis To analyze and visualize the functional PPI network of the identified DEGs, Cytoscape software (version 3.8.2) and Search Tool for the Retrieval of Interacting Genes (STRING, http://string-db.org, date last accessed on 8 March 2021) were used (maximum number of interactors = 0, and confidence score ≥ 0.9). The Cytoscape plugin Molecular Complex Detection (MCODE) was used to identify clustered modules within the PPI network (degree cutoff = 2, node score cutoff = 0.2, k-score = 2 and max. depth = 100).

2.5. DEG Survival Analysis The Kaplan–Meier plotter (KM plotter, http://kmplot.com/, date last accessed on 22 March 2021) database contains gene expression and survival data from 1656 ovarian cancer patients from TCGA and GEO database. The dataset from GEO database includes the following: GSE3149, GSE9891, GSE14764, GSE15622, GSE18520, GSE19829, GSE23554, GSE26193, GSE26712, GSE27651, GSE30161, GSE51373, GSE63885, and GSE65986. In this study, overall survival data of 1207 ovarian cancer patients with serous histologic type were analyzed. The patients were grouped into higher and lower expression groups by dividing them at a cut-off value for the median expression of each DEG to give an “above-median to a higher expression” group and a “below-median to a lower expression” group. The association between the mRNA expression level of each DEG and the OS was analyzed using the Kaplan–Meier plotter. The hazard ratio (HR), 95% confidence intervals (CI), and the log-rank p-values were obtained from the website. A p-value < 0.05 was considered statistically significant. Medicina 2021, 57, x FOR PEER REVIEW 4 of 15

(CI), and the log-rank p-values were obtained from the website. A p-value < 0.05 was con-

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2.6. Data Source for Analysis of Association with Clinicopathological Parameters Prior to the analysis of association with clinicopathological features, the patients were 2.6.grouped Data Sourceinto higher for Analysis and lower of Association expression with grou Clinicopathologicalps by dividing them Parameters at a cut-off value for the medianPrior to expression the analysis of of each association DEG to with give clinicopathological an “above-median features, to a higher the patients expression” were groupedgroup and into a “below-median higher and lower to expressiona lower expres groupssion” by group. dividing the themRNA- atseq a cut-offgene expression value for thedataset median (dataset expression ID: TCGA.OV.sampleMap/HiseqV2) of each DEG to give an “above-median and the clinicopathological to a higher expression” param- groupeters (dataset and a “below-median ID: TCGA.OV.sampleMap/OV_clinicalMatrix) to a lower expression” group.the for RNA-seq the TCGA gene OV expres- were siondownloaded dataset (datasetfrom the ID:UCSC TCGA.OV.sampleMap/HiseqV2) Xena Browser (https://xena.ucsc.edu/, and the date clinicopathological last accessed on parameters29 March 2021). (dataset Patients ID: TCGA.OV.sampleMap/OV_clinicalMatrix) without sufficient survival and gene expression for the TCGA data OVwere were ex- downloadedcluded. The evaluated from the UCSCTCGA XenaOV RNA-seq Browser data (https://xena.ucsc.edu/set comprised 308 samples,, date including last accessed 304 onprimary 29 March tumor 2021). tissue Patients and 4 normal without solid sufficient tissue samples. survival Additionally, and gene expression the microarray data were da- excluded.taset (dataset The ID: evaluated TCGA.OV.sampleMap/HT_HG-U133A) TCGA OV RNA-seq dataset comprised was downloaded 308 samples, for includingthe analy- 304sis of primary gene expression tumor tissue level and according 4 normal to solid histologic tissue samples.grade. TCGA Additionally, OV microarray the microarray dataset datasetcomprised (dataset 593 samples. ID: TCGA.OV.sampleMap/HT_HG-U133A) In this data, gene expression was measured was downloaded using Affymetrix for the anal- HT ysisHuman of gene Genome expression U133a level microarray according platform. to histologic Gene grade. expression TCGA level OV microarray in this data dataset is in comprisedlog2(x). Patients 593 samples. without Insufficient this data, information gene expression of histologic was measured grade were using excluded. Affymetrix This HTstudy Human met the Genome publication U133a guidelines microarray provided platform. by TCGA Gene expression (https://www.cancer.gov/about- level in this data is in log2(x).nci/organization/ccg/research/st Patients without sufficientructural-genomics/tcga/using-t information of histologic gradecga/citing-tcga, were excluded. date Thislast studyaccessed met on the 31 publication March 2021). guidelines provided by TCGA (https://www.cancer.gov/about- nci/organization/ccg/research/structural-genomics/tcga/using-tcga/citing-tcga, date last2.7. accessedStatistical on Analysis 31 March 2021). 2.7. StatisticalSPSS version Analysis 26.0 for Windows (SPSS Inc, Chicago, IL, USA) was used to analyze the collectedSPSS data. version The 26.0difference for Windows between (SPSS gene Inc,expression Chicago, and IL, clinical USA) information was used to was analyze ana- thelyzed collected by performing data. The the difference Pearson’s between chi-squared gene test expression for categorical and clinical variables. information The Student’s was analyzedt-test was byused performing to analyze the the Pearson’s difference chi-squared between patients test for with categorical histologic variables. grades 1 Theor 2 Student’sand thoset -testwith washistologic used to grades analyze 3 theor 4. difference A p-value between less than patients 0.05 was with considered histologic statisti- grades 1cally or 2 significant. and those with histologic grades 3 or 4. A p-value less than 0.05 was considered statistically significant. 3. Results 3.3.1. Results DEG Identification 3.1. DEGScreening Identification by GEO2R analysis revealed 54, 660, 52, 001, and 54, 666 DEGs from the GSE14407,Screening GSE36668, by GEO2R and GSE38666 analysis revealed datasets, 54,660,respectively. 52,001, Then, and Venn 54,666 diagram DEGs fromsoftware the GSE14407,was used for GSE36668, identifying and the GSE38666 common datasets, DEGs am respectively.ong the three Then, datasets. Venn diagram Overall, software 15 com- wasmon used DEGs for were identifying identified the common(Figure 1), DEGs incl amonguding nine the threeup-regulated datasets. Overall,and six down-regu- 15 common DEGslated genes were in identified serous ovarian (Figure cancer1), including tissues compared nine up-regulated with normal and ovarian six down-regulated tissues (Figure genes2 in serousand ovarian cancer tissuesTables compared with normal2 ovarian tissuesand (Figure2 and3). TablesLOC101930363/LOC101928349/LOC100507387/2 and3). LOC101930363/LOC101928349/LOC100507387/FAM153C/FAM153A/FAM153C/FAM153A/FAM153B, among FAM153B,the down-regulated among the DEGs, down-regulated was not evaluated DEGs, was further not evaluated due to insufficient further due information to insufficient on informationthe gene. on the gene.

Figure 1. Venn diagram of DEGs among the GEO datasets.

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Figure 1. Venn diagram of DEGs among the GEO datasets.

Figure 2. HeatmapFigure 2. showing Heatmap the showing 15 identified the 15 DEGs identified in the DEGs 3 independent in the 3 independent GEO datasets. GEO The datasets. data are The presented data in matrix format, withare rows presented representing in matrix individual format, with genes rows and repres columnsenting representing individual each genes tissue. and Eachcolumns cell inrepresenting the matrix represents each tissue. Each cell in the matrix represents the relative expression level of a genetic characteristic the relative expression level of a genetic characteristic of an individual tissue. The red and green colors in the cells of an individual tissue. The red and green colors in the cells reflect relatively high and low expres- reflect relatively high and low expression levels, respectively, as indicated by the scale bar. N: normal; T: tumor; LOC: sion levels, respectively, as indicated by the scale bar. N: normal; T: tumor; LOC: LOC101930363/LOC101928349/LOC100507387/FAM153C/FAM153A/FAM153B.LOC101930363/LOC101928349/LOC100507387/FAM153C/FAM153A/FAM153B.

Table 2. Up-regulatedTable 2.DEGsUp-regulated in serous ovar DEGsian in cancer serous of ovarian the GEO cancer datasets. of the GEO datasets. Log2FC Adjusted p-Value Gene Names Log2FC Adjusted p-Value GeneGSE14407 Names GSE36668 GSE38666 GSE14407 GSE36668 GSE38666 GSE14407 GSE36668 GSE38666 GSE14407 GSE36668 GSE38666 BIRC5 3.784515 13.83283 3.792818 4.58 × 10−6 2.45 × 10−4 3.34 × 10−8 BIRC5 3.784515 13.83283 3.792818 4.58 × 10−6 2.45 × 10−4 3.34 × 10−8 CDCA3 3.40002 3.542135 3.543772 9.12 × 10−7 2.72 × 10−3 2.20 × 10−9 CDCA3 3.40002 3.542135 3.543772 9.12 × 10−7 2.72 × 10−3 2.20 × 10−9 −7 −3 −9 CENPF CENPF3.904451 3.904451 3.450972 3.450972 3.934447 3.934447 1.69 × 10 1.69 3.17× 10 × −107 3.17 2.20× 10 × −103 2.20 × 10−9 KIF4A KIF4A3.533572 3.533572 5.173891 5.173891 3.746773 3.746773 4.76 × 10−6 4.76 2.84× 10 × −106 −3 2.84 1.70× 10 × −103 −8 1.70 × 10−8 NCAPG NCAPG3.233566 3.233566 3.533272 3.533272 3.092676 3.092676 5.15 × 10−6 5.15 3.17× 10 × −106 −3 3.17 1.26× 10 × −103 −7 1.26 × 10−7 −6 −3 −8 RRM2 RRM23.212708 3.212708 4.504844 4.504844 3.215252 3.215252 3.24 × 10−6 3.24 3.17× 10 × 10−3 3.17 2.23× 10 × 10−8 2.23 × 10 UBE2C 2.539233 3.816594 2.699924 × −6 × −3 × −7 UBE2C 2.539233 3.816594 2.699924 7.37 × 10−6 7.37 2.5610 × 10−3 2.56 1.2610 × 10−7 1.26 10 VEGFA 2.86746 2.906894 2.647287 2.89 × 10−6 2.84 × 10−3 4.33 × 10−7 −6 −3 −7 VEGFA NR2F62.86746 2.950696 2.906894 2.75339 2.647287 3.029833 2.89 × 10 1.85 2.84× 10 × −105 2.72 4.33× 10 × −103 1.63 × 10−7 NR2F6 2.950696 2.75339 3.029833 1.85 × 10−5 2.72 × 10−3 1.63 × 10−7 DEGs: differentially expressed genes; FC: fold change. DEGs: differentially expressed genes; FC: fold change. Table 3. Down-regulated DEGs in serous ovarian cancer of the GEO datasets. Table 3. Down-regulated DEGs in serous ovarian cancer of the GEO datasets. Log2FC Adjusted p-Value Gene Names Log2FC Adjusted p-Value Gene Names GSE14407 GSE36668 GSE38666 GSE14407 GSE36668 GSE38666 GSE14407 GSE36668 GSE38666 GSE14407 GSE36668 GSE38666 C21orf62 −3.784515 −13.83283 −3.792818 4.58 × 10−6 2.45 × 10−4 3.34 × 10−8 −6 −4 −8 C21orf62 MUM1L1 –3.784515−3.40002 –13.83283− 3.542135 –3.792818 −3.543772 4.58 × 10 9.12 × 2.4510− ×7 10 2.72 3.34× 10 ×− 103 2.20 × 10−9 MUM1L1 NBEA –3.40002−3.904451 –3.542135− 3.450972 –3.543772 −3.934447 9.12 × 10−7 1.69 × 2.7210− ×7 10−3 3.17 2.20× 10 ×− 103 −9 2.20 × 10−9 NBEA RNASE4 –3.904451−3.533572 –3.450972− 5.173891 –3.934447 −3.746773 1.69 × 10−7 4.76 × 3.1710− ×6 10−3 2.84 2.20× 10 ×− 103 −9 1.70 × 10−8 −6 −3 −7 RNASE4 RORA –3.533572−3.233566 –5.173891− 3.533272 –3.746773 −3.092676 4.76 × 10−6 5.15 × 2.8410 × 10−3 3.17 1.70× 10 × 10−8 1.26 × 10 LOC101930363 RORA –3.233566 –3.533272 –3.092676 5.15 × 10−6 3.17 × 10−3 1.26 × 10−7 /LOC101928349 LOC101930363/LOC100507387 −4.26894 −3.17681 −4.17039 4.56 × 10−7 0.003168 8.62 × 10−9 /LOC101928349/FAM153C /LOC100507387/FAM153A –4.26894 –3.17681 –4.17039 4.56 × 10−7 0.003168 8.62 × 10−9 /FAM153C /FAM153B /FAM153A DEGs: differentially expressed genes; FC: fold change. /FAM153B DEGs: differentially3.2. Expression expressed of the genes; Identified FC: fold DEGs change. between Normal and Serous Ovarian Cancer Tissues The expression of the identified DEGs between normal and tumor tissues was analyzed using RNA–seq data from TNMplot.com. The up–regulated DEGs showed significantly higher expression in tumor tissues compared with normal tissues (Figure3, Table4),

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Medicina 2021, 57, 9333.2. Expression of the Identified DEGs between Normal and Serous Ovarian Cancer Tissues 6 of 15 The expression of the identified DEGs between normal and tumor tissues was ana- lyzed using RNA–seq data from TNMplot.com. The up–regulated DEGs showed signifi- cantly higher expression in tumor tissues compared with normal tissues (Figure 3, Table 4), whereas thewhereas down-regulated the down-regulated DEGs showed DEGs significantly showed higher significantly expression higher in normal expression in normal tissues compared with tumor tissues (Figure4, Table5). tissues compared with tumor tissues (Figure 4, Table 5).

Figure 3. BoxFigure plots 3. of Box up-regulated plots of up-regulated DEGs expression DEGs betweenexpression normal between and normal serous ovarianand serous cancer ovarian tissues cancer in the RNA-seq dataset of TCGAtissues OV. in (theA) BIRC5RNA-seq,(B )datasetCDCA3 of,(C TCGA) CENPF OV.,(D (A) KIF4A) BIRC5,(E, )(BNCAPG) CDCA3,(F, )(NR2F6C) CENPF,(G), RRM2(D) KIF4A,(H), UBE2C(E) , and (I) VEGFA mRNANCAPG expression., (F) NR2F6, (G) RRM2, (H) UBE2C, and (I) VEGFA mRNA expression.

Table 4. Up-regulated DEGs in serous ovarian cancer of the RNA-seq dataset of TCGA OV. Table 4. Up-regulated DEGs in serous ovarian cancer of the RNA-seq dataset of TCGA OV. Gene Names Fold Change p-Value Gene Names Fold Change p-Value BIRC5 157.01 1.09 × 10−65 −65 CDCA3 BIRC5 2.58 157.01 1.7 × 10−29 1.09 × 10 CDCA3 2.58 1.7 × 10−29 CENPF 18.82 1.23 × 10−63 CENPF 18.82 1.23 × 10−63 −64 KIF4A KIF4A 34.68 34.68 2.54 × 10 2.54 × 10−64 NCAPG NCAPG 29.63 29.63 5.23 × 10−64 5.23 × 10−64 NR2F6 NR2F6 11.6 11.6 4.15 × 10−65 4.15 × 10−65 −64 RRM2 RRM2 41.23 41.23 2.99 × 10−64 2.99 × 10 UBE2C 101.05 3.88 × 10−65 UBE2C 101.05 3.88 × 10−65 VEGFA 2.61 3.13 × 10−34 VEGFA 2.61 3.13 × 10−34 DEGs: differentially expressed genes. DEGs: differentially expressed genes.

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Figure 4. Box plots of down-regulated DEGs expression between normal and serous ovarian cancer

tissues in the RNA-seq dataset of TCGA OV. (A) C21orf62, (B) PWWP3B, (C) NBEA, (D) RNASE4, Figure 4. BoxFigure plots 4. of Box down-regulated plotsand of ( Edown-regulated) RORA DEGs mRNA expression DEGsexpression. betweenexpression normal between and normal serousovarian and serous cancer ovarian tissues cancer in the RNA-seq dataset of TCGAtissues OV. in the (A) RNA-seqC21orf62,( datasetB) PWWP3B of TCGA,(C) OV.NBEA (A,() DC21orf62) RNASE4, (B,) and PWWP3B (E) RORA, (C)mRNA NBEA, expression.(D) RNASE4, and (E) RORA mRNATable expression.5. Down-regulated DEGs in serous ovarian cancer of the RNA-seq dataset of TCGA OV. Table 5. Down-regulated DEGs in serous ovarian cancer of the RNA-seq dataset of TCGA OV. Table 5. Down-regulated GeneDEGs Namesin serous ovarian cancer of Fold the RNA-seq Change dataset of TCGA OV.p -Value GeneC21orf62 Names Fold0.02 Change 8.79p-Value × 10−63 Gene Names Fold Change p-Value MUM1L1(PWWP3B) 0.01 5.22 × 10−−6365 C21orf62 C21orf62 0.02 0.02 8.79 × 10−63 8.79 × 10 MUM1L1(PWWP3B)NBEA 0.110.01 5.22 2.62× × 1010−−6564 MUM1L1(PWWP3B) 0.01 5.22 × 10−65 RNASE4NBEA 0.010.11 2.62 6.84× × 1010−−6466 NBEA 0.11 2.62 × 10−64 −66 RNASE4RORA 0.150.01 6.84 4.99× × 1010−63 RNASE4 RORA 0.01 0.15 6.84 × 10−66 4.99 × 10−63 DEGs: differentially expressed genes. RORADEGs: differentially expressed genes.0.15 4.99 × 10−63 DEGs: differentially expressed genes. 3.3. PPI Network Construction 3.3. PPI Network ConstructionThe PPI network was analyzed and visualizedvisualized using CytoscapeCytoscape software.software. Using the STRING database and MCODE tool, among the 14 DEGs shared by the 3 GEO datasets, The PPI networkSTRING was database analyzed and and MCODE visualiz tool,ed using among Cytoscape the 14 DEGs software. shared Using by the the 3 GEO datasets, we identified one cluster with 7 nodes and 21 edges (Figure 5). The clustered genes were STRING databasewe identifiedand MCODE one tool, cluster among with 7the nodes 14 DEGs and 21shared edges by (Figure the 3 5GEO). The datasets, clustered genes were BIRC5, CDCA3, NCAPG, UBE2C, KIF4A, CENPF, and RRM2, which were all up-regulated we identified oneBIRC5 cluster, CDCA3 with, NCAPG7 nodes ,andUBE2C 21 edges, KIF4A (Figure, CENPF 5)., The and clusteredRRM2, which genes were were all up-regulated DEGs. CDCA3 was identified as the seed node from which the cluster was derived. BIRC5, CDCA3DEGs., NCAPGCDCA3, UBE2Cwas, KIF4A identified, CENPF as the, and seed RRM2 node, which from whichwere all the up-regulated cluster was derived. DEGs. CDCA3 was identified as the seed node from which the cluster was derived.

Figure 5. Clustered DEGsDEGs identifiedidentified by by using using MCODE MCODE in in PPI PPI network. network. Seven Seven nodes nodes and and twenty–one twenty–

oneedges edges were were identified. identified. Square Square shape shape node node is a seedis a seed node, node, and otherand other nodes nodes have have same same score. score. Figure 5. Clustered DEGs identified by using MCODE in PPI network. Seven nodes and twenty– one edges were identified. Square shape node is a seed node, and other nodes have same score.

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3.4. Clustered Genes Are Mainly Involved in Cell Cycle Regulation To analyze the functional profile of the clustered DEGs, GO term analysis was per- formed using DAVID. The up–regulated DEGs, especially the clustered DEGs, were mainly involved in cell division, positive regulation of exit from mitosis, and mitotic nuclear division, whereas the down-regulated DEGs were not functionally clustered, with ex- ception of RORA, which was involved in the intracellular receptor signaling and steroid hormone–mediated signaling pathways (Table6).

Table 6. GO terms of DEGs in serous ovarian cancer.

Category Term Gene Names p-Value BIRC5, CDCA3, CENPF, NCAPG, GO_BP GO:0051301 Cell division 5.5 × 10−5 UBE2C GO:0031536 Positive regulation of exit from mitosis BIRC5, UBE2C 3.9 × 10−3 GO:0000278 Mitotic nuclear division BIRC5, CDCA3, CENPF 1.1 × 10−2 GO:0016567 Protein ubiquitination BIRC5, CDCA3, UBE2C 2.2 × 10−2 GO:0030522 Intracellular receptor signaling pathway RORA, NR2F6 2.5 × 10−2 GO:0043401 Steroid hormone mediated signaling RORA, NR2F6 3.7 × 10−2 pathway GO:0043154 Negative regulation of cysteine–type BIRC5, VEGFA 4.4 × 10−2 endopeptidase activity involved in apoptotic process BIRC5, CDCA3, CENPF, KIF4A, GO_CC Cytosol 2.2 × 10−3 NBEA, NCAPG, RRM2, UBE2C Midbody BIRC5, CENPF, KIF4A 3.1 × 10−3 RORA, BIRC5, CENPF, KIF4A, NR2F6, Nucleoplasm 5.1 × 10−3 RRM2, UBE2C Spindle microtubule BIRC5, KIF4A 2.9 × 10−2 , centromeric region BIRC5, CENPF 3.7 × 10−2 RNA polymerase II transcription factor activity, ligand GO_MF RORA, NR2F6 2.3 × 10−2 activated sequence–specific DNA Steroid hormone receptor activity RORA, NR2F6 3.6 × 10−2 RORA, BIRC5, CDCA3, CENPF, Protein binding KIF4A, NCAPG, NR2F6, RRM2, 4.4 × 10−2 UBE2C, VEGFA GO, gene ontology; BP, biological process; CC, cellular component; MF, molecular function.

3.5. Higher Expression of CDCA3 and UBE2C Associated with Poor OS in All Stages Prior to the Kaplan–Meier survival analysis using the Kaplan–Meier plotter, all pa- tients with serous ovarian cancer were categorized into disease stages as early (including stage I and II) and advanced (including stage III and IV) stages. This showed that higher expressions of CDCA3, CENPF, NCAPG, RRM2, UBE2C, and NBEA were associated with worse OS regardless of serous ovarian cancer stages (Figure6). Moreover, in patients with early stages, higher expressions of CDCA3, CENPF, NCAPG, N2RF6, RRM2, UBE2C, and NBEA were associated with worse OS (Figure7). However, in the advanced stages, only higher expressions of CDCA3, NR2F6, and UBE2C were associated with worse OS, whereas higher expression of BIRC5 was associated with better OS (Figure8). Medicina 2021, 57, 933 9 of 15 Medicina 2021, 57, x FOR PEER REVIEW 9 of 15

Figure 6. SurvivalFigure 6. analyses Survival of analyses the dysregulated of the dysregulated genes in all genes stages in of all serous stages ovarian of serous cancer. ovarian Kaplan–Meier cancer. Kaplan– plots of overall Meier plots of overall survival in subjects with higher versus lower expression. (A) BIRC5, (B) survival in subjects with higher versus lower expression. (A) BIRC5,(B) CDCA3,(C) CENPF,(D) KIF4A,(E) NCAPG,(F) CDCA3, (C) CENPF, (D) KIF4A, (E) NCAPG, (F) NR2F6, (G) RRM2, (H) UBE2C, (I) VEGFA, (J) NR2F6,(G) RRM2,(H) UBE2C,(I) VEGFA,(J) C21orf62,(K) MUM1L1,(L) NBEA,(M) RNASE4, and (N) RORA mRNA C21orf62, (K) MUM1L1, (L) NBEA, (M) RNASE4, and (N) RORA mRNA expression. expression.

Medicina 2021, 57, 933 10 of 15 Medicina 2021, 57, x FOR PEER REVIEW 10 of 15

Figure 7. SurvivalFigure analyses7. Survival of analyses the dysregulated of the dysregulated genes in the genes early in stages the early of serous stages ovarian of serous cancer. ovarian Kaplan–Meier cancer. plots of overall survivalKaplan–Meier in subjects plots with higherof overall versus survival lower in expression. subjects with (A) higherBIRC5,( versusB) CDCA3 lower,( Cexpression.) CENPF,( D(A) )KIF4A BIRC5,(,E ) NCAPG, (B) CDCA3, (C) CENPF, (D) KIF4A, (E) NCAPG, (F) NR2F6, (G) RRM2, (H) UBE2C, (I) VEGFA, (J) (F) NR2F6,(G) RRM2,(H) UBE2C,(I) VEGFA,(J) C21orf62,(K) MUM1L1,(L) NBEA,(M) RNASE4, and (N) RORA mRNA C21orf62, (K) MUM1L1, (L) NBEA, (M) RNASE4, and (N) RORA mRNA expression. expression.

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Figure 8. SurvivalFigure 8. analyses Survival of analyses the dysregulated of the dysreg genesulated in the genes advanced in the stagesadvanced of serous stages ovarian of serous cancer. ovarian Kaplan–Meier can- plots of overall survivalcer. Kaplan–Meier in subjects plots with of higher overall versus survival lower in expression. subjects with (A )higherBIRC5 ve,(Brsus) CDCA3 lower,( expression.C) CENPF,( (AD)) KIF4A,(E) BIRC5, (B) CDCA3, (C) CENPF, (D) KIF4A, (E) NCAPG, (F) NR2F6, (G) RRM2, (H) UBE2C, (I) VEGFA, NCAPG,(F) NR2F6,(G) RRM2,(H) UBE2C,(I) VEGFA,(J) C21orf62,(K) MUM1L1,(L) NBEA,(M) RNASE4, and (N) RORA (J) C21orf62, (K) MUM1L1, (L) NBEA, (M) RNASE4, and (N) RORA mRNA expression. mRNA expression. 3.6. Higher CDCA3 and UBE2C Expression Associated with Higher Histologic Grades Based on data3.6. Higherfrom the CDCA3 TCGA and OV UBE2C cohort, Expression we analyzed Associated the association with Higher between Histologic ex- Grades pressions of DEGs andBased clinicopathological on data from the feat TCGAures of OV serous cohort, ovarian we analyzed cancer. Higher the association ex- between pressions of CDCA3expressions and UBE2C of DEGs were and significantly clinicopathological associated features with ofhigher serous histologic ovarian cancer. Higher

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expressions of CDCA3 and UBE2C were significantly associated with higher histologic grades, but not with other clinical features, including age, clinical stage, lymphatic invasion, venousgrades, invasion, but not primarywith other therapy clinical outcome,features, including personal age, tumor clinical status, stage, and lymphatic vital status inva- (Figure 9 andsion, Supplementary venous invasion, Tables primary S1 therapy and S2). outcome, personal tumor status, and vital status (Figure 9 and Supplementary Tables S1 and S2).

FigureFigure 9. Higher 9. Higher expression expression levels levels of CDCA3 of CDCA3and andUBE2C UBE2Care are associated associated with with higher higher histologichistologic grade of of tumor. tumor. (A (A) )Box Box plot plot of the relative CDCA3 expression levels and (B) UBE2C expression levels in the grade 1 and 2 and grade 3 and 4 serous of the relative CDCA3 expression levels and (B) UBE2C expression levels in the grade 1 and 2 and grade 3 and 4 serous ovarian cancer tissues. ovarian cancer tissues. 4. Discussion 4. DiscussionOvarian cancer is one of the leading causes of cancer death in women’s reproductive organs [1]. According to the Surveillance, Epidemiology, and End Results Program: Ovar- ianOvarian Cancer cancer(http://seer.cancer.gov/, is one of the leading date causeslast accessed of cancer on 5 death July 2021), in women’s in 2021, reproductive approxi- or- gansmately [1]. According21 ,410 patients to the are Surveillance, expected to be Epidemiology, diagnosed, and and 13, End770 are Results estimated Program: to die Ovarianof Cancerovarian (http://seer.cancer.gov/ cancer in the United States, date [20]. last Approximately accessed on 70% 5 July of newly 2021), diagnosed in 2021, approximately ovarian 21,410cancer patients patients are are expected diagnosed to in be advanced diagnosed, stages and [2]. 13,770Most patients, are estimated even those to diewith of ad- ovarian cancervanced in thestage United cancer, Statesshow effi [20cient]. Approximately treatment outcomes 70% with of newly primary diagnosed treatment, ovarian which is cancer patientsgenerally are primary diagnosed debulking in advanced surgery stagesfollowe [d2 ].by Most taxane/platinum- patients, evenbased those chemotherapy with advanced stage[21]. cancer, Nevertheless, show efficientup to 80% treatment of these patients outcomes experience with primary cancer recurrence, treatment, which which is as- is gener- allysociated primary with debulking extremelysurgery poor survival followed outcom byes taxane/platinum-based [22]. There are currently established chemotherapy fac- [21]. Nevertheless,tors for predicting up to 80% recurrence of these and patients prognosis experience including cancer age, recurrence, clinical stages, which histologic is associated withgrades, extremely and size poor of residual survival diseases outcomes after surgery [22]. There [23,24]. are Despite currently these prognostic established factors, factors for heterogenic outcome of ovarian cancer to standard treatment makes these factors less re- predicting recurrence and prognosis including age, clinical stages, histologic grades, and liable, which suggests finding more reliable factors for predicting prognosis of ovarian sizecancer of residual might provide diseases clinical after surgeryvalue. [23,24]. Despite these prognostic factors, heterogenic outcomeApproximately of ovarian cancer 90% of to ovaria standardn cancers treatment are epithelial makes ovarian these factors cancer, lessamong reliable, which which suggeststhe vast finding majority more are serous reliable ovarian factors cancer fors predicting [25]. Although prognosis the ovarian of ovarian cancer develop- cancer might providemental clinical mechanism value. is not fully understood, numerous bioinformatics tools such as micro- arrayApproximately and high-throughput 90% of ovariansequencing cancers have arebeen epithelial developed ovarian over the cancer, past few among decades, which the vastenabling majority the are discovery serous ovarianof biomarkers cancers for [diag25].nosis Although and prognosis. the ovarian In the cancer present developmental study, mechanismwe focused is on not identifying fully understood, the common numerous DEGs based bioinformatics on microarray tools data such from as three microarray GEO and high-throughputdatasets. By finding sequencing common DEGs have between been developed normal and over tumor the tissues past fewwith decades, the most sig- enabling thenificant discovery FC from of biomarkers within each forGEO diagnosis dataset, we and identified prognosis. nine In up-regulated the present DEGs study, and we five focused ondown-regulated identifying the DEGs. common DEGs based on microarray data from three GEO datasets. The PPI network of 14 DEGs revealed one cluster that comprised the seven most By finding common DEGs between normal and tumor tissues with the most significant functionally connected genes (BIRC5, CDCA3, CENPF, KIF4A, NCAPG, RRM2, and FCUBE2C) from within, which each were GEO all up–regulated dataset, we identifiedDEGs. Among nine the up-regulated clustered genes, DEGs CDCA3 and five was down- regulatedidentified DEGs. to act as a key component in this cluster. GO term analysis of the 14 DEGs fur- therThe showed PPI network that the up-regulated of 14 DEGs DEGs, revealed especially one cluster the clustered that comprised genes identified the sevenin the most functionallyPPI network, connected were mainly genes involved (BIRC5 ,inCDCA3 the biological, CENPF processes, KIF4A, NCAPGof cell division,, RRM2 ,positive and UBE2C) , whichregulation were allof up–regulatedexit from mitosis, DEGs. and mitotic Among nuclear the clustered division. genes, UncontrolledCDCA3 cellwas cycle identified is a to act as a key component in this cluster. GO term analysis of the 14 DEGs further showed that the up-regulated DEGs, especially the clustered genes identified in the PPI network, were mainly involved in the biological processes of cell division, positive regulation of exit from

Medicina 2021, 57, 933 13 of 15

mitosis, and mitotic nuclear division. Uncontrolled cell cycle is a common feature of cancer, and it has been a main therapeutic target to impair cancer cell proliferation. However, the prognostic value of aberrant expression of genes involved in the regulation of cell cycle in ovarian cancer is still unclear. To verify the prognostic value of the identified DEGs, Kaplan–Meier survival analysis was performed based on the 14 DEGs. Overall, five genes among the down–regulated DEGs were not associated with favorable outcome in OS, but five out of the nine up–regulated DEGs were associated with worse survival regardless of the stage of serous ovarian cancer, and six genes were associated with worse survival in early stage (stage I and II) of ovarian cancer. Remarkably, higher expression of BIRC5 was associated with better OS in the advanced stages of serous ovarian cancer. Additionally, higher expression of NBEA was associated with worse OS in the early stages of serous ovarian cancer. However, BIRC5 and NBEA did not show a significant difference in clinical-pathologic features (data not shown). In absence of additional supporting evidence, it is not possible to conclude that dysregulation of BIRC5 and NBEA affects the prognosis of serous ovarian cancer. Interest- ingly, while the associations between OS and some DEGs were not significant or were even ambivalent, CDCA3 and UBE2C, which were among the up–regulated DEGs, were found to be consistently associated with worse survival in all stages, early stages and advanced stages. Moreover, a higher expression of the CDCA3 and UBE2C genes was significantly associated with higher tumor histologic grades. Of note, CDCA3 was identified as a key component among the clustered genes within the DEGs. Ubiquitin-conjugating enzyme, E2C, is a member of the UBE2 family that mediates cell cycle progression and mitotic cyclin destruction. Recent studies showed that overexpression of UBE2C is associated with various cancers such as breast, colon, lung, and liver cancers. It was also reported that the aberrant expression of UBE2C is associated with some gynecologic cancers, such as en- dometrial and ovarian cancers. Some studies suggested UBE2C as a prognostic biomarker and potential therapeutic target in certain cancers. For example, Zhang et al. suggested UBE2C as a prognostic marker and a promising therapeutic target in gastric cancer [26], and Li et al. reported UBE2C as a prognostic biomarker and a potential therapeutic target associated with chemo–resistance in ovarian cancer [27]. In the contrast, little is known about the association of CDCA3 and ovarian cancer. CDCA3 cell–division–cycle-associated protein–3 is a member of the CDCA family and modulates mitosis entry and cell cycle, which is regulated by protein degradation during the G1 phase [28]. Adams et al. sug- gested CDCA3 as a prognostic biomarker and potential therapeutic target in non-small–cell lung cancer [29], and Zhang et al. suggested CDCA3 as a potential prognostic marker in gastric cancer [30]. In the present study, we identified 14 DEGs from GEO datasets. Among them, we found nine up–regulated DEGs and seven clustered genes that were mainly associated with cell division and mitosis. By analysis of the clinical data, we found that CDCA3 and UBE2C were associated with poor prognosis in serous ovarian cancer patients. Further studies are necessary to verify the prognostic value of CDCA3 and UBE2C and to further understand the role of these genes in ovarian cancer to establish them as potential therapeutic targets.

5. Conclusions Based on the above-mentioned findings, CDCA3 and UBE2C were identified as being associated with higher tumor histologic grades and poor survival outcomes in patients with serous ovarian cancer. We suggested that CDCA3 and UBE2C may represent valuable biomarkers to predict the outcome of serous ovarian cancer. Nevertheless, further investi- gations are warranted to obtain a better understanding of their function in serous ovarian cancer.

Supplementary Materials: The following are available online at https://www.mdpi.com/article/ 10.3390/medicina57090933/s1. Table S1: CDCA3 mRNA expression levels in relation to clinico- pathological parameters of TCGA OV. Table S2: UBE2C mRNA expression levels in relation to clinicopathological parameters of TCGA OV. Medicina 2021, 57, 933 14 of 15

Author Contributions: Conceptualization, C.S. and S.K.; methodology, C.S. and S.K.; software, C.S.; validation, J.-H.L. and S.K.; formal analysis, C.S. and K.-B.K.; investigation, C.S.; data curation, S.K.; writing—original draft preparation, C.S. and K.-B.K.; writing—review and editing, J.-H.L. and S.K.; visualization, C.S.; supervision, S.K.; project administration, C.S. and S.K.; funding acquisition, S.K. All authors have read and agreed to the published version of the manuscript. Funding: This study was supported by grants from the National Research Foundation of Korea (grant funded by the Korea Government Ministry of Science, ICT and Future Planning; grant no. 2021R1I1A3A04037479). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: All available data are presented within the article or are available from the corresponding authors upon adequate request. Acknowledgments: We would like to thank all members of our research group for their enthusiastic participation in this study. This study met the publication guidelines provided by TCGA (https:// www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga/using-tcga/citing- tcga, date last accessed on 31 March 2021). Conflicts of Interest: The authors declare no conflict of interest.

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