CENPA Is One of the Potential Key Genes Associated with The

4086

Original Article

CENPA is one of the potential key genes associated with the proliferation and prognosis of ovarian cancer based on integrated bioinformatics analysis and regulated by MYBL2

Jing Han1, Rongkai Xie1, Ying Yang1, Diangang Chen2, Li Liu3, Jiayang Wu1, Sufen Li1

1Department of Obstetrics and Gynecology, Xinqiao Hospital, Army Medical University, Chongqing, China; 2Cancer Institute of PLA, Xinqiao Hospital, Army Medical University, Chongqing, China; 3Department of Orthopedics, Chengdu Seventh People’s Hospital, Chengdu, China Contributions: (I) Conception and design: J Han, S Li; (II) Administrative support: R Xie, S Li; (III) Provision of study materials or patients: Y Yang, D Chen, L Liu; (IV) Collection and assembly of data: J Han, J Wu, Y Yang, D Chen, L Liu; (V) Data analysis and interpretation: J Han, R Xie, S Li; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors. Correspondence to: Sufen Li. Department of Obstetrics and Gynecology, Xinqiao Hospital, Army Medical University, Xinqiao Street No. 2r, Chongqing 400037, China. Email: [email protected].

Background: Ovarian cancer (OV) is a highly lethal disease, and the fifth leading cause of all cancer- related deaths in women. The study aimed to identify potential key genes associated with the proliferation and prognosis of OV. Methods: Differentially expressed genes (DEGs) between ovarian cancer and normal tissues were screened by the robust rank aggregation (RRA) method. The expression of CENPA and MYBL2 were examined in SKOV3 and A2780 ovarian cancer cell lines and tumor tissues by qRT-PCR and western blot. Small RNA interference assays, plasmid overexpression assays and EdU assays were used to validate the proliferative effect of the MYBL2-CENPA axis in ovarian cancer cell lines. The ChIP assay was used to verify the direct regulation of MYBL2 on CENPA. Results: 133 up-regulated genes and 158 down-regulated genes were identified, and the up-regulated genes mainly enrichment in cell cycle. The three up-regulated gene with DNA separation (CENPA, CENPF and CEP55) might be tightly correlated with proliferation and prognosis of OV. Knockdown CENPA expression inhibited the proliferation of A2780 and SKOV3 cells After the knockout of MYBL2, the expression of CENPA significantly decreased. MYBL2 directly binds to the promoter region of CENPA. Conclusions: The MYBL2-CENPA pathway plays an important role in the proliferation of ovarian cancer cells, suggesting that this pathway may be a potential target for the treatment of ovarian cancer.

Keywords: MYBL2; CENPA; ovarian cancer; proliferation; robust rank aggregation (RRA)

Submitted Jan 26, 2021. Accepted for publication Jun 21, 2021. doi: 10.21037/tcr-21-175 View this article at: https://dx.doi.org/10.21037/tcr-21-175

Introduction only a small fraction of all cases. HGSCs are characterized by rapid growth and aggressive behavior (3). The Ovarian cancer is a highly lethal disease, and the fifth leading combination of cytoreductive surgery and chemotherapy has cause of all cancer-related deaths in women. Furthermore, there have been approximately 295,414 (1.6%) new cases been the most important treatment strategy for HGSC, in and 184,799 (1.9%) deaths in 185 countries (1), and 22,530 terms of improving patient survival. However, the treatment new cases and 13,980 deaths in the United States per year (2). efficacy remains unsatisfactory (4). Therefore, in order to Ovarian cancer can be classified as high-grade serous better treat ovarian cancer, it is very important to understand carcinoma (HGSC) and low-grade serous (LGSC), and that’s the pathogenesis in greater detail.

MYB-related protein B (B-MYB/MYBL2) is a Integrated analysis of microarray datasets transcription factor and plays important roles in the The Limma software package (20) in the R software was used regulation of cell cycle, survival and differentiation. to normalize the matrix data of each GEO data set and the In addition, it is often abnormally expressed in cancer, base 2 logarithm conversion was used, and the DEGs between suggesting that it functions in driving the onset and/or tumor and normal tissues was screened by Limma package. progression of cancer (5,6). In glioma, demethylzeylasteral The R package “RobustRankAggreg” (21) is based on the can inhibit glioma cell growth via the miR-30e-5p/MYBL2 Robust Rank Aggregation (RRA) method to perform genetic axis (7). In addition, studies have shown that MYBL2 is integration on DEGs determined from six data sets. Based on regulated by the Akt/FoxM1 signaling pathway in gliomas. the null hypothesis of irrelevant inputs, the genes screened This may become a biomarker for glioma radiation by this RRA method are always better than expected (21). therapy (8). In non-small-cell lung cancer (NSCLC), |log2FC| ≥0.5, and adjust P value <0.05 were used. MYBL2 is aberrantly upregulated. Its overexpression can significantly promote NSCLC cell growth and motility by suppressing IGFBP3 (9). Furthermore, miR-30a Functional enrichment analysis of DEGs may suppress the proliferation of NSCLC by targeting To elucidate possible differences in biological processes, MYBL2 (10). In breast cancer, MYBL2 regulates cell molecular functions, and cellular components with DEGs, proliferation and apoptosis, and is targeted by miR-143- GO enrichment analysis (22) and Kyoto Encyclopedia 3p (11). Mybl2 upregulation leads to an imbalance in cell of Genes and Genomes (KEGG) pathway enrichment cycle regulation that promotes liver cancer progression (12). analysis (23) were performed. In leukemic blasts, MYBL2 is regulated by CCNA1, and promotes cell proliferation (13). The present study evaluates the prognostic value of Cell lines and culture MYBL2 in ovarian cancer, and explores the molecular The SKOV3 and A2780 ovarian cell lines were purchased mechanisms underlying its role in the disease. The results from ATCC (Manassas, VA, USA). Cells were cultured at indicate that the abnormally high expression of MYBL2 37 in DMEM (Hyclone, Logan, UT, USA) with 10% promotes the proliferation of ovarian cancer cells mainly FBS (Hyclone) in a humidified incubator with 5% CO2. through the direct transcriptional regulation of centromere ℃ protein A (CENPA) expression. Therefore, targeting the MYBL2-CENPA axis may provide a new approach to the Small interfering RNA transfection treatment of ovarian cancer. Lipofectamine 3000 (Thermo Fisher Scientific, We present the following article in accordance with the Waltham, MA, USA) was used to transfect the MYBL2 MDAR checklist (available at https://dx.doi.org/10.21037/ or CENPA siRNA (GenePharma, Shanghai, China) tcr-21-175). into cells, according to manufacturer’s instructions. The siRNA sequences were as follows: siMYBL2: 5′-CCCU Methods GUCAGGUAUCAAAGA-3′ ( 24) . siCENPA-1: 5′-UAACACAUAUUUCUCUUGCCA-3′; siCENPA-2: Gene expression profile data 5′-AAUUUAACACAUAUUUCUCUU-3′. Microarray data [GSE4122, GSE18520 (14), GSE12470 (15), GSE38666 (16), GSE27651 (17) and GSE26712 (18)] were The qRT-PCR analysis downloaded from Gene Expression Omnibus (GEO). All of them meet the following criteria: (I) they all including TRIzol reagent (Thermo Fisher Scientific) was used to human ovarian cancer samples. (II) Containing normal extract the total RNA. Each experiment was performed for ovary samples. (III) Containing at least twenty samples. The three times. The primer sequences were, as follows: GAPDH R analysis process is borrowed from Liu et al. (19). The forward, 5′-CTGGGCTACACTGAGCACC-3′, GAPDH study was conducted in accordance with the Declaration of reverse, 5′-AAGTGGTCGTTGAGGGCAATG-3′; MYBL2 Helsinki (as revised in 2013). forward, 5′-CTTGAGCGAGTCCAAAGACTG-3′, MYBL2

Table 1 The details of the samples in the 6 data sets from the GEO datasets Number of samples Dataset Reference Platform (control/tumor)

GSE4122 Tate DL, Mostafavi ZB, Mostafavi MT GPL201 [HG-Focus] Affymetrix Human HG-Focus Target Array 67 (14/34)

GSE12470 Yoshihara K, Tajima A, Komata D, GPL887 Agilent-012097 Human 1A Microarray (V2) 53 (10/42) Yamamoto T, et al. G4110B (Feature Number version)

GSE18520 Mok SC, Bonome T, Vathipadiekal V, GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 63 (10/53) Bell A, et al. Plus 2.0 Array

GSE26712 Bonome T, Levine DA, Shih J, GPL96[HG-U133A] Affymetrix Human Genome U133A Array 195 (10/185) Randonovich M, et al.

GSE38666 Lili LN, Matyunina LV, Walker LD, GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 45 (20/25) Benigno BB, et al. Plus 2.0 Array

GSE27651 King ER, Tung CS, Tsang YT, Zu Z, GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 36 (6/30) et al. Plus 2.0 Array

reverse, 5′-AGTTGGTCAGAAGACTTCCCT-3′. CENPA 8Assay Kit (Dojindo, Kumamoto, Japan). forward, 5′-TTCCTCCCATCAACACAGTCG-3′; CENPA The EDU Assay Kit (GeneCopoeia, Rockville, MD, reverse, 5′-CACACCACGAGTGAATTTAACAC-3′. USA) was used to evaluate the proliferation of ovarian cells, according to manufacturer’s instructions. Briefly, these cells were treated with EdU for five hours. Western blot

The total protein was extracted by RIPA buffer containing Statistical analysis PMSF (Beyotime, Jiangsu, China). Then, 40 μg of total protein was separated on 10% polyacrylamide gels SPSS Statistics was used for the analyses. All data are (Beyotime). Afterwards, After the 10% polyacrylamide gels expressed as mean ± standard deviation (SD). Unpaired (Beyotime) separation, the proteins were transferred onto Student’s t-tests were used to evaluate the differences. PVDF membranes and blocked by 5% BSA. Subsequently, P<0.05 was considered statistically significant. these PVDF membranes were incubated overnight. The antibodies are: mouse monoclonal anti-GAPDH (60004- 1-Ig; Proteintech, Wuhan, China), Rabbit anti-MYBL2 Results (18896-1-AP; Proteintech), and Rabbit anti-CENPA Identification of DEGs and functional enrichment analysis (#2186; CST, Danvers, MA, USA). The protein bands of DEGs were visualized using the ECL Western Blotting Substrate (Pierce, Rockford, IL, USA). The details of the samples in the 6 data sets from the GEO datasets were listed in Table 1. After the integrated analysis, 291 DEGs were obtained, including 158 down-regulated ChIP assay genes (available online: https://cdn.amegroups.cn/static/ The CHIP kit (Cell Signaling; Cat. #9005) was used. public/tcr-21-175-1.xls) and 133 up-regulated genes The sequences were, as follows: forward, 5′-TTAT (available online: https://cdn.amegroups.cn/static/public/ CTCAAAGCCCCGCCG-3′, reverse, 5′-TGACAAC tcr-21-175-2.xls). Top 20 down and up-regulated genes was CCCGTGTCGTATC-3′; showed in Figure 1A. Enrichment analysis of GO and KEGG pathways was used to elucide the potential biological functions of the Cell proliferation assay 291 DEGs. In the down-regulated group, they were Cells with siRNA-mediated knockdown were seeded in 96- significantly enriched in various biological binding well plates (6×103 per well). and was detected using a CCK- (Figure 1B). In the up-regulated group, they showed most

A 1.80 4.14 2.46 3.47 1.29 FOLR1 −2.18 −4.75 −2.21 −3.53 −2.28 ADH1B 1.74 3.90 3.01 2.33 1.43 CP −2.59 −3.27 −3.51 −4.17 −1.98 ABCA8 2.53 4.17 2.28 3.60 1.20 WFDC2 4 0.28 −3.99 −3.54 −4.43 −2.30 BCHE 1.69 5.02 2.25 1.85 1.31 S100A1 −1.36 −2.74 −1.62 −2.87 −1.14 CAV1 2.96 2.52 3.28 2.47 1.35 HMGA1 −1.40 −2.28 −2.21 −1.76 −1.27 PMP22 2.50 4.84 1.77 2.60 1.08 LCN2 −1.46 −2.26 −2.68 −4.18 −1.87 EFEMP1 1.12 3.71 1.80 1.86 1.66 GLDC 2 −1.50 −2.43 −1.50 −2.22 −1.01 KLF4 4.30 4.18 2.54 2.76 0.48 CLDN3 0.00 −4.94 −3.58 −2.37 −2.24 OGN 1.42 4.45 1.69 2.03 1.15 KLK8 −1.82 −2.20 −1.88 −2.93 −1.55 RNASE4 2.28 2.87 1.68 2.91 1.41 MUC1 0.01 −1.14 −3.32 −3.73 −2.84 BNC1 1.22 3.08 1.59 2.54 1.05 DEFB1 0 −0.19 −3.50 −3.39 −3.67 −1.53 NPY1R 3.54 3.26 3.19 3.59 1.03 SCGB2A1 −2.52 −3.54 −3.01 −2.30 −0.46 TCF21 1.15 3.47 1.58 1.49 1.24 KIF4A −1.10 −1.20 −3.15 −4.37 −3.26 ALDH1A2 1.00 4.21 1.57 1.93 1.02 PAX8 −1.88 −2.09 −2.79 −4.01 −1.34 FLRT2 1.47 4.74 1.77 1.27 1.31 BUB1 −2 −0.41 −3.20 −3.24 −2.97 −1.49 HBB 2.27 3.06 1.77 1.20 1.00 KIF20A −1.55 −2.07 −3.50 −1.65 −1.77 MEOX2 1.07 2.46 2.13 1.95 1.56 MAL −0.94 −2.38 −1.63 −1.39 −1.16 NAP1L2 2.82 4.64 2.30 1.87 0.81 NEK2 −0.73 −2.18 −2.12 −2.25 −1.48 GFPT2 1.06 3.49 1.40 2.12 0.99 UBE2C −4 −1.68 −1.92 −2.27 −3.00 −1.46 PDGFD 1.17 2.45 1.52 1.16 1.18 SLC2A1 −2.09 −2.22 −1.19 −2.70 −1.16 SNCAIP GSE4122 GSE12470 GSE18520 GSE26712 GSE38666 GSE4122 GSE12470 GSE18520 GSE26712 GSE38666

B Enrichment GO UP Top10 Enrichment GO DOWN Top10 Count Sulfur compound binding Microtubule binding Count 4 3 5 Serine-type endopeptidase activity 4 Peptidase regulator activity 5 6 Protein kinase regulator activity 6 7 7 Glycosaminoglycan binding Serine -type peptidase activity 8 8 9 Hydrolase activity acting on acid phosphorus nitrogen bonds 9 Heparin binding 10 Serine hydrolase activity P.adjust 0.002 Haptoglobin binding P.adjust Kinase regulator activity 0.004 0.002 CDKs regulator activity 0.006 Oxygen carrier activity 0.008 0.004 Microtubule motor activity Retinol dehydrogenase activity 0.006 Cadherin binding involved in cell-cell adhesion 0.03 0.04 0.05 0.06 0.07 0.03 0.04 0.05 0.06 GeneRatio GeneRatio

C Up-regulated DEGs Down-regulated DEGs

Count Metabolic pathways 9 12 Cell cycle 15 P.adjust 18 21 0.01 0.02 0.03 Pathways in cancer P.adjust 0.04 0.2 0.05 0.4 Human T-cell leukemia virus 1 infection Count 0.6 9 P|3K-Akt signaling pathway 0.8 10 11 12 0.15 0.16 0.17 0.18 0.19 0.10 0.15 0.20 0.25 GeneRatio GeneRatio

Figure 1 Identification of DEGs and Functional enrichment analysis of the DEGs. (A) Heat maps of the top 20 up-regulated and down- regulated DEGs in the integrated microarray analysis. (B) Top 10 up-regulated and down-regulated GO enrichment analysis of the DEGs. (C) KEGG pathway enrichment analysis of the DEGs.

significantly enriched in microtubule binding. In addition, analysis, we know that the up-regulated DEGs are mainly they showed a close correlation with biological activity, related to the cell cycle, and the centromere protein had a such as serine-type endopeptidase activity, protein kinase very significant influence on cell cycle. Therefore, CENPF, regulator activity, serine-type peptidase activity and so on CENPA and CEP55 proteins were chose for further (Figure 1B). The results of KEGG pathway enrichment research. We further studied the expression of these genes analysis showed that the up-regulated genes were enriched in the TCGA database. The GEPIA dataset (http://gepia. in the cell cycle (Figure 1C). In addition, down-regulated cancer-pku.cn/) was used to verified the expression patterns genes are mainly involved in metabolic pathways, cancer of the proteins in normal tissues and ovarian tissues. The pathways, and PI3K-Akt signaling pathways (Figure 1C). mRNA expression levels of CENPF, CEP55 and CENPA in cancer tissues were higher than those in normal tissues (Figure 2A,B,C). The survival analysis was performed in Expression characteristics of CENPF, CENPA and CEP55 the web (http://kmplot.com/analysis/index.php). Ovarian in human ovarian tissues cancer patients with high expression of CENPA, CENPF According to the results of GO enrichment and KEGG or CEP55 had a lower overall survival rate than those with

A CENPA B CENPF C CEP55 * * * 6

6 5 6

4 4 4 3

2 2 2

1 Log2(Transcripts Per Million (TPM)) Log2(Transcripts Log2(Transcripts Per Million (TPM)) Log2(Transcripts 0 Per Million (TPM)) Log2(Transcripts 0 0 OV OV OV [num(T)=426; num(N)=88] [num(T)=426; num(N)=88] [num(T)=426; num(N)=88]

D CENPA (204962_s_at) E CENPF (207828_s_at) 1.0 1.0 HR =1.15 (1.01−1.31) HR =1.25 (1.1−1.42) logrank P=0.029 logrank P=0.00056 0.8 0.8

0.6 0.6

0.4 Probability 0.4 Probability

0.2 Expression 0.2 Expression Low Low 0.0 High 0.0 High 0 50 100 150 200 250 0 50 100 150 200 250 Time (months) Time (months) Number at risk Number at risk Low 543 84 22 4 0 0 Low 813 105 25 5 1 0 High 892 88 13 4 1 0 High 622 67 10 3 0 0

F CEP55 (218542_s_at) 1.0 HR =1.21 (1.05−1.39) logrank P=0.0072 0.8

0.6

0.4 Probability

0.2 Expression Low 0.0 High 0 50 100 150 200 250 Time (months) Number at risk Low 437 69 15 3 0 0 High 998 103 20 5 1 0

Figure 2 The expression pattern and prognostic value of CENPF, CENPA and CEP55 in ovarian cancer. (A) CENPA is more highly expressed in ovarian cancer tissues than in normal tissues (*P<0.05). (B) CENPF is more highly expressed in ovarian cancer tissues than in normal tissues (*P<0.05). (C) CEP55 is more highly expressed in ovarian cancer tissues than in normal tissues (*P<0.05). (D) The Kaplan- Meier analysis shows that ovarian cancer patients with a high expression of CENPA have the worse overall survival. (E) The Kaplan-Meier analysis shows that ovarian cancer patients with a high expression of CENPF have the worse overall survival. (F) The Kaplan-Meier analysis shows that ovarian cancer patients with a high expression of CEP55 have the worse overall survival.

© Translational Cancer Research. All rights reserved. Transl Cancer Res 2021;10(9):4076-4086 | https://dx.doi.org/10.21037/tcr-21-175 Translational Cancer Research, Vol 10, No 9 September 2021 4081 low expression (Figure 2D,E,F). qRT-PCR results also revealed the same trend (P<0.01, Figure 5C). These data indicate that MYBL2 binds to the CENPA promoter region and promotes the CENPA The inhibition of CENPA inhibits the proliferation of expression. ovarian cells

Previous experiments have demonstrated that ovarian Discussion cancer patients with a high expression of CENPA have poor prognosis. In order to verify the role of CENPA in ovarian Unlimited cell proliferation, metastatic, and drug resistance cancer, its expression was knocked down in SKOV3 and are all the characteristics of cancer cells that can lead to a A2780 cell lines using two different small RNA sequences malignant phenotype and affect prognosis. The inhibition (siCENPA-1 and siCENPA-2). Compared with levels in the of cell proliferation has become an important strategy for control group, the CENPA mRNA and protein levels were cancer treatment. MYBL2 is an important regulator of cell significantly lower in these two cell lines Figure( 3A,B). The cycle. When it is dysregulated in cancer cells, abnormal CCK-8 results revealed that the decreased expression of proliferation occurs. In fact, MYBL2 is overexpressed in CENPA significantly inhibits the proliferation of ovarian a variety of cancers and is associated with poor prognosis. cell lines (Figure 3C). Similar results were obtained in the Thus, Some investigators have used MYBL2 as a biomarker EDU assay (Figure 3D). These results indicate that CENPA for prognosis and as a potential therapeutic target (6). For greatly promotes the proliferation of ovarian cells. example, the high expression levels of CCNE1, KRT16 and MYBL2 are associated with worse relapse-free survival and overall survival (OS) in ER-negative/HER2-negative The expression of CENPA was regulated by MYBL2 in breast cancer (26). In patients with primary hepatocellular ovarian cancer cells carcinoma, high expression of MYBL2 is associated In order to study the regulatory mechanism of CENPA, with low OS (27). in human hepatocellular carcinoma, we further studied CENPA in the TCGA database. We high expression of MYBL2 is associated with faster found that CENPA showed high consistency with MYBL2 progression (28). MYBL2 can be used as a target for cancer (Figure 4A). At the same time, mRNA levels of MYBL2 therapy, according to some studies. Ginkgetin regulates were higher in cancer tissues compared to normal tissues b-Myb by modulating miR-34a, and may be used to induce (Figure 4B). Ovarian cancer patients with high MYBL2 G2 arrest for the treatment of colorectal cancer (29). expression showed worse overall survival (Figure 4C). The An important sign of cancer is the unlimited proliferation level of CENPA decreases with the knock down of MYBL2 of cancer cells (30), and this is mainly caused by the disruption in SKOVE cell line (Figure 4D). in cell cycle regulation (31). The overexpression of MYBL2 is associated with the increase in cell proliferation, and plays an important role in cancer progression (32). CENPA was directly regulated by MYBL2 However, the molecular mechanism by which mybl2 In order to determine whether MYBL2 directly regulates promotes cell proliferation in lung cancer remains unclear. the CENPA expression in ovarian cancer cells, ChIP assay A previous study revealed that MYBL2 binds to the A3B was performed. The Cistrome database (http://cistrome. promoter, causing the transactivation in breast cancer org/db/#/) was used to predict the binding ability. The cells (33). In addition, in the present study, it was found results of the ChIP-seq from the Gertz et al. (25) revealed that MYBL2 promotes ovarian cell proliferation by directly that MYBL2 can bind to the CENPA promotor in liver regulating the expression of CENPA. This pathway further cells (Figure 5A). In order to verify the direct binding in contributes to the pathogenesis and progression of ovarian ovarian cancer cells, the SKOV3 cell line was used for the cancer, providing a basis for treatment. ChIP assays. These results show that MYBL2 binds to CENPA is a fundamental component of the human the promoter region of CENPA (Figure 5B). In order to centromere and plays an important role in cell cycle, further confirm this result, the binding in the NC-SKOV3 survival and genetic stability. CENPA expression is altered and siMYBL2-SKOV3 cell lines was examined, and it was in various types of human cancers. In epithelial ovarian found that the binding of MYBL2 to the CENPA promoter cancer, high levels of CENPA are significantly correlated significantly decreased (Figure 5B). Furthermore, the with poor survival (34). In osteosarcoma, the expression

A SKOV3 A2780 1.5 1.5

1.0 1.0

0.5 0.5 *** *** ***

*** Relative mRNA level Relative mRNA level 0.0 0.0 NC siCENPA-1 siCENPA-2 NC siCENPA-1 siCENPA-2 B SKOV3 A2780 CENPA CENPA

GAPDH GAPDH

NC NC

siCENPA-1 siCENPA-2 C SKOV3 A2780 siCENPA-1 siCENPA-2 2.5 1.5 siCENPA-2 siCENPA-2 siCENPA-1 2.0 siCENPA-1 NC NC 1.0 1.5 ** ** ** 1.0 ** 0.5

OD Value (450nm) OD Value 0.5 OD Value (450nm) OD Value

0.0 0.0 1 2 3 1 2 3 Time (day) Time (day) D NC-SKOV3 siCENPA-1-SKOV3 siCENPA-2-SKOV3

400 500 600 400 300 30.8 400 300 22.1 23.3 200 200 200 100 100 0 2 3 4 5 6 0 0 10 10 10 10 10 102 103 104 105 106 102 103 104 105 106 EDU-APC EDU-APC EDU-APC

NC-A2780 siCENPA-1-A2780 siCENPA-2-A2780 600 600 600

400 400 400 25.7 17.8 19.3

200 200 200

0 2 3 4 5 6 0 0 10 10 10 10 10 102 103 104 105 106 102 103 104 105 106 EDU-APC EDU-APC EDU-APC

Figure 3 Knockdown of CENPA inhibits the proliferation of ovarian cells. (A) The qRT-PCR analysis of CENPA expression levels in the CENPA knockdown of SKOV3 and A2780 ovarian cell lines (***P<0.001, independent t-test). (B) The western blot analysis of CENPA expression levels in the CENPA knockdown of SKOV3 and A2780 ovarian cell lines. (C) The CCK-8 assays in the CENPA knockdown of SKOV3 and A2780 ovarian cell lines (**P<0.01, independent t-test). (D) The EDU assays in the CENPA knockdown SKOV3 and A2780 ovarian cell lines.

MYBL2

A B 8 * P (two-tailed) <0.0001 r=0.7719 22 6

4 18

16 2 MYBL2 mRNA expression

14 12 14 16 18 20 1 CENPA mRNA expression T (426) N (88)

MYBL2 D SKOV3 C 1.0 HR =1.27 logrank P=0.00065 0.8 MYBL2

Low (546) 0.6 CENPA High (1110) 0.4 Probability GAPDH 0.2

NC 0.0 siMYBL2 0 50 100 150 200 250 Time (months)

Figure 4 The expression of CENPA was regulated by MYBL2 in ovarian cancer. (A) CENPA expression showed a very high correlation with MYBL2 expression in ovarian cancer tissues (data from TCGA). (B) MYBL2 is more highly expressed in ovarian cancer tissues than in normal tissues (*P<0.05). (C) The Kaplan-Meier analysis shows that ovarian cancer patients with a high expression of MYBL2 have the worse overall survival. (D) The level of CENPA decreases with the knock down of MYBL2 in SKOVE cell line.

of CENPA is significantly associated with progression, Acknowledgments and patients with a high expression of CENPA have Funding: This work was supported by grants from The poor overall and recurrence-free survival (35). In triple- Key scientific research projects of Xinqiao Hospital, Army negative breast cancer, BIRC5, CENPA and FAM64A Medical University, PLA (No. 2016YLC07). are specifically upregulated, and the high levels of these three genes are associated with poor prognosis, suggesting the clinical application as therapeutic targets (36). In lung Footnote adenocarcinoma, CENPA upregulation predicts a poor OS in patients, and CENPA downregulation may attenuate Reporting Checklist: The authors have completed the MDAR the aggressive phenotype of lung adenocarcinoma cells checklist. Available at https://dx.doi.org/10.21037/tcr-21- (37,38). In glioblastoma, CENPA is consistently expressed 175 in GSC cultures, but is not expressed in NSC cultures, suggesting that this should be further explored as therapeutic Conflicts of Interest: All authors have completed the ICMJE targets (39). In hepatocellular carcinoma, CENPA is strongly uniform disclosure form (available at https://dx.doi. associated with hepatocellular carcinoma cell growth and org/10.21037/tcr-21-175). The authors have no conflicts of division (40). interest to declare.

A Chr2:

46241_treat.bw

CENPA CENPA

100- Layered H3k27Ac 0-

B NC-MYBL2 siMYBL2

NC siMYBL2 bp 10 1000 8 500 6

250 4 2

Fold enrichment of lgG 0 LgG MYBL2 Input LgG Input LgG Maker

Anti-MYBL2 Anti-MYBL2 Anti-Histone H3 Anti-Histone H3

Figure 5 MYBL2 directly regulates the expression of CENPA in SKOV3 cell line. (A) Prediction of the binding ability of MYBL2 to the CENPA promoter region using Cistrome DB. (B) The ChIP assay results for the direct binding of MYBL2 to the promoter of CENPA. (C) The qRT-PCR analysis of the ChIP assay results.

Ethical Statement: The authors are accountable for all License (CC BY-NC-ND 4.0), which permits the non- aspects of the work in ensuring that questions related commercial replication and distribution of the article with to the accuracy or integrity of any part of the work are the strict proviso that no changes or edits are made and the appropriately investigated and resolved. The study was original work is properly cited (including links to both the conducted in accordance with the Declaration of Helsinki (as formal publication through the relevant DOI and the license). revised in 2013). Institutional ethical approval and informed See: https://creativecommons.org/licenses/by-nc-nd/4.0/. consent were waived.

References Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons 1. Bray F, Ferlay J, Soerjomataram I, et al. Global cancer Attribution-NonCommercial-NoDerivs 4.0 International statistics 2018: GLOBOCAN estimates of incidence and

mortality worldwide for 36 cancers in 185 countries. CA 15. Yoshihara K, Tajima A, Komata D, et al. Gene Cancer J Clin 2018;68:394-424. expression profiling of advanced-stage serous ovarian 2. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. cancers distinguishes novel subclasses and implicates CA Cancer J Clin 2019;69:7-34. ZEB2 in tumor progression and prognosis. Cancer Sci 3. Vang R, Shih IeM, Kurman RJ. Ovarian low-grade 2009;100:1421-8. and high-grade serous carcinoma: pathogenesis, 16. Lili LN, Matyunina LV, Walker LD, et al. Molecular clinicopathologic and molecular biologic features, and profiling predicts the existence of two functionally diagnostic problems. Adv Anat Pathol 2009;16:267-82. distinct classes of ovarian cancer stroma. Biomed Res Int 4. Coleman RL, Monk BJ, Sood AK, et al. Latest research 2013;2013:846387. and treatment of advanced-stage epithelial ovarian cancer. 17. King ER, Tung CS, Tsang YT, et al. The anterior gradient Nat Rev Clin Oncol 2013;10:211-24. homolog 3 (AGR3) gene is associated with differentiation 5. Roussel M, Saule S, Lagrou C, et al. Three new types of and survival in ovarian cancer. Am J Surg Pathol viral oncogene of cellular origin specific for haematopoietic 2011;35:904-12. cell transformation. Nature 1979;281:452-5. 18. Bonome T, Levine DA, Shih J, et al. A gene signature 6. Musa J, Aynaud MM, Mirabeau O, et al. MYBL2 (B-Myb): predicting for survival in suboptimally debulked patients a central regulator of cell proliferation, cell survival and with ovarian cancer. Cancer Res 2008;68:5478-86. differentiation involved in tumorigenesis. Cell Death Dis 19. Liu X, Wu J, Zhang D, et al. Identification of Potential 2017;8:e2895. Key Genes Associated With the Pathogenesis and 7. Zhang K, Fu G, Pan G, et al. Demethylzeylasteral inhibits Prognosis of Gastric Cancer Based on Integrated glioma growth by regulating the miR-30e-5p/MYBL2 axis. Bioinformatics Analysis. Front Genet 2018;9:265. Cell Death Dis 2018;9:1035. 20. Ritchie ME, Phipson B, Wu D, et al. limma powers 8. Zhang X, Lv QL, Huang YT, et al. Akt/FoxM1 signaling differential expression analyses for RNA-sequencing and pathway-mediated upregulation of MYBL2 promotes microarray studies. Nucleic Acids Res 2015;43:e47. progression of human glioma. J Exp Clin Cancer Res 21. Kolde R, Laur S, Adler P, et al. Robust rank aggregation 2017;36:105. for gene list integration and meta-analysis. Bioinformatics 9. Fan X, Wang Y, Jiang T, et al. B-Myb Mediates 2012;28:573-80. Proliferation and Migration of Non-Small-Cell 22. Huang da W, Sherman BT, Lempicki RA. Systematic Lung Cancer via Suppressing IGFBP3. Int J Mol Sci and integrative analysis of large gene lists using DAVID 2018;19:1479. bioinformatics resources. Nat Protoc 2009;4:44-57. 10. Geng GJ, Yang YT, Jiang J, et al. MicroRNA-30a 23. Yu G, Wang LG, Han Y, et al. clusterProfiler: an R suppresses non-small-cell lung cancer by targeting Myb- package for comparing biological themes among gene related protein B. Exp Ther Med 2018;15:1633-9. clusters. OMICS 2012;16:284-7. 11. Chen J, Chen X. MYBL2 Is Targeted by miR-143-3p and 24. Hasson SA, Kane LA, Yamano K, et al. High-content Regulates Breast Cancer Cell Proliferation and Apoptosis. genome-wide RNAi screens identify regulators of parkin Oncol Res 2018;26:913-22. upstream of mitophagy. Nature 2013;504:291-5. 12. Frau M, Ladu S, Calvisi DF, et al. Mybl2 expression is 25. Gertz J, Savic D, Varley KE, et al. Distinct properties of under genetic control and contributes to determine a cell-type-specific and shared transcription factor binding hepatocellular carcinoma susceptible phenotype. J Hepatol sites. Mol Cell 2013;52:25-36. 2011;55:111-9. 26. Shao N, Yuan K, Zhang Y, et al. Identification of key 13. Müller-Tidow C, Wang W, Idos GE, et al. Cyclin candidate genes, pathways and related prognostic A1 directly interacts with B-myb and cyclin A1/cdk2 values in ER-negative/HER2-negative breast cancer by phosphorylate B-myb at functionally important serine and bioinformatics analysis. J BUON 2018;23:891-901. threonine residues: tissue-specific regulation of B-myb 27. Guan Z, Cheng W, Huang D, et al. High MYBL2 function. Blood 2001;97:2091-7. expression and transcription regulatory activity is 14. Mok SC, Bonome T, Vathipadiekal V, et al. A gene associated with poor overall survival in patients with signature predictive for outcome in advanced ovarian hepatocellular carcinoma. Curr Res Transl Med cancer identifies a survival factor: microfibril-associated 2018;66:27-32. glycoprotein 2. Cancer Cell 2009;16:521-32. 28. Calvisi DF, Simile MM, Ladu S, et al. Activation of

v-Myb avian myeloblastosis viral oncogene homolog- ovarian cancer. Tumour Biol 2013;34:2971-5. like2 (MYBL2)-LIN9 complex contributes to human 35. Gu XM, Fu J, Feng XJ, et al. Expression and prognostic hepatocarcinogenesis and identifies a subset of relevance of centromere protein A in primary hepatocellular carcinoma with mutant p53. Hepatology osteosarcoma. Pathol Res Pract 2014;210:228-33. 2011;53:1226-36. 36. Zhang C, Han Y, Huang H, et al. Integrated analysis 29. Lee YJ, Kang YR, Lee SY, et al. Ginkgetin induces G2- of expression profiling data identifies three genes in phase arrest in HCT116 colon cancer cells through the correlation with poor prognosis of triple-negative breast modulation of b‑Myb and miRNA34a expression. Int J cancer. Int J Oncol 2014;44:2025-33. Oncol 2017;51:1331-42. 37. Wu Q, Chen YF, Fu J, et al. Short hairpin RNA-mediated 30. Hainaut P, Plymoth A. Targeting the hallmarks of cancer: down-regulation of CENP-A attenuates the aggressive towards a rational approach to next-generation cancer phenotype of lung adenocarcinoma cells. Cell Oncol therapy. Curr Opin Oncol 2013;25:50-1. (Dordr) 2014;37:399-407. 31. Dominguez-Brauer C, Thu KL, Mason JM, et al. 38. Liu WT, Wang Y, Zhang J, et al. A novel strategy of Targeting Mitosis in Cancer: Emerging Strategies. Mol integrated microarray analysis identifies CENPA, CDK1 Cell 2015;60:524-36. and CDC20 as a cluster of diagnostic biomarkers in lung 32. Iness AN, Felthousen J, Ananthapadmanabhan V, et al. adenocarcinoma. Cancer Lett 2018;425:43-53. The cell cycle regulatory DREAM complex is disrupted 39. Stangeland B, Mughal AA, Grieg Z, et al. Combined by high expression of oncogenic B-Myb. Oncogene expressional analysis, bioinformatics and targeted 2019;38:1080-92. proteomics identify new potential therapeutic targets in 33. Chou WC, Chen WT, Hsiung CN, et al. B-Myb Induces glioblastoma stem cells. Oncotarget 2015;6:26192-215. APOBEC3B Expression Leading to Somatic Mutation in 40. Li B, Pu K, Wu X. Identifying novel biomarkers in Multiple Cancers. Sci Rep 2017;7:44089. hepatocellular carcinoma by weighted gene co-expression 34. Qiu JJ, Guo JJ, Lv TJ, et al. Prognostic value of network analysis. J Cell Biochem 2019. [Epub ahead of centromere protein-A expression in patients with epithelial print]. doi: 10.1002/jcb.28420.

Cite this article as: Han J, Xie R, Yang Y, Chen D, Liu L, Wu J, Li S. CENPA is one of the potential key genes associated with the proliferation and prognosis of ovarian cancer based on integrated bioinformatics analysis and regulated by MYBL2. Transl Cancer Res 2021;10(9):4076-4086. doi: 10.21037/tcr-21-175