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| ORIGINAL ARTICLE |
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| Year : 2023 | Volume
: 66
| Issue : 4 | Page : 266-275 |
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E2F8 knockdown suppresses cell proliferation and induces cell cycle arrest via Wnt/β-Catenin pathway in ovarian cancer
Meiyin Zhang, Ye Xu, Yongjian Zhang, Ge Lou
Department of Gynecology, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China
| Date of Submission | 27-Nov-2022 |
| Date of Decision | 01-Feb-2023 |
| Date of Acceptance | 20-Mar-2023 |
| Date of Web Publication | 15-Jun-2023 |
Correspondence Address:
Dr. Ge Lou
Department of Gynecology, Harbin Medical University Cancer Hospital, No. 150, Haping Road, Nangang, Harbin, Heilongjiang Province
China
 Source of Support: None, Conflict of Interest: None
DOI: 10.4103/cjop.CJOP-D-22-00142
Ovarian cancer is one of the leading causes of death in female reproductive system cancers. However, the pathogenesis of ovarian cancer remains elusive. Our aim is to investigate the potential targets for ovarian cancer. Two microarray datasets were obtained from the Gene Expression Omnibus public database. Using R package limma, the differentially expressed genes (DEGs) were identified from the datasets. There were 95 overlapping DEGs in two microarray datasets. GO, KEGG pathway analysis, and protein-protein interaction (PPI) network analysis were carried out based on the DEGs. Wnt signaling pathway and cell cycle were enriched in the KEGG pathway analysis. Moreover, the top 10 hub genes with the most nodes were determined by PPI network analysis. E2F8, one of hub genes was positively linked to a bad outcome in ovarian cancer patients. Furthermore, E2F8 knockdown suppressed cell proliferation and induced cell cycle arrest in ovarian cancer. In addition, we found that silencing E2F8 inhibited the Wnt/β-catenin signaling pathway. In ovarian cancer cells with E2F8 knockdown, overexpressing β-catenin restored both the suppressed capacity of cell proliferation and cell cycle progression. Therefore, our results revealed that E2F8 had an involvement in the development of ovarian cancer which might act as a therapeutic target.
Keywords: Bioinformatic analysis, cell cycle, E2F8, ovarian cancer, Wnt/β-catenin pathway
How to cite this article:
Zhang M, Xu Y, Zhang Y, Lou G. E2F8 knockdown suppresses cell proliferation and induces cell cycle arrest via Wnt/β-Catenin pathway in ovarian cancer. Chin J Physiol 2023;66:266-75 |
How to cite this URL:
Zhang M, Xu Y, Zhang Y, Lou G. E2F8 knockdown suppresses cell proliferation and induces cell cycle arrest via Wnt/β-Catenin pathway in ovarian cancer. Chin J Physiol [serial online] 2023 [cited 2023 Dec 4];66:266-75. Available from: https://www.cjphysiology.org/text.asp?2023/66/4/266/378759 |
| Introduction | |  |
Ovarian cancer is one of the leading causes of death in female reproductive system tumors, which has a high mortality rate, insidious onset, low early diagnosis, and poor prognosis.[1],[2],[3] The overall survival of ovarian cancer patients has only improved slightly, despite improvements in cytoreductive radical surgery and cytotoxic chemotherapy.[4],[5],[6] The etiology of ovarian cancer is still unclear. Increasing research found that the occurrence and development of ovarian cancer were highly correlated with gene mutation, and its high mortality was caused by multiple factors, but metastasis and recurrence were the main reasons.[3],[7] Thus, it is crucial to identify the potential molecular biomarkers of poor prognosis of ovarian cancer and explore the underlying mechanisms, which may improve the quality of life of ovarian cancer patients, prolong their survival time and improve their prognosis.
E2F is a group of genes encoding transcription factors of eukaryotes, which are widely expressed in various tissues and organs. E2F family consists of eight members: E2F1 to E2F8. The molecular functions of E2Fs are implicated in cellular proliferation, differentiation, death, DNA repair, and cell cycle.[8],[9],[10] Because it is unclear how E2Fs maintain normal animal physiology, numerous studies have demonstrated that E2F activity in cells contributes to the normal maintenance of cellular homeostasis and prevention of cancer by regulating multiple genes affecting cell proliferation and genomic integrity.[8],[9],[10],[11] Recently, E2Fs have been identified as transcriptional activators or repressors.[11],[12] Moreover, E2Fs have emerged as major transcriptional regulators of cell cycle-dependent gene expression.[13] Abnormal expression or increased activation of E2Fs have been reported in several human malignant tumors.[14],[15],[16],[17],[18] Our previous research found that the circular RNA targeting E2F2-regulated cell proliferation, metastasis, and glucose metabolism in ovarian cancer.[19] According to these research, E2F has the potential to be a useful biomarker for predicting the prognosis of tumors. Therefore, identifying the molecular mechanism of E2F-mediated oncogene or tumor suppressor as a predictive biomarker may provide a new therapeutic strategy.
The various tissue of origin, distinct biology, and different gene expression patterns of human malignancies all illustrate the complexity of human diseases, particularly malignancies.[20],[21],[22] Thus, profiling gene expression patterns of cancers might offer a new foundation for the tumorigenesis and progression of malignancies. Bioinformatic analysis is an efficient and comprehensive tool to analyze a number of gene expression profiles of different disease samples which help to screen the potential targets for the development or prognosis of diseases.[23],[24],[25]
Herein, we applied a variety of bioinformatic analysis approaches to screening the potential gene markers in ovarian cancer. We identified E2F8 as a candidate gene target, which was linked to a bad outcome in ovarian cancer patients. Furthermore, we uncovered that suppressing E2F8 inhibited cell growth and induced cell cycle arrest through the Wnt/β-catenin pathway inactivation in ovarian cancer. Thereby, these results implied that E2F8 had an involvement in the development of ovarian cancer.
| Materials and Methods | | |
Microarray acquirement
Two ovarian cancer microarray datasets (GSE36668 and GSE54388) were included in this study. The series matrix TXT files and platform TXT files were downloaded from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). Raw data of two datasets were preprocessed using R software (Bioconductor, Roswell Park Cancer Institute, Buffalo, NY, USA). The log2 transformation was performed on all gene expression data. The number of samples used and detailed information of two microarray datasets are included in [Table 1]. | Table 1: Detail information of three gastric cancer gene chips downloaded from the gene expression omnibus database
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Differentially expressed genes identification
Using the R package limma, we performed the differential analysis in two microarray datasets. The differentially expressed genes (DEGs) were considered as those with an adjusted P < 0.05 and a |log2 fold-change| more than 2.0. Volcano plots and heatmap were drawn by R language. And then, R package VennDiagram was utilized to determine the overlapping DEGs.
GO and KEGG pathway analysis
Based on the overlapping DEGs, GO functional analysis and KEGG pathway analysis were carried out to identify the potential functions and pathways by Enrichr (https://maayanlab.cloud/Enrichr/). The top 10 pathways of GO items and the top 10 KEGG pathways were visualized.
Network analysis of protein-protein interaction
Based on the overlapping DEGs, the protein-protein interaction (PPI) network was constructed by the STRING (https://string-db.org/) database. The Cytoscape software (http://www.cytoscape.org/) was used for visualizing the PPI network. The cytoHubba, a plugin in Cytoscape software was used to select the top 10 hub genes via maximal clique centrality method. Moreover, the genes with the top 10 maximal clique centrality score in the PPI network were selected as hub genes for further study.
The verification of hub gene expression
Gene Expression Profiling Interactive Analysis (GEPIA) (http://gepia.cancer-pku.cn/) was utilized to analyze the expression level of hub genes between tumor samples and normal samples as previously described.[26]
Survival analysis of hub genes
Kaplan–Meier Plotter (https://kmplot.com/analysis/) consists of a lot of gene expression data of tumor samples and clinical data of cancer patients. Kaplan–Meier Plotter was performed to assess the effect of the top 10 hub genes on overall survival time. The P value of log rank and hazard ratio of 95% confidence interval were displayed in the graph, and the log rank P < 0.05 was defined as statistically difference.
Cell culture and transfection
Human ovarian cancer cell lines (A2780 and OVCAR3) were obtained from American Type Culture Collection (Manassas, VA, USA) and were maintained in RPMI 1640 (Thermo Fisher Scientific, Waltham, MA, USA) containing 10% fetal bovine serum (Thermo Fisher Scientific) and 100 U/ml of penicillin/streptomycin (Thermo Fisher Scientific) in a humidified incubator.
A2780 and OVCAR3 cells were transfected with E2F8-short interference RNA (siRNA), negative control (NC)-siRNA, β-catenin plasmid vector (Thermo Fisher Scientific), and control vectors as previously described.[27] When cells were at 60%–80% confluence, they were incubated with Lipofectamine transfection reagent (Thermo Fisher Scientific) for 12 h as directed by the manufacturer. After transfection, the capacity of cell proliferation and cell cycle were assayed.
Western blotting
Cells were collected and lysed in radioimmunoprecipitation assay buffer with protease and phosphatase inhibitors. The concentrations of supernatant were detected by bicinchonic acid protein detection kit (Servicebio, Wuhan, China). Protein was loaded, separated by 10% sodium dodecyl-sulfate polyacrylamide gel electrophoresis, and then transferred to polyvinylidene fluoride (PVDF) membranes in equal amounts. The blots were then treated with antibodies after blocking. Second antibodies (Thermo Fisher Scientific) were added for 1-h incubation. Following that, enhanced chemiluminescence (ECL) was applied to develop the blots. The details of the antibodies used in western blotting experiment is provided in [Table 2].
Cell counting kit-8 assay
The cell viability was measured using cell counting kit-8 (CCK-8) assay as instructed by the manufacturer. After the cells were digested with trypsin, A2780 and OVCAR3 cells were seeded into 96-well plates and then 10 μL CCK-8 solution was added for 2-h incubation. At a wavelength of 450 nm, optical density values were determined using a microplate reader (Bio-Rad, Richmond, VA, USA).
Colony formation assay
A2780 and OVCAR3 cells were inoculated into 12-well plates after being digested with trypsin and then cultured for 14 days. Subsequently, cells were fixed with methanol, and stained with crystal violet for 10 min. Under the inverted microscope, cell colonies were photographed, counted, and analyzed after being stained with crystal violet.
Cell cycle analysis
After growing the logarithmic growth phase, cells were fixed with 70% cold ethanol overnight and then stained with 500 μl prepared propidium iodide staining solution at room temperature for 30-min incubation. Then, cells were enumerated and data analyzed using FlowJo software.
Statistical analysis
In this study, all data analysis and visualization were carried out using GraphPad Prism software (version 8.0, San Diego, CA, USA). The data are presented as the mean ± standard deviation. A one-way analysis of variance or Student's t-test was applied to determine the statistical difference. P <0.05 was considered statistically significant.
| Results | | |
Analysis of differentially expressed genes in ovarian cancer expression profiles
There were two microarray datasets included in this study, involving 24 ovarian cancer samples and 10 control samples [Table 1]. The DEGs of two microarrays were screened out by limma package (P < 0.05, |log2 fold-change| >2). There were 677 differential genes in the GSE36668 dataset, where 454 were upregulated and 223 were downregulated. GSE54388 dataset showed 238 differential genes, which consists of 87 upregulated DEGs and 151 downregulated DEGs. Volcano plots exhibited the DEGs between ovarian cancer tissues and normal tissues of two datasets [Figure 1]a and [Figure 1]b. Heatmap showed the relative level of hub genes in two microarray datasets [Figure 1]c and [Figure 1]d, suggesting distinct gene expression profiles presented in cancer and normal tissues. Then, Veen diagram indicated 95 overlapping DEGs in two microarrays [Figure 1]e. | Figure 1: Analysis of DEGs in ovarian cancer expression profiles. (a) Volcano plots showed the DEGs between ovarian cancer tissues and normal tissues in the GSE36668 dataset. (b) Volcano plots showed the DEGs between ovarian cancer tissues and normal tissues in the GSE54388 dataset. (c) Heatmap showed the DEGs between ovarian cancer tissues and normal tissues in the GSE36668 dataset. (d) Heatmap showed the DEGs between ovarian cancer tissues and normal tissues in the GSE54388 dataset. (e) Veen diagram showed the overlapped DEGs among two microarray datasets. DEGs: Differentially expressed genes.
Click here to view |
Pathway enrichment analysis and protein-protein interaction network establishment
Based on the overlapping DEGs, GO functional analysis and KEGG pathway enrichment analysis were carried out. Following that, the top 10 GO items in the biological process were illustrated [Figure 2]a. Microtubule cytoskeleton organization involved in mitosis, mitotic spindle organization, and midbody abscission were the top three enriched pathways in GO functional analysis. Furthermore, the top 10 KEGG pathways were visualized [Figure 2]b. The KEGG pathways were mainly associated with Wnt signaling pathway, ABC transporters, and cell cycle. | Figure 2: Pathway enrichment analysis and PPI network establishment. (a) Bar graph showed the top 10 GO items in biological process based on the overlapping DEGs. (b) Bar graph showed top 10 KEGG pathways based on the overlapping DEGs. (c) PPI network was constructed by STRING based on the overlapping DEGs. (d) Bar graph showed the top 10 hub genes with the highest number of nodes in the PPI network. PPI: Protein-protein interaction, DEGs: Differentially expressed genes.
Click here to view |
The PPI network was established according to the overlapping DEGs [Figure 2]c. Moreover, the top 10 hub genes that had the most nodes in the PPI network were illustrated [Figure 2]d. CEP55, FOXM1, TTK, BUB1B, CDC20, DLGAP5, DTL, E2F8, KIF11, and KIF20A were the top 10 hub genes.
The expression and prognosis significance of hub genes in ovarian cancer
To verify the expression levels of hub genes, the mRNA expression of CEP55, FOXM1, TTK, BUB1B, CDC20, DLGAP5, DTL, E2F8, KIF11, and KIF20A in ovarian cancer samples were downloaded from GEPIA web server. Further analysis showed that an increase in hub gene expression of ovarian cancer samples compared to normal tissues [Figure 3]a. | Figure 3: The expression and prognosis significance of hub genes in ovarian cancer. (a) Box diagrams showed the mRNA expression of CEP55, FOXM1, TTK, BUB1B, CDC20, DLGAP5, DTL, E2F8, KIF11, and KIF20A in ovarian cancer tissues and normal tissues derived from GEPIA tool. (b) Kaplan–Meier Plotter showed the survival rates of high CEP55, FOXM1, TTK, BUB1B, CDC20, DLGAP5, DTL, E2F8, KIF11, and KIF20A expression group and low CEP55, FOXM1, TTK, BUB1B, CDC20, DLGAP5, DTL, E2F8, KIF11, and KIF20A expression group. *P < 0.05 versus the normal tissue group. HR: Hazard ratio.
Click here to view |
To investigate the prognosis significance of hub genes in ovarian cancers, Kaplan–Meier Plotter was applied to assess the effects of these genes on overall survival time [Figure 3]b. We found that there was no difference in overall survival time of FOXM1, TTK, CDC20, and DTL. Inversely, the higher expression of CEP55, BUB1B, DLGAP5, E2F8, KIF11, and KIF20A in ovarian cancer tissues was linked to poor prognosis in ovarian cancer patients. Thereby, CEP55, BUB1B, DLGAP5, E2F8, KIF11, and KIF20A were identified as prognostic biomarkers of ovarian cancer.
E2F8 knockdown suppresses the proliferative capability of ovarian cancer cells
Recently, the transcription factor E2F family has multiple regulatory effects on cell growth, differentiation, apoptosis, and cell cycle.[8],[9],[10] In addition, our previous research found that the circular RNA targeting E2F2 regulated cell proliferation, metastasis, and glucose metabolism in ovarian cancer.[19] We then focus on the effect of E2F8 on ovarian cancer. A2780 and OVCAR3 cell lines were transfected with siRNA targeting E2F8. Western blotting indicated a reduction of the protein level of E2F8 in the si-E2F8 groups in relation to the control and si-NC groups [Figure 4]a and [Figure 4]b. CCK-8 assay showed that silencing E2F8 decreased the proliferative capability of both A2780 and OVCAR3 cells in the si-E2F8 groups compared to the control and si-NC groups [Figure 4]c. Colony formation assay revealed the reduced number of stained cell colonies in the si-E2F8 groups in contrast to the control and si-NC groups [Figure 4]d and [Figure 4]e. Thus, these data indicated that E2F8 knockdown suppressed the proliferative capability of ovarian cancer cells, suggesting an involvement of E2F8 in the growth of ovarian cancer. | Figure 4: E2F8 knockdown suppresses the proliferative capability of ovarian cancer cells. (a) Representative graph of western blotting experiment indicating the protein expression of E2F8 in both A2780 cell line and OVCAR3 cell line with or without si-E2F8 transfection. (b) Histogram showed the protein levels of E2F8 in both A2780 cell line and OVCAR3 cell line with or without si-E2F8 transfection. (c) CCK-8 test showed the viability of A2780 and OVCAR3 cells with or without si-E2F8 transfection. (d) Representative graph of colony formation assay showed the stained cell colonies in both A2780 cell line and OVCAR3 cell line with or without si-E2F8 transfection. (e) Histogram showed the percent of colony area in both A2780 cell line and OVCAR3 cell line with or without si-E2F8 transfection. ^^P < 0.01 versus the si-NC group. CCK-8: Cell counting kit-8, si-NC: Short interference-negative control.
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E2F8 knockdown induces ovarian cancer cell cycle arrest
Cell cycle is essential for cell proliferation, which was enriched in KEGG pathway analysis, as shown in [Figure 2]b. To investigate the mechanism underlying E2F8-mediated ovarian cancer cell proliferation suppression, we detected the cell cycle phase distribution of ovarian cancer cells with or without si-E2F8 transfection. We discovered that E2F8 silence caused ovarian cancer cell cycle arrest at G1 phase compared to the control and si-NC groups [Figure 5]. Altogether, these results suggested that E2F8 inhibited cell proliferation by arresting ovarian cancer cell cycle. | Figure 5: E2F8 knockdown induces ovarian cancer cell cycle arrest. Flow cytometry examined the cell cycle distribution of A2780 and OVCAR3 cells with or without si-E2F8 transfection. ^P < 0.05, ^^P < 0.01 versus the si-NC group. si-NC: Short interference-negative control.
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E2F8 knockdown inhibits the Wnt/β-catenin signaling pathway
KEGG pathway analysis showed an involvement of the Wnt/β-catenin signaling pathway in ovarian cancer. Given the importance of the Wnt/β-catenin signaling pathway in cell growth and death, the protein levels of β-catenin, c-Myc, and Cyclin D1 in ovarian cancer cells were examined by western blotting [Figure 6]. We discovered that E2F8 knockdown significantly reduced the protein levels of β-catenin, c-Myc, and Cyclin D1 in ovarian cancer cells. Thus, these results implied that E2F8 knockdown inhibited the Wnt/β-catenin signaling pathway in ovarian cancer. | Figure 6: E2F8 knockdown inhibits the Wnt/β-catenin signaling pathway. Western blotting indicated the expression of β-catenin, c-Myc, and Cyclin D1 in both A2780 cell line and OVCAR3 cell line with or without si-E2F8 transfection. ^^P < 0.01 versus the si-NC group. si-NC: Short interference-negative control.
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Overexpressing β-catenin augments cell proliferation and reverses cell cycle arrest
To further explore the involvement of the Wnt/β-catenin pathway in E2F8 activity, A2780 cell line was transfected with siRNA targeting E2F8 and β-catenin plasmid vector. As described above, the knockdown of E2F8 significantly decreased the ability of cell proliferation and impeded cell cycle progression [Figure 4], [Figure 5] and [Figure 7]. Colony formation assay revealed that overexpressing β-catenin improved the suppressed cell proliferation induced by E2F8 knockout [Figure 7]a and [Figure 7]b. Flow cytometry showed that overexpressing β-catenin reversed cell cycle arrest induced by E2F8 knockout [Figure 7]c and [Figure 7]d. Thus, these findings demonstrated that the effect of E2F8 in ovarian cancer cells was regulated by the Wnt/β-catenin signaling pathway. | Figure 7: Overexpressing β-catenin augments cell proliferation and reverses cell cycle arrest. (a) Representative graph of colony formation assay showed the stained cell colonies in A2780 cell line transfected with or without E2F8 siRNA and β-catenin plasmid vector. (b) Histogram showed the percent of colony area in A2780 cell line transfected with or without E2F8 siRNA and β-catenin plasmid vector. (c) Representative graph of flow cytometry showed cell cycle in A2780 cell line transfected with or without E2F8 siRNA and β-catenin plasmid vector. (d) Histogram showed the percent of cell cycle phase in A2780 cell line transfected with or without E2F8 siRNA and β-catenin plasmid vector. ^^P < 0.01 versus the si-NC with pcDNA group, ##P < 0.01 versus the si-E2F8 with pcDNA group. siRNA: Short interference RNA.
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| Discussion | | |
Despite significant advancements in early diagnosis, surgical and therapeutic treatment for ovarian cancer, it still has the fifth-highest overall fatality rate among malignant gynecologic cancers.[1],[7],[28] However, the pathogenesis of ovarian cancer is still unclear. In this study, using bioinformatic analysis approaches, we screened the potential targets for ovarian cancer from the public database, GEO. Moreover, we identified E2F8 as an important marker in ovarian cancers which was positively associated with a bad outcome in ovarian cancer patients. Furthermore, knockdown of E2F8 suppressed cellular proliferative capability and induced cell cycle arrest in ovarian cancer. Furthermore, silencing E2F8 inhibited the Wnt/β-catenin signaling pathway and overexpressing β-catenin improved the suppressed capacity of cell proliferation and reversed cell cycle arrest. Thereby, this report revealed that E2F8 had an involvement in the development of ovarian cancer which might act as a therapeutic target.
Emerging research into the processes underpinning ovarian cancer growth and chemoresistance has discovered many genetic and epigenetic abnormalities. The E2Fs are found highly expressed in numerous tissues and organs, with various cellular biological processed including cell proliferation, differentiation, death, and DNA repair.[8],[9],[10] Recently, E2F8 was identified as one of the E2F family that features a duplicated DNA-binding domain. Increasing researches reported that E2F8 had a role in cell cycle,[29] which was then involved in multiple cancers.[14],[15],[16],[17],[18] Eoh et al. reported that a higher E2F8 expression of ovarian cancer tissues than that of normal tissues.[30] In line with previous findings, we found that there was an increase in E2F8 expression of ovarian cancer tissues, which was related to a bad outcome in ovarian cancer patients. To further investigate the molecular mechanism of E2F8 underlying the development of ovarian cancer, we applied RNA interference technology. Then, silencing E2F8 suppressed cellular proliferation, suggesting that E2F8 had an involvement in the growth of ovarian cancer.
Considering that E2F8 could function as a transcriptional factor to control cell cycle, we further investigate whether E2F8 regulate ovarian cancer cell cycle. We discovered that knockdown of E2F8-induced ovarian cancer cell cycle arrest, which might account for the reduced proliferative ability induced by E2F8 silence.
The Wnt/β-catenin signaling pathway is essential for animal embryo development, organ formation, tissue regeneration, and other physiological functions.[31],[32] β-catenin is a hub mediator in the canonical Wnt signaling pathway. In response to binding with Wnt receptor ligands, β-catenin translocates into the nucleus, and then binds to target genes that control a variety of biological processes, including cell proliferation and differentiation.[33] In this report, our results revealed that E2F8 knockdown suppressed the Wnt/β-catenin signaling pathway activation in ovarian cancer and overexpressing β-catenin restored both the suppressed capacity of cell proliferation and cell cycle progression induced by E2F8 knockdown. Therefore, these data implied that E2F8 had a regulatory role in cell proliferation and cell cycle through the Wnt/β-catenin signaling pathway.
| Conclusion | | |
In this report, using a variety of bioinformatic analysis approaches, numerous potential biomarkers of ovarian cancer were screened from public database. E2F8 was found as an important biomarker in ovarian cancer, which was linked to a bad outcome in ovarian cancer patients. Moreover, we found that silencing E2F8 suppressed cellular proliferation and induced cell arrest through regulating the Wnt/β-catenin signaling pathway in ovarian cancer. Taken altogether, these results revealed that E2F8 had an effect on cell proliferation and cell cycle through the Wnt/β-catenin signaling pathway in ovarian cancer, and E2F8 might act as a therapeutic target for ovarian cancer.
Availability of data and materials
All data generated or analyzed during this study are included in this published article.
Financial support and sponsorship
This work was supported by the National Natural Science Foundation of China (Grant No. 81872507) and Nn10 Project (Grant No. Nn10py2017-01).
Conflicts of interest
There are no conflicts of interest.
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[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6], [Figure 7]
[Table 1], [Table 2]
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