ArticleJournal of ovarian research2021
Integrative analysis of key candidate genes and signaling pathways in ovarian cancer by bioinformatics.
Article in Journal of ovarian research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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12 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.
- KIF2C/MCAK a prognostic biomarker and its oncogenic potential in malignant progression, and prognosis of cancer patients: a systematic review and meta-analysis as biomarker.Critical reviews in clinical laboratory sciences · 2024Pooled it
- Comprehensive analysis of metabolic reprogramming-related gene signatures for predicting ovarian cancer prognosis, the immune landscape, and potential treatment options.Discover oncology · 2026Article
- Targeting AKR1B1 reprograms tumor-associated macrophages to enhance antitumor immunity.Journal for immunotherapy of cancer · 2026Article
- Hepatoprotective evaluation of benzoylated emodin derivatives: Integrating bioinformatics andBioMedicine · 2026Article
- Network pharmacology and in silico approach to study the mechanism of quercetin against breast cancer.In silico pharmacology · 2025Article
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- Overexpression of CDCA8 predicts poor prognosis and drug insensitivity in lung adenocarcinoma.BMC medical genomics · 2024Article
- Single-cell RNA sequencing reveals that MYBL2 in malignant epithelial cells is involved in the development and progression of ovarian cancer.Frontiers in immunology · 2024Article
- Eg5 and Diseases: From the Well-Known Role in Cancer to the Less-Known Activity in Noncancerous Pathological Conditions.Biochemistry research international · 2024Review
- Downregulation of BUBR1 regulates the proliferation and cell cycle of breast cancer cells and increases the sensitivity of cells to cisplatin.In vitro cellular & developmental biology. Animal · 2023Article
- Inhibition of CDK1 by RO-3306 Exhibits Anti-Tumorigenic Effects in Ovarian Cancer Cells and a Transgenic Mouse Model of Ovarian Cancer.International journal of molecular sciences · 2023Article
- CDC20 is a novel biomarker for improved clinical predictions in epithelial ovarian cancer.American journal of cancer research · 2022Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOvarian cancer is one of the most common gynecological tumors, and among gynecological tumors, its incidence and mortality rates are fairly high. However, the pathogenesis of ovarian cancer is not clear. The present study aimed to investigate the differentially expressed genes and signaling pathways associated with ovarian cancer by bioinformatics analysis.
methodsThe data from three mRNA expression profiling microarrays (GSE14407, GSE29450, and GSE54388) were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes between ovarian cancer tissues and normal tissues were identified using R software. The overlapping genes from the three GEO datasets were identified, and profound analysis was performed. The overlapping genes were used for pathway and Gene Ontology (GO) functional enrichment analysis using the Metascape online tool. Protein-protein interactions were analyzed with the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). Subnetwork models were selected using the plugin molecular complex detection (MCODE) application in Cytoscape. Kaplan-Meier curves were used to analyze the univariate survival outcomes of the hub genes. The Human Protein Atlas (HPA) database and Gene Expression Profiling Interactive Analysis (GEPIA) were used to validate hub genes.
resultsIn total, 708 overlapping genes were identified through analyses of the three microarray datasets (GSE14407, GSE29450, and GSE54388). These genes mainly participated in mitotic sister chromatid segregation, regulation of chromosome segregation and regulation of the cell cycle process. High CCNA2 expression was associated with poor overall survival (OS) and tumor stage. The expression of CDK1, CDC20, CCNB1, BUB1B, CCNA2, KIF11, CDCA8, KIF2C, NDC80 and TOP2A was increased in ovarian cancer tissues compared with normal tissues according to the Oncomine database. Higher expression levels of these seven candidate genes in ovarian cancer tissues compared with normal tissues were observed by GEPIA. The protein expression levels of CCNA2, CCNB1, CDC20, CDCA8, CDK1, KIF11 and TOP2A were high in ovarian cancer tissues, which was further confirmed via the HPA database.
conclusionTaken together, our study provided evidence concerning the altered expression of genes in ovarian cancer tissues compared with normal tissues. In vivo and in vitro experiments are required to verify the results of the present study.
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