ArticleMedical oncology (Northwood, London, England)2016
Identification of candidate biomarkers and analysis of prognostic values in ovarian cancer by integrated bioinformatics analysis.
Article in Medical oncology (Northwood, London, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers.
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Who cites it
60 citing papers in PubMed.
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- A bioinformatics approach to identify potential biomarkers of high-grade ovarian cancer.Journal of the Turkish German Gynecological Association · 2026Article
- Prediction of new candidate proteins and analysis of sub-modules and protein hubs associated with seed development in rice (Oryza sativa) using an ensemble network-based systems biology approach.BMC plant biology · 2025Article
- Integrative Bioinformatics Analysis for Targeting Hub Genes in Hepatocellular Carcinoma Treatment.Current genomics · 2025Article
- DTL promotes the growth and migration of melanoma cells through the ERK/E2F1/BUB1 axis.Scientific reports · 2024Article
- Unravelling driver genes as potential therapeutic targets in ovarian cancer via integrated bioinformatics approach.Journal of ovarian research · 2024Article
- Mitotic Functions and Characters of KIF11 in Cancers.Biomolecules · 2024Review
- Screening and identification of key biomarkers of depression using bioinformatics.Scientific reports · 2023Article
- Bioinformatic identification of differentially expressed genes associated with hepatocellular carcinoma prognosis.Medicine · 2022Article
- Exploring Potential Biomarkers, Ferroptosis Mechanisms, and Therapeutic Targets Associated with Cutaneous Squamous Cell Carcinoma via Integrated Transcriptomic Analysis.Journal of healthcare engineering · 2022Article
- Novel Diagnostic and Therapeutic Options forFrontiers in pharmacology · 2022Article
- Integrative Analysis for Identification of Therapeutic Targets and Prognostic Signatures in Non-Small Cell Lung Cancer.Bioinformatics and biology insights · 2022Article
- ASPM combined with KIF11 promotes the malignant progression of hepatocellular carcinoma via the Wnt/β-catenin signaling pathway.Experimental and therapeutic medicine · 2021Article
- Identification of potential genes associated with ALDH1A1 overexpression and cyclophosphamide resistance in chronic myelogenous leukemia using network analysis.Medical oncology (Northwood, London, England) · 2021Article
- Zwint facilitates melanoma progression by promoting c-Myc expression.Experimental and therapeutic medicine · 2021Article
- Specific Antiproliferative Properties of Proteinaceous Toxin Secretions from the Marine AnnelidMarine drugs · 2021Article
- An Integrated Platform for Skin Cancer Heterogenous and Multilayered Data Management.Journal of medical systems · 2021Article
- Biodata Mining of Differentially Expressed Genes between Acute Myocardial Infarction and Unstable Angina Based on Integrated Bioinformatics.BioMed research international · 2021Article
- Integrated Analysis of FAM57A Expression and Its Potential Roles in Hepatocellular Carcinoma.Frontiers in oncology · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ovarian cancer is the first leading cause of mortality in gynecological malignancies. To identify key genes and microRNAs in ovarian cancer, mRNA microarray dataset GSE36668, GSE18520, GSE14407 and microRNA dataset GSE47841 were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) and microRNAs (DEMs) were obtained using GEO2R. Functional and pathway enrichment analysis were performed for DEGs using DAVID database. Protein-protein interaction (PPI) network was established by STRING and visualized by Cytoscape. Following, overall survival (OS) analysis of hub genes was performed by the Kaplan-Meier plotter online tool. Module analysis of the PPI network was performed using MCODE. Moreover, miRecords was applied to predict the targets of the DEMs. A total of 345 DEGs were obtained, which were mainly enriched in the terms related to cell cycle, mitosis, and ovulation cycle process. A PPI network was constructed, consisting of 141 nodes and 296 edges. Sixteen genes had high degrees in the network. High expression of four genes of the 16 genes was associated with worse OS of patients with ovarian cancer, including CCNB1, CENPF, KIF11, and ZWINT. A significant module was detected from the PPI network. The enriched functions and pathways included cell cycle, nuclear division, and oocyte meiosis. Additionally, a total of 36 DEMs were identified. The expression of KIF11 was negatively correlated with that of has-miR-424 and has-miR-381, and it was also the potential target of two microRNAs. In conclusion, these results identified key genes, which could provide potential targets for ovarian cancer diagnosis and treatment.
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