ArticleTranslational cancer research2022
Identification of novel candidate genes and small molecule drugs in ovarian cancer by bioinformatics strategy.
Article in Translational cancer research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 8 citations in OpenAlex.
- A bioinformatics approach to identify potential biomarkers of high-grade ovarian cancer.Journal of the Turkish German Gynecological Association · 2026Article
- Identification of key candidate genes for ovarian cancer using integrated statistical and machine learning approaches.Briefings in bioinformatics · 2025Article
- The role and therapeutic value of NUSAP1 in human cancers.Journal of translational medicine · 2025Review
- Bioinformatics Based Drug Repurposing Approach for Breast and Gynecological Cancers:European journal of breast health · 2025Article
- Proteomic analysis of ascitic extracellular vesicles describes tumour microenvironment and predicts patient survival in ovarian cancer.Journal of extracellular vesicles · 2024Article
- cAMP-Dependent Signaling and Ovarian Cancer.Cells · 2022Review
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Ovarian cancer (OC) is the most lethal type of malignancies in the female reproductive system. This study aimed to identify novel biomarkers and potential small molecule drugs in OC by integrating two expression profile datasets. Methods: GSE18520 and GSE14407 from the Gene Expression Omnibus (GEO) database were selected and the overlapped differentially expressed genes (DEGs) were detected. The Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway enrichment analysis were performed to establish the protein-protein interaction (PPI) network of DEGs and identified the hub genes. Gene Expression Profiling Interactive Analysis (GEPIA), Oncomine database and The Human Protein Atlas (HPA) were used to validate the expression of the identified hub genes. The prognostic value of these hub genes were evaluated by the Kaplan Meier plotter online tool. The expression of NCAPG was further explored by immunohistochemistry in our OC tissues. Moreover, CMap database was used to look for prospective small compounds with therapeutic efficacy based on OC RNA-seq. Results: A total of 433 DEGs were identified. The DEGs were mainly enriched in negative regulation of transcription and pathways in cancer. A PPI network was constructed with 344 nodes and 1,596 interactions. The top ten module genes were chosen as hub genes. Among which, survival analysis showed that patients with high expression of Conclusions: By means of bioinformatics analysis, we identified six real hub genes and indicated a group of candidate small molecule drugs as adjunctive agents for OC. They could be the potential novel biomarkers for the diagnosis and promising therapeutic targets of OC.
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