ArticleBioscience reports2019
Integrated analysis of a competing endogenous RNA network in renal cell carcinoma using bioinformatics tools.
Article in Bioscience reports, 2019. 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.
- Identification of hub genes, non-coding RNAs and pathways in Renal cell carcinoma (RCC): A comprehensive in silico study.Biochemistry and biophysics reports · 2025Article
- Analysis of the Effect of Sichs on Gastric Ulcer Rats Based on RNA Sequencing Technique.Computational and mathematical methods in medicine · 2022Article
- Use of Circular RNAs in Diagnosis, Prognosis and Therapeutics of Renal Cell Carcinoma.Frontiers in cell and developmental biology · 2022Review
- Circular RNAs in renal cell carcinoma: implications for tumorigenesis, diagnosis, and therapy.Molecular cancer · 2020Review
- Circular RNA in renal diseases.Journal of cellular and molecular medicine · 2020Review
- Identification of Key Genes in Gastric Cancer by Bioinformatics Analysis.BioMed research international · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCircular RNAs (circRNAs) are known to be closely involved in tumorigenesis and cancer progression. Nevertheless, their function and underlying mechanisms in renal cell carcinoma (RCC) remain largely unknown. The aim of the present study was to explore their expression, functions, and molecular mechanisms in RCC.
methodsWe downloaded the circRNA expression profiles from Gene Expression Omnibus (GEO) database, and RNA expression profiles from The Cancer Genome Atlas (TCGA) database. A ceRNA network was constructed based on circRNA-miRNA pairs and miRNA-mRNA pairs. Interactions between proteins were analyzed using the STRING database, and hub genes were identified using the cytoHubba app. We also constructed a circRNA-miRNA-hub gene regulatory module. Functional and pathway enrichment analyses were conducted using "DAVID 6.8" and R package "clusterProfiler".
resultsAbout 6 DEcircRNAs, 17 DEmiRNAs, and 134 DEmRNAs were selected for the construction of ceRNA network of RCC. Protein-protein interaction network and module analysis identified 8 hub genes. A circRNA-miRNA-hub gene sub-network was constructed based on 3 DEcircRNAs, 4 DEmiRNAs, and 8 DEmRNAs. GO and KEGG pathway analysis indicated the possible association of DEmRNAs with RCC onset and progression.
conclusionsThese findings together provide a deeper understanding of the pathogenesis of RCC and suggest potential therapeutic targets.
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