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Identification of CFTR as a novel key gene in chromophobe renal cell carcinoma through bioinformatics analysis.


ABSTRACT: Chromophobe renal cell carcinoma (chRCC), the third most common histological subtype of RCC, comprises 5-7% of all RCC cases. The aim of the present study was to identify potential biomarkers for chRCC and to examine the underlying mechanisms. A total of 4 profile datasets were downloaded from the Gene Expression Omnibus database to identify differentially expressed genes (DEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of DEGs were performed with the Database for Annotation, Visualization and Integrated Discovery. A protein-protein interaction (PPI) network was constructed to predict hub genes. Hub gene expression within chRCC across multiple datasets, as well as overall survival, were investigated by utilizing the Oncomine platform and UALCAN dataset, separately. A total of 266 DEGs (88 upregulated genes and 168 downregulated genes) were identified from 4 profile datasets. Integrating the results from the PPI network, Oncomine platform and survival analysis, CFTR was screened as a key factor in the prognosis of chRCC. GO and KEGG analysis revealed that 266 DEGs were mainly enriched in 17 terms and 9 pathways. The present study identified key genes and potential molecular mechanisms underlying the development of chRCC, and CFTR may be a potential prognostic biomarker and novel therapeutic target for chRCC.

SUBMITTER: Wang S 

PROVIDER: S-EPMC6607225 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Identification of <i>CFTR</i> as a novel key gene in chromophobe renal cell carcinoma through bioinformatics analysis.

Wang Sheng S   Yu Zhi-Hong ZH   Chai Ke-Qun KQ  

Oncology letters 20190614 2


Chromophobe renal cell carcinoma (chRCC), the third most common histological subtype of RCC, comprises 5-7% of all RCC cases. The aim of the present study was to identify potential biomarkers for chRCC and to examine the underlying mechanisms. A total of 4 profile datasets were downloaded from the Gene Expression Omnibus database to identify differentially expressed genes (DEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of DEGs were performed with the Databa  ...[more]

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