Identifying the novel key genes in renal cell carcinoma by bioinformatics analysis and cell experiments

Cancer Cell International
Yeda ChenGuohua Zeng

Abstract

Although major driver gene have been identified, the complex molecular heterogeneity of renal cell cancer (RCC) remains unclear. Therefore, more relevant genes need to be identified to explain the pathogenesis of renal cancer. Microarray datasets GSE781, GSE6344, GSE53000 and GSE68417 were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified by employing GEO2R tool, and function enrichment analyses were performed by using DAVID. The protein-protein interaction network (PPI) was constructed and the module analysis was performed using STRING and Cytoscape. Survival analysis was performed using GEPIA. Differential expression was verified in Oncomine. Cell experiments (cell viability assays, transwell migration and invasion assays, wound healing assay, flow cytometry) were utilized to verify the roles of the hub genes on the proliferation of kidney cancer cells (A498 and OSRC-2 cell lines). A total of 215 DEGs were identified from four datasets. Six hub gene (SUCLG1, PCK2, GLDC, SLC12A1, ATP1A1, PDHA1) were identified and the overall survival time of patients with RCC were significantly shorter. The expression levels of these six genes were significantly decreased in six ...Continue Reading

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Datasets Mentioned

BETA
GSE781
GSE53000
GSE68417

Methods Mentioned

BETA
surgical resection
chips
transfection
Assay
fluorescence microscopy
flow cytometry

Software Mentioned

GEO2R
DAVID
Molecular Complex Detection ( MCODE
cBioPortal
Search Tool for the Retrieval of
SPSS
UALCAN
Cytoscape
GEPIA ( Gene Expression Profiling Interactive Analysis )
MCODE

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