| Objective:To screen the genes related to the progression of renal cell clear cell carcinoma by bioinformatics as the key genes to predict the progression and prognosis of renal cell clear cell carcinoma.Method:Firstly,the high-throughput sequencing sets GSE150404 and GSE89563 of ccRCC patients were downloaded from the gene expression database platform.In these two data sets,the original data were divided into localized renal clear cell carcinoma group(including stage ? and ?)and advanced renal clear cell carcinoma group(including stage ? and ?).The differentially expressed genes of the two datasets were analyzed and screened by GEO2R on-line.The venn map generated by FunRich software was used to screen the DEGs expressed simultaneously in the two data sets.Secondly,the R language cluster Profiler package was used to annotate the gene ontology function of DEGs and analyze the Kyoto encyclopedia of genes and genomes pathway.Then the String database was used to construct the protein interaction network for the above-mentioned DEGs.Uses the Cytohubba plug-in to obtain the top 10 DEGs of the highest connectivity on the PPI network as the key gene(hub gene).Using the Cytoscape software MCODE plug-in to build a clustering function module for DEGs.Then through the cancer genome map to verify the key genes we screened.Finally,the TCGA database was used to explore the role of each Hub gene in renal clear cell carcinoma.According to the expression of these genes in renal cell carcinoma,the relationship between the expression of each Hub gene and the overall survival rate was evaluated by Kaplan-Meier Plotter to clarify whether there was a difference in survival between the two groups.Log rank was used to test the above differences.For meaningful genes,AUC was calculated by ROC curve as the efficacy of observing prognosis.Results:Through GE02R analysis,503 DEGs were screened from GSE150404 data set,of which 327 genes were up-regulated and 176 genes were down-regulated.484 DEGs were filtered out from the GSE89563 dataset.Among them,369 genes were up-regulated and 115 genes were down-regulated.Venn analysis showed that there were 168 genes up-regulated and 44 genes down-regulated.In GO analysis,BP is mainly enriched in biological processes such as cell migration,proliferation and apoptosis,angiogenesis and positive regulation of erkl-erk2,while CC is mainly enriched in plasma membrane components,peroxisome,extracellular matrix,extracellular domain and receptor complex.MF is mainly enriched in cytokine receptor activity,binding protease,binding growth factor,transmembrane receptor protein tyrosine kinase receptor activity,cytoskeleton composition and binding lipoprotein granules and other molecular functions;KEGG results showed that it was mainly enriched in PI3K-Akt,HIF-1,PPAR pathway,fatty acid and amino acid metabolism,complement and coagulation cascade system and insulin resistance.String database was used to analyze the PPI network online,the comprehensive score was more than 0.4,and the top 10 key genes with the highest connectivity were obtained by Cytohubba,which were OL-6,KDR,HGF,CD44,PLCB1,EDN1,VWF,FLT1,PLAUR,EHHADH.Through TCGA database,it was confirmed that the expression of IL-6,CD44,HGF and PLAUR in progressive renal cell carcinoma was significantly higher than that in localized renal cell carcinoma,while the expression of KDR,PLCB1,VWF and FLT1 in progressive renal cell carcinoma was significantly lower than that in localized renal cell carcinoma.Through Kaplan-Meier survival analysis,it was found that three IL-6,PLAUR,CD44 genes were associated with prognosis,among which IL-6 had the highest prognostic efficacy.Conclusion:Multiple genes are associated with the progression of ccRCC.Among them,IL-6,PLAUR and CD44 are key genes in the progression of ccRCC and are related to prognosis.Their high expression indicates tumor progression and poor prognosis.The three genes IL-6,PLAUR and CD44 may have certain significance for the treatment selection and prognosis of ccRCC. |
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