Screening Of Key Genes And Pathway Analysis Of Clear Cell Renal Cell Carcinoma Based On GEO Database

Objective: To further understand the pathogenesis of cc RCC through data mining,and at the same time provide new directions for the diagnosis of cc RCC and the provision of potential new targeted drugs.Methods: GEO downloaded the gene chip information of cc RCC patients in the data sets GSE53000,GSE53757,and GSE66272,and then processed the above data set through the GEO2 R online website and screened out differentially expressed genes.The Venn Diagram software package in R software was used to draw a Wayne diagram to screen overlapping differentially expressed genes.We then performed GO functional enrichment analysis and KEGG pathway enrichment analysis,and then used the online database String and Cytoscape software to perform protein-protein interaction network analysis and HUB gene network screening.Finally,the UALCAN database was used to screen the relative tumor and normal samples.Expression and then use survival analysis to clarify the correlation between genes and survival rates in patients with renal clear cell carcinoma.Results: 1.By processing the data sets GSE53000,GSE53757 and GSE66272 on the GEO-2R online website,the expression of differential genes in renal clear cell carcinoma and normal kidney tissue was screened.Then,by Wayne analysis,the overlapping differentially expressed genes among the three datasets were obtained,and 230 common differentially expressed genes were screened out.GO functional enrichment analysis and KEGG pathway enrichment analysis Through GO functional enrichment analysis,we can analyze the biological processes of these 230 common differentially expressed genes involved in enrichment mainly including anion transport,active transmembrane transporter activity,ion homeostasis,potassium ion import across plasma membrane,sodium ion homeostasis,kidney development,monovalent inorganic cation homeostasis,positive regulation of lipid localization.Growth factor receptor binding is mainly involved in its molecular function.The changes in cell composition are mainly related to brush border membrane,apical plasma membrane and plasma membrane.Enrichment analysis of KEGG pathway of differentially expressed genes showed that these DEGs were involved in glycolysis gluconeogenesis,PPAR signaling pathway,complement and coagulation cascades,and other signaling pathways.Protein-protein interaction network analysis and HUB gene network screening: The PPI network diagram between differentially expressed genes is analyzed by using the online database String and Cytoscape software.Use cyto Hubba to obtain the top 20 hub genes with the highest connectivity in the PPI network.Set the confidence level> 0.4 and Degree> 5 as the cut-off criterion.The results were: C3,KNG1,ALB,CP,IGFBP3,ENAM,GPC3,TC2,KCNJ1,AQP2,SCNN1 A,CNN1B,WNK4,SCNN1 G,SLC12A1,SLC12A3,CLCNKB,SLC26A4,PLG,EGF.Using UALCAN to screen and verify that PLG and CP are differently expressed in renal clear cell carcinoma and normal kidney tissue,and these genes are significantly related to the prognosis of renal clear cell carcinoma.Conclusion: Our research screened out two core differentially expressed genes of PLG and CP,and related signaling pathways.These two target genes play an important role in the occurrence and development of cc RCC,and they are expected to become potential cc RCC.Biomarkers and targets,but the pathogenesis and development process of cc RCC is complex.The genes in this study only involve a part of them,but it can provide a new idea for future research.