| Objective: Renal cell carcinoma is one of the most common tumor types in the urinary system.And the most common type of renal cell carcinoma is clear cell Renal Cell Carcinoma,which has a high metastasis rate and a high mortality rate.The molecular biological mechanism of the occurrence and development of clear cell Renal Cell Carcinoma has not been fully elucidated,it is of great significance to study the molecular mechanism of clear cell Renal Cell Carcinoma.Bioinformatics are now widely used in the field of cancer biology to help researchers find cancer biomarkers and therapeutic targets.In this study,we used open bioinformatics databases to find potential therapeutic targets related to tumor progression,This study may contribute to the clinical treatment of clear renal cell carcinoma.Methods: Firstly,we obtained the expression profile of clear cell Renal Cell Carcinoma in the Human Cancer Genome Atlas(TCGA)database,which included 539 ccRCC samples and 72 adjacent cancer samples.Then,we used the "edgeR" package to obtain genes that were differentially expressed in ccRCC cancer with adjacent tissues,and in stage ?/? ccRCC patients with stage ?/? patients,then screened genes that were up-regulated as the course of ccRCC progressed.Finally,we analyzed the correlation between the up-regulated genes AURKB and KIF18 B in ccRCC patients.Download the GSE53757 dataset of The Gene Expression Omnibus(GEO)database,which contains 72 ccRCC tissue samples and 72 adjacent cancer samples,to verify the differential expression and correlation of AURKB and KIF18 B in ccRCC patients.We continued to analyze the correlation between the clinical parameters of AURKB,KIF18 B with ccRCC patients,as well as the prognosis of ccRCC patients in different situations.KEGG was used to analyze the potential mechanism of the co-expression genes of AURKB and KIF18 B in ccRCC.Results: 1.By analyzing the TCGA database,3 genes were identified that were highly expressed in ccRCC,and their expression levels were significantly up-regulated as the clinical stages of ccRCC patients progressed: IL20 RB,AURKB,and KIF18 B.of which AURKB and KIF18 B were closely related in cCCCC patients(r = 0.963,p <0.001),so we chose AURKB and KIF18 B for further analysis.2.The GSE53753 dataset derived from the GEO database was used as a validation set to verify that the expression levels of AURKB and KIF18 B increased significantly with the progress of ccRCC and the close correlation between them in ccRCC patients(r = 0.572,p<0.001).3.High expression of AURKB and high expression of KIF18 B were related to T stage,N stage,M stage,and Grade of ccRCC patients(p<0.001).In the four situations of which AURKB and KIF18 B were synergy expressed,AURKB(high)+KIF18B(high)was related to T stage,N stage,M stage,and Grade of ccRCC patients(p <0.001).4.High expression of AURKB(p<0.0001)and high expression of KIF18B(p<0.0001)were associated with poor prognosis in patients with ccRCC.AURKB(high)+ KIF18B(high)(p<0.0001))was related to poor prognosis in ccRCC patients.The multivariate COX regression model showed that in addition to including age,T stage,N stage,M stage and Grade,when AURKB and KIF18 B were analyzed separately,AURKB(HR=1.955,95%CI:1.203-3.176)and KIF18B(HR=2.077,95%CI:1.282-3.365)were important independent prognostic risk factors of ccRCC patients respectively.However,when analyzing the synergistic expression of the two,it showed that only AURKB(high)+KIF18B(high)were independent prognostic risk factors(HR = 2.094,95%CI:1.297-3.379).Conclusion: 1.Differentially expressed genes AURKB and KIF18 B were found in ccRCC patients,and their expression levels increased significantly with the occurrence and progression of ccRCC patients.2.AURKB was closely related to KIF18 B in ccRCC patients.3.Considering the synergistic expression of each other,only AURKB(high)+KIF18B(high)expression was an independent prognostic risk factor for ccRCC patients,but not other situations. |
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