| ObjectiveIn order to establish a prognostic model of renal clear cell carcinoma,we obtained genes related to cellular lipid uptake from the GSEA database,and obtained the related data of renal clear cell carcinoma and normal tissues adjacent to the cancer from the TCGA database.Then we use bioinformatics technology to establish a prognosis model of renal clear cell carcinoma to predict the prognosis of patients with renal clear cell carcinoma.This study established a model that can predict the prognosis of patients with renal clear cell carcinoma based on lipid uptake-related genes,so as to more effectively guide the individualized treatment of clinical patients and predict the survival of patients with renal clear cell carcinoma.MethodsIn this study,we first obtained the genes related to cellular lipid uptake through the GSEA website.Pearson analysis was used to analyze the correlation of the cellular lipid uptake-related genes.Then we obtained the data of normal kidney tissue samples and renal clear cell carcinoma tissue samples through the TCGA database,and used the R language limma package to analyze the difference in lipid uptake-related gene expression between normal kidney tissue samples and renal clear cell carcinoma tissue samples.then we draw heat map and violin map by R language pheatmap package and vioplotb package.The univariate COX regression method combined with the clinical survival data of patients in the TCGA database was used to screened lipid uptake-related genes that may related to the survival of patients,and calculated the hazard ratio(HR)and 95%confidence interval of the related genes,p<0.05 was statistically significant,HR<1 was a protective gene,HR>1 was a dangerous gene.Further we used LASSO regression analysis to construct a prognostic risk model.The prognostic model divided patients into high-risk and low-risk groups.The Kaplan-Meier survival curve was drawn to judge whether the prognostic model was sufficiently discriminative,and then ROC curve was drawn and the area under curve(AUC)was calculated to further evaluate the accuracy of the model.Both univariate COX regression analysis and multivariate COX regression analysis showed that the risk score of the prognostic model can be used as an independent risk factor for renal clear cell carcinoma.ResultsWe obtained 17 genes related to cellular lipid uptake(ACSL1,ACSL3,ACSL5,AKT1,AKT2,CD36,FABP3,RPS6KB1,SLC27A1,SLC27A2,SLC27A4,SLC27A5,SLC2A1,SPX,THBS1)through the GSEA website.The process may be achieved through cell transmembrane transport or endocytosis.According to Pearson analysis,the expression of some lipid uptake gene members was correlated.The data of 72 normal kidney samples and 539 renal clear cell carcinoma samples were obtained from TCGA data,and the expression of 17 lipid uptake genes was analyzed using the R language limma package.Among them,The expression levels of 15 genes(ACSL1,ACSL3,ACSL5,AKT1,AKT2,CD36,FABP3,RPS6KB1,SLC27A1,SLC27A2,SLC27A4,SLC27A5,SLC2A1,SPX,THBS1)in normal kidney tissues and renal clear cell carcinoma were different and statistically significant(P<0.05).Through univariate COX regression analysis,among the 17 lipid uptake genes,5 lipid uptake-related genes(ACSL1,EPRS,THBS1,SLC27A2,CD36)were related to the prognosis of clear cell renal cell carcinoma(P<0.05),and were protective genes(HR<1).Through further LASSO regression analysis,seven lipid uptake-related genes(ACSL1,ACSL5,AKT2,EPRS,RPS6KB1,SLC27A1,SLC27A2)were finally determined as predictive genes and used to establish a prognostic risk model for renal clear cell carcinoma.In order to verify the quality of the prognostic model,the ROC curve analysis was used to calculate the area under the curve of the prognostic model for 3 years,5 years,7 years,and 10 years to be 0.697,0.701,0.728,and 0.732,respectively,indicating that the quality of the prediction model was good and had certain predictive ability.The K-M survival curve of the prognostic model showed that the survival time of patients with renal clear cell carcinoma in the low-risk group and the high-risk group in the model were significantly different,and there was a statistical difference.Both univariate COX regression analysis and multivariate COX regression analysis concluded that the prognostic model of renal clear cell carcinoma based on lipid uptake genes can be used as independent risk factors to predict patient prognosis.ConclusionsIn this study,a prognostic model of renal clear cell carcinoma was constructed based on 7 lipid uptake genes.After verification,the quality of the prognostic model was good and had certain predictive power for the prognosis of patients with renal clear cell carcinoma.These same lipid uptake-related genes may provide new diagnosis and treatment ideas for individualized treatment of renal clear cell carcinoma. |
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