ZFP36 as a Ferroptosis-related Diagnostic Marker Gene for Diabetic Nephropathy
Objective To screen out diagnostic marker genes for diabetic nephropathy(DN)using machine learning models.Methods The microarray dataset downloaded from the GEO database was integrated using R language and the batch effect was eliminated.The"limma"package was used to screen for the differentially expressed genes(DEGs)between the healthy control group and the DN group.3 machine learning models,namely,LASSO regression,Random Forest(RF)and Support Vector Machine(SVM),were used to screen out marker genes with the most significant correlation with DN,and the intersection of the DEGs screened by different algorithms and ferroptosis-related genes was performed using the"Venn"package,to obtain the ferroptosis-related diagnostic markers of DN.Differential expression and diagnostic efficacy of diagnostic marker genes were verified by differential analysis,ROC curve analysis,in vivo and in vitro experiments.Results A total of 80 DEGs were screened,and the intersection of the results of machine learning models and ferroptosis-related genes yielded a total of three diagnostic marker genes,among which zinc finger protein 36(ZFP36)was differentially expressed in both the training and validation groups with an area under the curve(AUC)of＞0.7.The experimental results of both immunohistochemistry and cellular protein immunoblotting in the mouse DN model showed that the expression of ZFP36 was significantly reduced in the DN group(P＜0.05).Conclusions ZFP36 can have potential diagnostic efficacy for DN and can be idealized as a ferroptosis-related diagnostic marker gene and therapeutic target for DN.
diabetic nephropathyzinc finger protein 36(ZFP36)diagnostic markerferroptosisanimal,experimentmice
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