Construction and evaluation of a disulfidptosis diagnostic model for Alzheimer's disease
Objective To establish and validate the expression patterns and the diagnostic biomarkers of disulfidptosis-related genes in the pathogenesis of Alzheimer's disease(AD)using machine learning.Methods The GSE33000 dataset was downloaded from the GEO database as the training dataset,and Disulfidptosis-related genes were extracted for analysis.Expressions of differentially expressed genes between AD patients and healthy controls were compared in different immune cells and their biological functions were assessed through immune infiltration and GSVA enrichment analyses.AD patients were divided into two subgroups according to consensus clustering.Characteristic genes between AD patients and healthy controls,and between AD subtypes were identified by weighted gene co-expression network analysis(WGCNA).The intersected genes from these analyses were taken as AD signature genes.Random forest(RF),support vector machine(SVM),eXtreme gradient boosting(XGB),and generalized linear model(GLM)algorithms were employed to construct the training models,and the top five genes were screened as diagnostic biomarkers and then validated in the GSE122063 dataset.Results Of the 24 disulfidptosis-related genes reported in the literature,22 were significantly differentially expressed in the progression of AD.Immune infiltration analysis highlighted the potential roles of plasma cells,CD8+T cells,and monocytes in the process of di-sulfidptosis regulating AD.GSVA enrichment analysis indicated that disulfidptosis-related genes were upregulated in Huntington's di-sease,Parkinson's disease,and Alzheimer's disease in C2 subgroup compared to Cl subgroup.A total of 63 characteristic AD genes were identified by WGCNA.The residual of SVM model was the lowest,with the highest AUC value(0.946).The top five key AD sig-nature genes,PARP10,MAP2K1,PTBP1,PAK1,and NMS,were screened by SVM model,and used to construct an AD diagnostic risk assessment nomogram.Decision curve and calibration curve analyses demonstrated the predictive accuracy of the model was good.In the GSE122063 dataset,the model was confirmed to be accuracy,with an AUC value of 0.788,indicating the successful construction of the model.Conclusion Disulfidptosis plays a crucial role in the occurrence and diagnosis of AD.In the future,disulfidptosis-related genes may be used to predict and screen the potential therapeutic drugs for AD.
国家科技期刊平台
NSTL
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