Construction of the anoikis-related prediction model for Alzheimer's disease based on various machine learning models
Alzheimer's disease(AD)is the most common neurodegenerative disease.Anoikis is a new type of programmed cell death that can lead to the development of many diseases.The purpose of this study is to investigate the role of anoikis-related genes(ANRGs)in AD and establish a prediction model.Based on GSE33000,1 666 differentially expressed genes are screened,and 10 genes are obtained by intersection with 53 ANRGs.Using the above genes,310 patients with AD are classified into three subtypes by unsupervised clustering,and the differences of immune microenvironment among different subtypes are further analyzed.After that,WGCNA algorithm is used to screen the characteristic genes associated with AD,and combined with four machine learning models(RF,GLM,SVM and XGB),the AD risk prediction model is constructed and verified in three external cohorts(GSE5281,GSE29378 and GSE122063).Finally,we successfully construct a nomogram based on five AD characteristic genes(TM6SF1,SMYD3,OXCT1,MAP1B and ITPKB)of the XGB model to provide reference for clinical prediction of AD.
Alzheimer's diseaseAnoikisMolecular clustersMachine learningPrediction model
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