Bioinformatics and Machine Learning-Based Screening of Key Genes in Alzheimer's Disease

Abstract

Objective To provide theoretical support for the study of AD pathogenesis and therapeutic targets. Methods The AD data were downloaded from the GEO database for differential expression analysis to obtain DEGs, followed by enrichment analysis of GO and KEGG signalling pathways, construction of machine learning models to screen key genes, and construction of risk prediction models and prediction of transcription factors based on key genes. In addition, consistent clustering analysis was performed on AD samples. Results Seven key genes were finally screened in this study, and the risk prediction model constructed on the basis of these seven genes had an AUC of 0.877. Cluster analysis classified the AD samples into two subtypes, and there was also a significant difference in immune infiltration between the two subtypes. Conclusion This study provides new perspectives and potential therapeutic targets for exploring the potential mechanisms by which mitochondrial autophagy affects AD, as well as providing directions for individualised treatment of AD.