Bioinformatics and machine learning-based screening of key genes in Alzheimer's disease.

Abstract

Abstract Objective To analyze differentially expressed genes (DEGs) related to mitophagy between Alzheimer's disease (AD) patients and normal controls using bioinformatics and machine learning methods and to screen for key genes to provide theoretical support for the study of AD pathogenesis and therapeutic targets. Methods Data microarrays related to AD were obtained from the GEO database, and DEGs were obtained by differential expression analysis of gene expression data using R. The DEGs were intersected with mitophagy-related genes to obtain the genes of interest, and then the group of genes was enriched for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathways. A machine-learning model was constructed at the same time to screen key genes, construct risk prediction models and predict transcription factors based on key genes. In addition, consistent clustering analysis was performed on AD samples, followed by immunization and pathway analysis for each subtype. Results Seven key genes were finally screened from 38 mitophagy-related DEGs, and the risk prediction model constructed on the basis of these 7 genes had an AUC of 0.877. Cluster analysis classified the AD samples into two subtypes, and the immune infiltration between the two subtypes was also significantly different. Conclusion This study screened the key genes of AD based on bioinformatics, which provides a new perspective and potential therapeutic targets for exploring the potential mechanism of mitophagy affecting AD as well as a new idea and direction for individualized treatment of AD.