Exosomal gene-based predictive model and therapeutic target identification for Alzheimer’s disease: A bioinformatics analysis

Abstract

Background Alzheimer’s disease (AD) is a degenerative central nervous system disorder characterized by progressive cognitive and behavioral impairment. As nanoscale intercellular communication vesicles that carry AD-related pathological molecules, exosomes are promising biomarkers and therapeutic carriers for AD. In this study, we downloaded AD-related gene expression profiles and clinical data from the Gene Expression Omnibus (GEO) database (datasets GSE138260, GSE29378, GSE36980, and GSE5281). Through a series of bioinformatics analyses, clinical predictive model construction, pharmacological network analysis, and molecular docking simulations, we developed an exosomal gene-based predictive model for AD pathogenesis and identified potential pharmacological networks and molecular docking targets for AD treatment. Materials and methods AD-related gene expression and clinical data were retrieved from the GEO database. Bioinformatics analyses, clinical model construction, drug-gene network analysis, and molecular docking were subsequently performed to explore exosomal gene models for predicting AD pathogenesis, as well as potential pharmacological networks and molecular docking targets for AD therapy. Results A five-exosomal-gene predictive model was established, comprising CD44, CXCR4, TUBB, PSMA5, and PSMB3. Pharmacological network analysis of these five genes revealed their significant associations with chelidonine, 2-chloro-1,4-dinitrobenzene, oxazolone, phencyclidine, thioridazine, and etodolac. Further molecular docking simulations identified key binding targets, including R41, Y42, R78, Y79, C77, I88, C97, A98, I96, I72, L70, E67, G103, I91, and T102. Conclusions Our comprehensive analyses successfully established a reliable exosomal gene-based model for predicting AD pathogenesis, and identified relevant pharmacological networks and core molecular docking targets, providing novel insights for AD diagnosis and targeted therapy.