Diagnostic models and predictive drugs associated with cuproptosis hub genes in Alzheimer's disease

Abstract

Alzheimer's disease (AD) is a chronic neurodegenerative disease, and its underlying genes and treatments are unclear. Abnormalities in copper metabolism can prevent the clearance of β-amyloid peptides and promote the progression of AD pathogenesis. Therefore, the present study used a bioinformatics approach to perform an integrated analysis of the hub gene based on cuproptosis that can influence the diagnosis and treatment of AD. The gene expression profiles were obtained from the Gene Expression Omnibus database, including non-demented (ND) and AD samples. A total of 2,977 cuproptosis genes were retrieved from published articles. The seven hub genes associated with cuproptosis and AD were obtained from the differentially expressed genes and WGCNA in brain tissue from GSE33000. The GO analysis demonstrated that these genes were involved in phosphoribosyl pyrophosphate, lipid, and glucose metabolism. By stepwise regression and logistic regression analysis, we screened four of the seven cuproptosis genes to construct a diagnostic model for AD, which was validated by GES15222, GS48350, and GSE5281. In addition, immune cell infiltration of samples was investigated for correlation with these hub genes. We identified six drugs targeting these seven cuproptosis genes in DrugBank. Hence, these cuproptosis gene signatures may be an important prognostic indicator for AD and may offer new insights into treatment options.