Construction and validation of a bioinformatics-based screen for Cuproptosis-related genes and risk model for Alzheimer's disease

Abstract

Abstract This study aimed to validate the correlation between core cuproptosis genes (CRGs) and Alzheimer's disease (AD) from both bioinformatics and experimental perspectives and also to develop a risk prediction model. To this end, 78 human-derived temporal back samples were analyzed in GSE109887, and then the biological functions of the resulting CRGs were explored by cluster analysis, weighted gene co-expression network analysis (WGCNA), and similar methods to identify the best machine model. Moreover, a nomogram was developed to validate the model. The mRNA and protein expression of CRGs were validated using the SH-SY5Y cell model and SD rat animal model. The RT-qPCR and western blot results showed that the mRNA and protein expression content of DLD, FDX1, GLS, and PDHB decreased, and the DBT expression content increased in AD, which supported the bioinformatic analysis results. CRGs expression alterations affected the aggregation and infiltration of certain immune cells. The study results also confirmed the accuracy and validity of AD diagnostic models and nomograms. This study validated the correlation between five CRGs and AD, indicating a significant difference between AD patients and healthy individuals. Therefore, CRGs are expected to serve as relevant biomarkers for the diagnosis and prognostic monitoring of AD.