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

Abstract

The present study aimed to validate the associa‑ tion 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 from GSE109887, and the biological functions of the resulting CRGs were explored by cluster analysis, weighted gene co‑expression network analysis and similar methods to identify the best machine model. Moreover, an external dataset GSE33000 and a nomogram were used to validate the model. The mRNA and protein expression of CRGs were validated using the SH‑SY5Y cell model and the Sprague‑Dawley rat animal model. The RT‑qPCR and western blotting results showed that the mRNA and protein expression content of dihydrolipoamide dehydrogenase, ferredoxin 1, glutaminase and pyruvate dehydrogenase E1 subunit β decreased, and the expression of dihydrolipoamide branched chain transacylase E2 increased in AD, which supported the bioinformatic analysis results. The CRG expression alterations affected the aggregation and infiltration of certain immune cells. The present study also confirmed the accuracy and validity of AD diagnostic models and nomograms, and validated the association between five CRGs and AD, indicating a significant difference between patients with AD and healthy individuals. Therefore, CRGs are expected to serve as relevant biomarkers for the diagnosis and prognostic monitoring of AD.