Abstract / Summary
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and memory impairment. Oxidative stress plays an important role in disease progression in AD. However, the molecular mechanisms underlying oxidative stress in AD remain unclear and reliable biomarkers for its detection have not been identified. In this study, bioinformatics analyses integrating differential expression analysis and weighted gene co-expression network analysis were performed to identify oxidative-stress-related differentially expressed genes. Using machine learning algorithms, ACO2 was identified as a significantly downregulated hub gene associated with AD. Single-cell transcriptomic analysis further showed markedly reduced ACO2 expression levels in hippocampal neurons of patients with AD. To validate these findings, proteomic analysis, quantitative real-time polymerase chain reaction (qPCR), and Western blotting were performed in a mouse model of AD. These results confirmed decreased ACO2 mRNA and protein expression levels in the hippocampus. Consistently, qPCR analysis of peripheral blood samples from patients with AD revealed significantly reduced ACO2 expression levels compared to those in healthy controls. Collectively, these findings identified ACO2 as an oxidative-stress-related biomarker associated with AD and suggested its potential value as an AD-related candidate biomarker.