Identification of cuproptosis-related genes in Alzheimer's disease based on bioinformatic analysis.

OBJECTIVE

To explore the role of cuproptosis in Alzheimer's disease (AD).

METHODS

An AD-related microarray dataset was downloaded from the Gene Expression Omnibus (GEO) database (GSE140830). Weighted gene co-expression network analysis was used to identify AD-related modular genes. The Venn analysis was performed to obtain module genes associated with apoptosis and cuproptosis. Besides, we conducted an enrichment analysis of overlapped genes and constructed the protein-protein interaction (PPI) network, followed by screening hub genes and those significantly associated with AD were used to construct models of apoptosis and cuproptosis, respectively. Further, receiver operating characteristic (ROC) curve analysis, decision curve analysis (DCA), and subgroup analysis were used to compare the AD prediction performance of two models. Finally, the accuracy and reliability of AD prediction models were verified by GSE26927.

RESULTS

We obtained 42 module genes related to apoptosis and 9 module genes related to cuproptosis. The enrichment analysis results revealed MAPK signaling pathway as the common signaling pathway of apoptosis- and cuproptosis-related genes. Next, the hub genes associated with apoptosis (TRADD, FADD, BIRC2, and CASP2) and cuproptosis (MAP2K1, SLC31A1, and PDHB) in AD were identified, which were used to construct apoptosis and cuproptosis models to distinguish AD patients from the control group (P < 0.05). The ROC, DCA, and subgroup analysis results showed that apoptosis-related models and cuproptosis-related models had comparable ability in predicting AD. GSE26927 further confirmed that the two models have comparable predictive effects for AD.

CONCLUSIONS

The cuproptosis model had a certain performance in predicting AD. Three hub genes (MAP2K1, SLC31A1, and PDHB) closely related to cuproptosis in AD might serve as biomarkers for AD diagnosis and treatment.