Identification of Copper Metabolism Related Biomarkers, Polygenic Prediction Model, and Potential Therapeutic Agents in Alzheimer's Disease.

Abstract

BACKGROUND:

The underlying pathogenic genes and effective therapeutic agents of Alzheimer's disease (AD) are still elusive. Meanwhile, abnormal copper metabolism is observed in AD brains of both human and mouse models.

OBJECTIVE:

To investigate copper metabolism-related gene biomarkers for AD diagnosis and therapy.

METHODS:

The AD datasets and copper metabolism-related genes (CMGs) were downloaded from GEO and GeneCards database, respectively. Differentially expressed CMGs (DE-CMGs) performed through Limma, functional enrichment analysis and the protein-protein interaction were used to identify candidate key genes by using CytoHubba. And these candidate key genes were utilized to construct a prediction model by logistic regression analysis for AD early diagnosis. Furthermore, ROC analysis was conducted to identify a single gene with AUC values greater than 0.7 by GSE5281. Finally, the single gene biomarker was validated by quantitative real-time polymerase chain reaction (qRT-PCR) in AD clinical samples. Additionally, immune cell infiltration in AD samples and potential therapeutic drugs targeting the identified biomarkers were further explored.

RESULTS:

A polygenic prediction model for AD based on copper metabolism was established by the top 10 genes, which demonstrated good diagnostic performance (AUC values). COX11, LDHA, ATOX1, SCO1, and SOD1 were identified as blood biomarkers for AD early diagnosis. 20 agents targeting biomarkers were retrieved from DrugBank database, some of which have been proven effective for the treatment of AD.

CONCLUSIONS:

The five blood biomarkers and copper metabolism-associated model can differentiate AD patients from non-demented individuals and aid in the development of new therapeutic strategies.

• Authors: Yuanyuan Du, Xi Chen, Bin Zhang, Xing Jin, Zemin Wan, Min Zhan, Jun Yan, Pengwei Zhang, Peifeng Ke, Xianzhang Huang, Liqiao Han, Qiaoxuan Zhang
• Journal: Journal of Alzheimer's disease : JAD (J Alzheimers Dis) Vol. 95 Issue 4 Pg. 1481-1496 ( 2023) ISSN: 1875-8908 [Electronic] Netherlands
• PMID: 37694370 (Publication Type: Journal Article)