Exploring the key ferroptosis-related gene in the peripheral blood of patients with Alzheimer's disease and its clinical significance

Xiaonan Wang; Yaotian Tian; Chunmei Li; Min Chen

doi:10.3389/fnagi.2022.970796

Exploring the key ferroptosis-related gene in the peripheral blood of patients with Alzheimer's disease and its clinical significance

Front Aging Neurosci. 2022 Sep 1:14:970796. doi: 10.3389/fnagi.2022.970796. eCollection 2022.

Authors

Xiaonan Wang^{1

2}, Yaotian Tian^{1

2}, Chunmei Li^{1

2}, Min Chen^{1

2}

Affiliations

¹ Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
² Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.

Abstract

Introduction: Alzheimer's disease (AD) is the most common type of dementia, and there is growing evidence suggesting that ferroptosis is involved in its pathogenesis. In this study, we aimed to investigate the key ferroptosis-related genes in AD and identify a novel ferroptosis-related gene diagnosis model for patients with AD.

Materials and methods: We extracted the human blood and hippocampus gene expression data of five datasets (GSE63060, GSE63061, GSE97760, GSE48350, and GSE5281) in the Gene Expression Omnibus database as well as the ferroptosis-related genes from FerrDb. Differentially expressed ferroptosis-related genes were screened by random forest classifier, and were further used to construct a diagnostic model of AD using an artificial neural network. The patterns of immune infiltration in the peripheral immune system of AD were also investigated using the CIBERSORT algorithm.

Results: We first screened and identified 12 ferroptosis-related genes (ATG3, BNIP3, DDIT3, FH, GABARAPL1, MAPK14, SOCS1, SP1, STAT3, TNFAIP3, UBC, and ULK) via a random forest classifier, which was differentially expressed between the AD and normal control groups. Based on the 12 hub genes, we successfully constructed a satisfactory diagnostic model for differentiating AD patients from normal controls using an artificial neural network and validated its diagnostic efficacy in several external datasets. Further, the key ferroptosis-related genes were found to be strongly correlated to immune cells infiltration in AD.

Conclusion: We successfully identified 12 ferroptosis-related genes and established a novel diagnostic model of significant predictive value for AD. These results may help understand the role of ferroptosis in AD pathogenesis and provide promising therapeutic strategies for patients with AD.

Keywords: Alzheimer’s disease; GEO; diagnosis; ferroptosis; immune infiltration.