AUTHOR=Gu Xuefeng , Lai Donglin , Liu Shuang , Chen Kaijie , Zhang Peng , Chen Bing , Huang Gang , Cheng Xiaoqin , Lu Changlian TITLE=Hub Genes, Diagnostic Model, and Predicted Drugs Related to Iron Metabolism in Alzheimer's Disease JOURNAL=Frontiers in Aging Neuroscience VOLUME=14 YEAR=2022 URL=https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2022.949083 DOI=10.3389/fnagi.2022.949083 ISSN=1663-4365 ABSTRACT=

Alzheimer's disease (AD), the most common neurodegenerative disease, remains unclear in terms of its underlying causative genes and effective therapeutic approaches. Meanwhile, abnormalities in iron metabolism have been demonstrated in patients and mouse models with AD. Therefore, this study sought to find hub genes based on iron metabolism that can influence the diagnosis and treatment of AD. First, gene expression profiles were downloaded from the GEO database, including non-demented (ND) controls and AD samples. Fourteen iron metabolism-related gene sets were downloaded from the MSigDB database, yielding 520 iron metabolism-related genes. The final nine hub genes associated with iron metabolism and AD were obtained by differential analysis and WGCNA in brain tissue samples from GSE132903. GO analysis revealed that these genes were mainly involved in two major biological processes, autophagy and iron metabolism. Through stepwise regression and logistic regression analyses, we selected four of these genes to construct a diagnostic model of AD. The model was validated in blood samples from GSE63061 and GSE85426, and the AUC values showed that the model had a relatively good diagnostic performance. In addition, the immune cell infiltration of the samples and the correlation of different immune factors with these hub genes were further explored. The results suggested that these genes may also play an important role in immunity to AD. Finally, eight drugs targeting these nine hub genes were retrieved from the DrugBank database, some of which were shown to be useful for the treatment of AD or other concomitant conditions, such as insomnia and agitation. In conclusion, this model is expected to guide the diagnosis of patients with AD by detecting the expression of several genes in the blood. These hub genes may also assist in understanding the development and drug treatment of AD.