Identification and immunological characterization of genes associated with ferroptosis in Alzheimer's disease and experimental demonstration

Xiao,Zhen; Hu,Rui; Liu,Wan-Lu; He,Xiao-Xuan; Dong,Ming-You; Huang,Zhong-Shi

doi:10.3892/mmr.2024.13279

Identification and immunological characterization of genes associated with ferroptosis in Alzheimer's disease and experimental demonstration

Authors:
- Zhen Xiao
- Rui Hu
- Wan-Lu Liu
- Xiao-Xuan He
- Ming-You Dong
- Zhong-Shi Huang
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Affiliations: School of Basic Medical Sciences, Youjiang Medical University for Nationalities, Baise, Guangxi 533000, P.R. China, College of Pharmacy, Guangxi University of Chinese Medicine, Qingxiu, Nanning, Guangxi 530200, P.R. China, Guangxi Key Laboratory of Molecular Pathology of Hepatobiliary Diseases, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, Guangxi 533000, P.R. China
Published online on: July 3, 2024 https://doi.org/10.3892/mmr.2024.13279
Article Number: 155
Copyright: © Xiao et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

The incidence of Alzheimer's disease (AD) is rising globally, yet its treatment and prediction of this condition remain challenging due to the complex pathophysiological mechanisms associated with it. Consequently, the objective of the present study was to analyze and characterize the molecular mechanisms underlying ferroptosis‑related genes (FEGs) in the pathogenesis of AD, as well as to construct a prognostic model. The findings will provide new insights for the future diagnosis and treatment of AD. First, the AD dataset GSE33000 from the Gene Expression Omnibus database and the FEGs from FerrDB were obtained. Next, unsupervised cluster analysis was used to obtain the FEGs that were most relevant to AD. Subsequently, enrichment analyses were performed on the FEGs to explore biological functions. Subsequently, the role of these genes in the immune microenvironment was elucidated through CIBERSORT. Then, the optimal machine learning was selected by comparing the performance of different machine learning models. To validate the prediction efficiency, the models were validated using nomograms, calibration curves, decision curve analysis and external datasets. Furthermore, the expression of FEGs between different groups was verified using reverse transcription quantitative PCR and western blot analysis. In AD, alterations in the expression of FEGs affect the aggregation and infiltration of certain immune cells. This indicated that the occurrence of AD is strongly associated with immune infiltration. Finally, the most appropriate machine learning models were selected, and AD diagnostic models and nomograms were built. The present study provided novel insights that enhance understanding with regard to the molecular mechanism of action of FEGs in AD. Moreover, the present study provided biomarkers that may facilitate the diagnosis of AD.

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