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Machine learning models identify ferroptosis-related genes as potential diagnostic biomarkers for Alzheimer’s disease

Alzheimer’s disease (AD) is a complex, and multifactorial neurodegenerative disease. Previous studies have revealed that oxidative stress, synaptic toxicity, autophagy, and neuroinflammation play crucial roles in the progress of AD, however, its pathogenesis is still unclear. Recent researches have...

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Autores principales: Deng, Yanyao, Feng, Yanjin, Lv, Zhicheng, He, Jinli, Chen, Xun, Wang, Chen, Yuan, Mingyang, Xu, Ting, Gao, Wenzhe, Chen, Dongjie, Zhu, Hongwei, Hou, Deren
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575464/
https://www.ncbi.nlm.nih.gov/pubmed/36262887
http://dx.doi.org/10.3389/fnagi.2022.994130
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author Deng, Yanyao
Feng, Yanjin
Lv, Zhicheng
He, Jinli
Chen, Xun
Wang, Chen
Yuan, Mingyang
Xu, Ting
Gao, Wenzhe
Chen, Dongjie
Zhu, Hongwei
Hou, Deren
author_facet Deng, Yanyao
Feng, Yanjin
Lv, Zhicheng
He, Jinli
Chen, Xun
Wang, Chen
Yuan, Mingyang
Xu, Ting
Gao, Wenzhe
Chen, Dongjie
Zhu, Hongwei
Hou, Deren
author_sort Deng, Yanyao
collection PubMed
description Alzheimer’s disease (AD) is a complex, and multifactorial neurodegenerative disease. Previous studies have revealed that oxidative stress, synaptic toxicity, autophagy, and neuroinflammation play crucial roles in the progress of AD, however, its pathogenesis is still unclear. Recent researches have indicated that ferroptosis, an iron-dependent programmed cell death, might be involved in the pathogenesis of AD. Therefore, we aim to screen correlative ferroptosis-related genes (FRGs) in the progress of AD to clarify insights into the diagnostic value. Interestingly, we identified eight FRGs were significantly differentially expressed in AD patients. 10,044 differentially expressed genes (DEGs) were finally identified by differential expression analysis. The following step was investigating the function of DEGs using gene set enrichment analysis (GSEA). Weight gene correlation analysis was performed to explore ten modules and 104 hub genes. Subsequently, based on machine learning algorithms, we constructed diagnostic classifiers to select characteristic genes. Through the multivariable logistic regression analysis, five features (RAF1, NFKBIA, MOV10L1, IQGAP1, FOXO1) were then validated, which composed a diagnostic model of AD. Thus, our findings not only developed genetic diagnostics strategy, but set a direction for further study of the disease pathogenesis and therapy targets.
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spelling pubmed-95754642022-10-18 Machine learning models identify ferroptosis-related genes as potential diagnostic biomarkers for Alzheimer’s disease Deng, Yanyao Feng, Yanjin Lv, Zhicheng He, Jinli Chen, Xun Wang, Chen Yuan, Mingyang Xu, Ting Gao, Wenzhe Chen, Dongjie Zhu, Hongwei Hou, Deren Front Aging Neurosci Neuroscience Alzheimer’s disease (AD) is a complex, and multifactorial neurodegenerative disease. Previous studies have revealed that oxidative stress, synaptic toxicity, autophagy, and neuroinflammation play crucial roles in the progress of AD, however, its pathogenesis is still unclear. Recent researches have indicated that ferroptosis, an iron-dependent programmed cell death, might be involved in the pathogenesis of AD. Therefore, we aim to screen correlative ferroptosis-related genes (FRGs) in the progress of AD to clarify insights into the diagnostic value. Interestingly, we identified eight FRGs were significantly differentially expressed in AD patients. 10,044 differentially expressed genes (DEGs) were finally identified by differential expression analysis. The following step was investigating the function of DEGs using gene set enrichment analysis (GSEA). Weight gene correlation analysis was performed to explore ten modules and 104 hub genes. Subsequently, based on machine learning algorithms, we constructed diagnostic classifiers to select characteristic genes. Through the multivariable logistic regression analysis, five features (RAF1, NFKBIA, MOV10L1, IQGAP1, FOXO1) were then validated, which composed a diagnostic model of AD. Thus, our findings not only developed genetic diagnostics strategy, but set a direction for further study of the disease pathogenesis and therapy targets. Frontiers Media S.A. 2022-09-28 /pmc/articles/PMC9575464/ /pubmed/36262887 http://dx.doi.org/10.3389/fnagi.2022.994130 Text en Copyright © 2022 Deng, Feng, Lv, He, Chen, Wang, Yuan, Xu, Gao, Chen, Zhu and Hou. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Deng, Yanyao
Feng, Yanjin
Lv, Zhicheng
He, Jinli
Chen, Xun
Wang, Chen
Yuan, Mingyang
Xu, Ting
Gao, Wenzhe
Chen, Dongjie
Zhu, Hongwei
Hou, Deren
Machine learning models identify ferroptosis-related genes as potential diagnostic biomarkers for Alzheimer’s disease
title Machine learning models identify ferroptosis-related genes as potential diagnostic biomarkers for Alzheimer’s disease
title_full Machine learning models identify ferroptosis-related genes as potential diagnostic biomarkers for Alzheimer’s disease
title_fullStr Machine learning models identify ferroptosis-related genes as potential diagnostic biomarkers for Alzheimer’s disease
title_full_unstemmed Machine learning models identify ferroptosis-related genes as potential diagnostic biomarkers for Alzheimer’s disease
title_short Machine learning models identify ferroptosis-related genes as potential diagnostic biomarkers for Alzheimer’s disease
title_sort machine learning models identify ferroptosis-related genes as potential diagnostic biomarkers for alzheimer’s disease
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575464/
https://www.ncbi.nlm.nih.gov/pubmed/36262887
http://dx.doi.org/10.3389/fnagi.2022.994130
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