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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...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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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. |
format | Online Article Text |
id | pubmed-9575464 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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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