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Identification of ferroptosis-related gene signatures associated with multiple sclerosis using weighted gene co-expression network analysis

Multiple sclerosis (MS) is a chronic inflammatory disease of central nervous system leading to demyelination followed by neurological symptoms. Ferroptosis is a newly discovered pathogenic hallmark important for the progression of MS. However, the gene markers of ferroptosis in MS are still uncertai...

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Autores principales: Gu, Si-Chun, Yuan, Can-Xing, Gu, Chao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9794287/
https://www.ncbi.nlm.nih.gov/pubmed/36595760
http://dx.doi.org/10.1097/MD.0000000000031802
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author Gu, Si-Chun
Yuan, Can-Xing
Gu, Chao
author_facet Gu, Si-Chun
Yuan, Can-Xing
Gu, Chao
author_sort Gu, Si-Chun
collection PubMed
description Multiple sclerosis (MS) is a chronic inflammatory disease of central nervous system leading to demyelination followed by neurological symptoms. Ferroptosis is a newly discovered pathogenic hallmark important for the progression of MS. However, the gene markers of ferroptosis in MS are still uncertain. In this study, mRNA expression profiles and clinical data of MS samples were retrieved from Gene Expression Omnibus database. Weighted gene co-expression network analysis and receiver operating characteristic curve analysis were utilized to identify ferroptosis-related gene (FRG) signatures of MS. Gene set enrichment analysis and gene set variation analysis were performed to explore the biological functions of single FRG signature. HMOX1, LPCAT3 and RPL8 were firstly identified as FRG signatures of MS with the predictive capacity confirmed. Gene set enrichment analysis and gene set variation analyses revealed that metabolism-related, immune and inflammation-related, microglia-related, oxidation-related, and mitochondria-related biological functions were enriched, providing implications of the mechanisms underlying ferroptosis in MS. This study presented a systematic analysis of FRG in MS and explored the potential ferroptosis targets for new interventional strategies in MS.
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spelling pubmed-97942872022-12-28 Identification of ferroptosis-related gene signatures associated with multiple sclerosis using weighted gene co-expression network analysis Gu, Si-Chun Yuan, Can-Xing Gu, Chao Medicine (Baltimore) 5300 Multiple sclerosis (MS) is a chronic inflammatory disease of central nervous system leading to demyelination followed by neurological symptoms. Ferroptosis is a newly discovered pathogenic hallmark important for the progression of MS. However, the gene markers of ferroptosis in MS are still uncertain. In this study, mRNA expression profiles and clinical data of MS samples were retrieved from Gene Expression Omnibus database. Weighted gene co-expression network analysis and receiver operating characteristic curve analysis were utilized to identify ferroptosis-related gene (FRG) signatures of MS. Gene set enrichment analysis and gene set variation analysis were performed to explore the biological functions of single FRG signature. HMOX1, LPCAT3 and RPL8 were firstly identified as FRG signatures of MS with the predictive capacity confirmed. Gene set enrichment analysis and gene set variation analyses revealed that metabolism-related, immune and inflammation-related, microglia-related, oxidation-related, and mitochondria-related biological functions were enriched, providing implications of the mechanisms underlying ferroptosis in MS. This study presented a systematic analysis of FRG in MS and explored the potential ferroptosis targets for new interventional strategies in MS. Lippincott Williams & Wilkins 2022-12-23 /pmc/articles/PMC9794287/ /pubmed/36595760 http://dx.doi.org/10.1097/MD.0000000000031802 Text en Copyright © 2022 the Author(s). Published by Wolters Kluwer Health, Inc. https://creativecommons.org/licenses/by-nc/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC) (https://creativecommons.org/licenses/by-nc/4.0/) , where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal.
spellingShingle 5300
Gu, Si-Chun
Yuan, Can-Xing
Gu, Chao
Identification of ferroptosis-related gene signatures associated with multiple sclerosis using weighted gene co-expression network analysis
title Identification of ferroptosis-related gene signatures associated with multiple sclerosis using weighted gene co-expression network analysis
title_full Identification of ferroptosis-related gene signatures associated with multiple sclerosis using weighted gene co-expression network analysis
title_fullStr Identification of ferroptosis-related gene signatures associated with multiple sclerosis using weighted gene co-expression network analysis
title_full_unstemmed Identification of ferroptosis-related gene signatures associated with multiple sclerosis using weighted gene co-expression network analysis
title_short Identification of ferroptosis-related gene signatures associated with multiple sclerosis using weighted gene co-expression network analysis
title_sort identification of ferroptosis-related gene signatures associated with multiple sclerosis using weighted gene co-expression network analysis
topic 5300
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9794287/
https://www.ncbi.nlm.nih.gov/pubmed/36595760
http://dx.doi.org/10.1097/MD.0000000000031802
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