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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Lippincott Williams & Wilkins
2022
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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. |
format | Online Article Text |
id | pubmed-9794287 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Lippincott Williams & Wilkins |
record_format | MEDLINE/PubMed |
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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