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Identification of hub biomarkers and immune cell infiltration characteristics of polymyositis by bioinformatics analysis

BACKGROUND: Polymyositis (PM) is an acquirable muscle disease with proximal muscle involvement of the extremities as the main manifestation; it is a category of idiopathic inflammatory myopathy. This study aimed to identify the key biomarkers of PM, while elucidating PM-associated immune cell infilt...

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Autores principales: Jia, Qi, Hao, Rui-Jin-Lin, Lu, Xiao-Jian, Sun, Shu-Qing, Shao, Jun-Jie, Su, Xing, Huang, Qing-Feng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9548705/
https://www.ncbi.nlm.nih.gov/pubmed/36225941
http://dx.doi.org/10.3389/fimmu.2022.1002500
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author Jia, Qi
Hao, Rui-Jin-Lin
Lu, Xiao-Jian
Sun, Shu-Qing
Shao, Jun-Jie
Su, Xing
Huang, Qing-Feng
author_facet Jia, Qi
Hao, Rui-Jin-Lin
Lu, Xiao-Jian
Sun, Shu-Qing
Shao, Jun-Jie
Su, Xing
Huang, Qing-Feng
author_sort Jia, Qi
collection PubMed
description BACKGROUND: Polymyositis (PM) is an acquirable muscle disease with proximal muscle involvement of the extremities as the main manifestation; it is a category of idiopathic inflammatory myopathy. This study aimed to identify the key biomarkers of PM, while elucidating PM-associated immune cell infiltration and immune-related pathways. METHODS: The gene microarray data related to PM were downloaded from the Gene Expression Omnibus database. The analyses using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes, gene set enrichment analysis (GSEA), and protein-protein interaction (PPI) networks were performed on differentially expressed genes (DEGs). The hub genes of PM were identified using weighted gene co-expression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) algorithm, and the diagnostic accuracy of hub markers for PM was assessed using the receiver operating characteristic curve. In addition, the level of infiltration of 28 immune cells in PM and their interrelationship with hub genes were analyzed using single-sample GSEA. RESULTS: A total of 420 DEGs were identified. The biological functions and signaling pathways closely associated with PM were inflammatory and immune processes. A series of four expression modules were obtained by WGCNA analysis, with the turquoise module having the highest correlation with PM; 196 crossover genes were obtained by combining DEGs. Subsequently, six hub genes were finally identified as the potential biomarkers of PM using LASSO algorithm and validation set verification analysis. In the immune cell infiltration analysis, the infiltration of T lymphocytes and subpopulations, dendritic cells, macrophages, and natural killer cells was more significant in the PM. CONCLUSION: We identified the hub genes closely related to PM using WGCNA combined with LASSO algorithm, which helped clarify the molecular mechanism of PM development and might have great significance for finding new immunotherapeutic targets, and disease prevention and treatment.
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spelling pubmed-95487052022-10-11 Identification of hub biomarkers and immune cell infiltration characteristics of polymyositis by bioinformatics analysis Jia, Qi Hao, Rui-Jin-Lin Lu, Xiao-Jian Sun, Shu-Qing Shao, Jun-Jie Su, Xing Huang, Qing-Feng Front Immunol Immunology BACKGROUND: Polymyositis (PM) is an acquirable muscle disease with proximal muscle involvement of the extremities as the main manifestation; it is a category of idiopathic inflammatory myopathy. This study aimed to identify the key biomarkers of PM, while elucidating PM-associated immune cell infiltration and immune-related pathways. METHODS: The gene microarray data related to PM were downloaded from the Gene Expression Omnibus database. The analyses using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes, gene set enrichment analysis (GSEA), and protein-protein interaction (PPI) networks were performed on differentially expressed genes (DEGs). The hub genes of PM were identified using weighted gene co-expression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) algorithm, and the diagnostic accuracy of hub markers for PM was assessed using the receiver operating characteristic curve. In addition, the level of infiltration of 28 immune cells in PM and their interrelationship with hub genes were analyzed using single-sample GSEA. RESULTS: A total of 420 DEGs were identified. The biological functions and signaling pathways closely associated with PM were inflammatory and immune processes. A series of four expression modules were obtained by WGCNA analysis, with the turquoise module having the highest correlation with PM; 196 crossover genes were obtained by combining DEGs. Subsequently, six hub genes were finally identified as the potential biomarkers of PM using LASSO algorithm and validation set verification analysis. In the immune cell infiltration analysis, the infiltration of T lymphocytes and subpopulations, dendritic cells, macrophages, and natural killer cells was more significant in the PM. CONCLUSION: We identified the hub genes closely related to PM using WGCNA combined with LASSO algorithm, which helped clarify the molecular mechanism of PM development and might have great significance for finding new immunotherapeutic targets, and disease prevention and treatment. Frontiers Media S.A. 2022-09-26 /pmc/articles/PMC9548705/ /pubmed/36225941 http://dx.doi.org/10.3389/fimmu.2022.1002500 Text en Copyright © 2022 Jia, Hao, Lu, Sun, Shao, Su and Huang 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 Immunology
Jia, Qi
Hao, Rui-Jin-Lin
Lu, Xiao-Jian
Sun, Shu-Qing
Shao, Jun-Jie
Su, Xing
Huang, Qing-Feng
Identification of hub biomarkers and immune cell infiltration characteristics of polymyositis by bioinformatics analysis
title Identification of hub biomarkers and immune cell infiltration characteristics of polymyositis by bioinformatics analysis
title_full Identification of hub biomarkers and immune cell infiltration characteristics of polymyositis by bioinformatics analysis
title_fullStr Identification of hub biomarkers and immune cell infiltration characteristics of polymyositis by bioinformatics analysis
title_full_unstemmed Identification of hub biomarkers and immune cell infiltration characteristics of polymyositis by bioinformatics analysis
title_short Identification of hub biomarkers and immune cell infiltration characteristics of polymyositis by bioinformatics analysis
title_sort identification of hub biomarkers and immune cell infiltration characteristics of polymyositis by bioinformatics analysis
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9548705/
https://www.ncbi.nlm.nih.gov/pubmed/36225941
http://dx.doi.org/10.3389/fimmu.2022.1002500
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