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Five genes as diagnostic biomarkers of dermatomyositis and their correlation with immune cell infiltration
BACKGROUND: Dermatomyositis (DM) is a rare autoimmune disease characterized by severe muscle dysfunction, and the immune response of the muscles plays an important role in the development of DM. Currently, the diagnosis of DM relies on symptoms, physical examination, and biopsy techniques. Therefore...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9889851/ https://www.ncbi.nlm.nih.gov/pubmed/36742332 http://dx.doi.org/10.3389/fimmu.2023.1053099 |
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author | Zhao, Xiaohu Si, Shangkun |
author_facet | Zhao, Xiaohu Si, Shangkun |
author_sort | Zhao, Xiaohu |
collection | PubMed |
description | BACKGROUND: Dermatomyositis (DM) is a rare autoimmune disease characterized by severe muscle dysfunction, and the immune response of the muscles plays an important role in the development of DM. Currently, the diagnosis of DM relies on symptoms, physical examination, and biopsy techniques. Therefore, we used machine learning algorithm to screen key genes, and constructed and verified a diagnostic model composed of 5 key genes. In terms of immunity, The relationship between 5 genes and immune cell infiltration in muscle samples was analyzed. These diagnostic and immune-cell-related genes may contribute to the diagnosis and treatment of DM. METHODS: GSE5370 and GSE128470 datasets were utilised from the Gene Expression Omnibus database as DM test sets. And we also used R software to merge two datasets and to analyze the results of differentially expressed genes (DEGs) and functional correlation analysis. Then, we could detect diagnostic genes adopting least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine recursive feature elimination (SVM-RFE) analyses. The validity of putative biomarkers was assessed using the GSE1551 dataset, and we confirmed the area under the receiver operating characteristic curve (AUC) values. Finally, CIBERSORT was used to evaluate immune cell infiltration in DM muscles and the correlations between disease-related biomarkers and immune cells. RESULTS: In this study, a total of 414 DEGs were screened. ISG15, TNFRSF1A, GUSBP11, SERPINB1 and PTMA were identified as potential DM diagnostic biomarkers(AUC > 0.85),and the expressions of 5 genes in DM group were higher than that in healthy group (p < 0.05). Immune cell infiltration analyses indicated that identified DM diagnostic biomarkers may be associated with M1 macrophages, activated NK cells, Tfh cells, resting NK cells and Treg cells. CONCLUSION: The study identified that ISG15, TNFRSF1A, GUSBP11, SERPINB1 and PTMA as potential diagnostic biomarkers of DM and these genes were closely correlated with immune cell infiltration.This will contribute to future studies in diagnosis and treatment of DM. |
format | Online Article Text |
id | pubmed-9889851 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-98898512023-02-02 Five genes as diagnostic biomarkers of dermatomyositis and their correlation with immune cell infiltration Zhao, Xiaohu Si, Shangkun Front Immunol Immunology BACKGROUND: Dermatomyositis (DM) is a rare autoimmune disease characterized by severe muscle dysfunction, and the immune response of the muscles plays an important role in the development of DM. Currently, the diagnosis of DM relies on symptoms, physical examination, and biopsy techniques. Therefore, we used machine learning algorithm to screen key genes, and constructed and verified a diagnostic model composed of 5 key genes. In terms of immunity, The relationship between 5 genes and immune cell infiltration in muscle samples was analyzed. These diagnostic and immune-cell-related genes may contribute to the diagnosis and treatment of DM. METHODS: GSE5370 and GSE128470 datasets were utilised from the Gene Expression Omnibus database as DM test sets. And we also used R software to merge two datasets and to analyze the results of differentially expressed genes (DEGs) and functional correlation analysis. Then, we could detect diagnostic genes adopting least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine recursive feature elimination (SVM-RFE) analyses. The validity of putative biomarkers was assessed using the GSE1551 dataset, and we confirmed the area under the receiver operating characteristic curve (AUC) values. Finally, CIBERSORT was used to evaluate immune cell infiltration in DM muscles and the correlations between disease-related biomarkers and immune cells. RESULTS: In this study, a total of 414 DEGs were screened. ISG15, TNFRSF1A, GUSBP11, SERPINB1 and PTMA were identified as potential DM diagnostic biomarkers(AUC > 0.85),and the expressions of 5 genes in DM group were higher than that in healthy group (p < 0.05). Immune cell infiltration analyses indicated that identified DM diagnostic biomarkers may be associated with M1 macrophages, activated NK cells, Tfh cells, resting NK cells and Treg cells. CONCLUSION: The study identified that ISG15, TNFRSF1A, GUSBP11, SERPINB1 and PTMA as potential diagnostic biomarkers of DM and these genes were closely correlated with immune cell infiltration.This will contribute to future studies in diagnosis and treatment of DM. Frontiers Media S.A. 2023-01-18 /pmc/articles/PMC9889851/ /pubmed/36742332 http://dx.doi.org/10.3389/fimmu.2023.1053099 Text en Copyright © 2023 Zhao and Si 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 Zhao, Xiaohu Si, Shangkun Five genes as diagnostic biomarkers of dermatomyositis and their correlation with immune cell infiltration |
title | Five genes as diagnostic biomarkers of dermatomyositis and their correlation with immune cell infiltration |
title_full | Five genes as diagnostic biomarkers of dermatomyositis and their correlation with immune cell infiltration |
title_fullStr | Five genes as diagnostic biomarkers of dermatomyositis and their correlation with immune cell infiltration |
title_full_unstemmed | Five genes as diagnostic biomarkers of dermatomyositis and their correlation with immune cell infiltration |
title_short | Five genes as diagnostic biomarkers of dermatomyositis and their correlation with immune cell infiltration |
title_sort | five genes as diagnostic biomarkers of dermatomyositis and their correlation with immune cell infiltration |
topic | Immunology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9889851/ https://www.ncbi.nlm.nih.gov/pubmed/36742332 http://dx.doi.org/10.3389/fimmu.2023.1053099 |
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