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Comparison of immune cells and diagnostic markers between spondyloarthritis and rheumatoid arthritis by bioinformatics analysis

BACKGROUND: Spondyloarthritis (SpA) and rheumatoid arthritis (RA) are chronic autoimmune diseases, but they are usually difficult to distinguish in the early stage of the diseases. The purpose of this study is to explore the differences of immune mechanism and diagnostic markers through bioinformati...

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Autores principales: Wang, Jiaqian, Xue, Yuan, Zhou, Liang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9066892/
https://www.ncbi.nlm.nih.gov/pubmed/35509008
http://dx.doi.org/10.1186/s12967-022-03390-y
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author Wang, Jiaqian
Xue, Yuan
Zhou, Liang
author_facet Wang, Jiaqian
Xue, Yuan
Zhou, Liang
author_sort Wang, Jiaqian
collection PubMed
description BACKGROUND: Spondyloarthritis (SpA) and rheumatoid arthritis (RA) are chronic autoimmune diseases, but they are usually difficult to distinguish in the early stage of the diseases. The purpose of this study is to explore the differences of immune mechanism and diagnostic markers through bioinformatics analysis. METHODS: First, microarray datasets from patients with SpA, RA and normal controls were obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between groups were identified in R software. Functional and pathway enrichment of DEGs were analyzed by David database. Then, we screened the hub genes using Cytoscape plugin, and constructed the protein–protein interaction (PPI) network and heatmap of hub genes. After that, CIBERSORT was used to evaluate the differences and connections of immune cells in SpA and RA, and screened out diagnostic markers. Correlation analysis was used to analyze the relationship between immune cells and diagnostic markers. Finally, quantitative real-time polymerase chain reaction (qRT‐PCR) was used to verify the effectiveness of immunodiagnostic markers. RESULTS: We obtained three datasets, from which we can see that the functional enrichment of DEGs is mainly in cell chemotaxis, lymphocyte activation, primary immunodeficiency and other immune responses. The difference of immune cells between SpA, RA and normal control was concentrated in B, T lymphocytes cells, macrophages and dendritic cells. C19orf12 + S1PR3 is most associated with these immune cells and S1PR3 can be used as a diagnostic marker of this kind of immune diseases. In addition, MZB1 + XIST is closely related to T cells, NK cells and dendritic cells, and is expected to be used as a marker to distinguish the two diseases. CONCLUSION: Although the clinical manifestations of SpA and RA are similar, the pathogenesis is different. The screening of immune cells and diagnostic markers provides a more accurate target for the treatment of this kind of diseases. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-022-03390-y.
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spelling pubmed-90668922022-05-04 Comparison of immune cells and diagnostic markers between spondyloarthritis and rheumatoid arthritis by bioinformatics analysis Wang, Jiaqian Xue, Yuan Zhou, Liang J Transl Med Research BACKGROUND: Spondyloarthritis (SpA) and rheumatoid arthritis (RA) are chronic autoimmune diseases, but they are usually difficult to distinguish in the early stage of the diseases. The purpose of this study is to explore the differences of immune mechanism and diagnostic markers through bioinformatics analysis. METHODS: First, microarray datasets from patients with SpA, RA and normal controls were obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between groups were identified in R software. Functional and pathway enrichment of DEGs were analyzed by David database. Then, we screened the hub genes using Cytoscape plugin, and constructed the protein–protein interaction (PPI) network and heatmap of hub genes. After that, CIBERSORT was used to evaluate the differences and connections of immune cells in SpA and RA, and screened out diagnostic markers. Correlation analysis was used to analyze the relationship between immune cells and diagnostic markers. Finally, quantitative real-time polymerase chain reaction (qRT‐PCR) was used to verify the effectiveness of immunodiagnostic markers. RESULTS: We obtained three datasets, from which we can see that the functional enrichment of DEGs is mainly in cell chemotaxis, lymphocyte activation, primary immunodeficiency and other immune responses. The difference of immune cells between SpA, RA and normal control was concentrated in B, T lymphocytes cells, macrophages and dendritic cells. C19orf12 + S1PR3 is most associated with these immune cells and S1PR3 can be used as a diagnostic marker of this kind of immune diseases. In addition, MZB1 + XIST is closely related to T cells, NK cells and dendritic cells, and is expected to be used as a marker to distinguish the two diseases. CONCLUSION: Although the clinical manifestations of SpA and RA are similar, the pathogenesis is different. The screening of immune cells and diagnostic markers provides a more accurate target for the treatment of this kind of diseases. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-022-03390-y. BioMed Central 2022-05-04 /pmc/articles/PMC9066892/ /pubmed/35509008 http://dx.doi.org/10.1186/s12967-022-03390-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Wang, Jiaqian
Xue, Yuan
Zhou, Liang
Comparison of immune cells and diagnostic markers between spondyloarthritis and rheumatoid arthritis by bioinformatics analysis
title Comparison of immune cells and diagnostic markers between spondyloarthritis and rheumatoid arthritis by bioinformatics analysis
title_full Comparison of immune cells and diagnostic markers between spondyloarthritis and rheumatoid arthritis by bioinformatics analysis
title_fullStr Comparison of immune cells and diagnostic markers between spondyloarthritis and rheumatoid arthritis by bioinformatics analysis
title_full_unstemmed Comparison of immune cells and diagnostic markers between spondyloarthritis and rheumatoid arthritis by bioinformatics analysis
title_short Comparison of immune cells and diagnostic markers between spondyloarthritis and rheumatoid arthritis by bioinformatics analysis
title_sort comparison of immune cells and diagnostic markers between spondyloarthritis and rheumatoid arthritis by bioinformatics analysis
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9066892/
https://www.ncbi.nlm.nih.gov/pubmed/35509008
http://dx.doi.org/10.1186/s12967-022-03390-y
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