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Identifying hub circadian rhythm biomarkers and immune cell infiltration in rheumatoid arthritis
BACKGROUND: Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease with symptoms characterized by typical circadian rhythmic changes. This study aimed to identify the hub circadian rhythm genes (CRGs) in RA and explore their association with immune cell infiltration and pathogenesis of R...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9550876/ https://www.ncbi.nlm.nih.gov/pubmed/36238290 http://dx.doi.org/10.3389/fimmu.2022.1004883 |
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author | Wen, Pengfei Ma, Tao Zhang, Binfei Hao, Linjie Wang, Yakang Guo, Jianbin Song, Wei Wang, Jun Zhang, Yumin |
author_facet | Wen, Pengfei Ma, Tao Zhang, Binfei Hao, Linjie Wang, Yakang Guo, Jianbin Song, Wei Wang, Jun Zhang, Yumin |
author_sort | Wen, Pengfei |
collection | PubMed |
description | BACKGROUND: Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease with symptoms characterized by typical circadian rhythmic changes. This study aimed to identify the hub circadian rhythm genes (CRGs) in RA and explore their association with immune cell infiltration and pathogenesis of RA. METHODS: The differentially expressed CRGs (DECRGs) between RA and normal control samples were screened from Datasets GSE12021 and GSE55235. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis were used to explore the potential functional mechanisms of DECRGs in RA. Weighted Gene Co-expression Network Analysis and Least Absolute Shrinkage and Selection Operator regression analysis were performed to identify hub CRGs of RA. CIBERSORT was conducted to compare the infiltration level of immune cells in RA and control synovial tissue and their relationship with hub genes. In addition, the diagnostic value of hub biomarkers was evaluated by the area under the receiver operator characteristic curve. Further, a nomogram prediction model was constructed and its significance for clinical decision-making was evaluated. RESULTS: The green module was identified as the hub module associated with RA. Four hub CRGs (EGR1, FOSL2, GADD45B, and NFIL3) were identified and showed that they had the highest specificity and sensitivity for RA diagnosis, respectively. The expression levels and diagnostic values of these genes were externally validated in the dataset GSE55457. A nomogram prediction model based on the four hub CRGs was constructed and proved to have a certain clinical decision value. Additionally, the correlation analysis of immune cells with hub genes showed that all hub genes were significantly positively correlated with activated mast cells, resting memory CD4+ T cells, and monocytes. Whereas, all hub genes were negatively correlated with plasma cells, CD8+ T cells, and activated memory CD4+ T cells. Meanwhile, FOSL2 and GADD45B were negatively correlated with Tfh cells. CONCLUSION: Four hub CRGs were identified and showed excellent diagnostic value for RA. These genes may be involved in the pathological process of RA by disrupting the rhythmic oscillations of cytokines through immune-related pathways and could be considered molecular targets for future chronotherapy against RA. |
format | Online Article Text |
id | pubmed-9550876 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95508762022-10-12 Identifying hub circadian rhythm biomarkers and immune cell infiltration in rheumatoid arthritis Wen, Pengfei Ma, Tao Zhang, Binfei Hao, Linjie Wang, Yakang Guo, Jianbin Song, Wei Wang, Jun Zhang, Yumin Front Immunol Immunology BACKGROUND: Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease with symptoms characterized by typical circadian rhythmic changes. This study aimed to identify the hub circadian rhythm genes (CRGs) in RA and explore their association with immune cell infiltration and pathogenesis of RA. METHODS: The differentially expressed CRGs (DECRGs) between RA and normal control samples were screened from Datasets GSE12021 and GSE55235. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis were used to explore the potential functional mechanisms of DECRGs in RA. Weighted Gene Co-expression Network Analysis and Least Absolute Shrinkage and Selection Operator regression analysis were performed to identify hub CRGs of RA. CIBERSORT was conducted to compare the infiltration level of immune cells in RA and control synovial tissue and their relationship with hub genes. In addition, the diagnostic value of hub biomarkers was evaluated by the area under the receiver operator characteristic curve. Further, a nomogram prediction model was constructed and its significance for clinical decision-making was evaluated. RESULTS: The green module was identified as the hub module associated with RA. Four hub CRGs (EGR1, FOSL2, GADD45B, and NFIL3) were identified and showed that they had the highest specificity and sensitivity for RA diagnosis, respectively. The expression levels and diagnostic values of these genes were externally validated in the dataset GSE55457. A nomogram prediction model based on the four hub CRGs was constructed and proved to have a certain clinical decision value. Additionally, the correlation analysis of immune cells with hub genes showed that all hub genes were significantly positively correlated with activated mast cells, resting memory CD4+ T cells, and monocytes. Whereas, all hub genes were negatively correlated with plasma cells, CD8+ T cells, and activated memory CD4+ T cells. Meanwhile, FOSL2 and GADD45B were negatively correlated with Tfh cells. CONCLUSION: Four hub CRGs were identified and showed excellent diagnostic value for RA. These genes may be involved in the pathological process of RA by disrupting the rhythmic oscillations of cytokines through immune-related pathways and could be considered molecular targets for future chronotherapy against RA. Frontiers Media S.A. 2022-09-27 /pmc/articles/PMC9550876/ /pubmed/36238290 http://dx.doi.org/10.3389/fimmu.2022.1004883 Text en Copyright © 2022 Wen, Ma, Zhang, Hao, Wang, Guo, Song, Wang and Zhang 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 Wen, Pengfei Ma, Tao Zhang, Binfei Hao, Linjie Wang, Yakang Guo, Jianbin Song, Wei Wang, Jun Zhang, Yumin Identifying hub circadian rhythm biomarkers and immune cell infiltration in rheumatoid arthritis |
title | Identifying hub circadian rhythm biomarkers and immune cell infiltration in rheumatoid arthritis |
title_full | Identifying hub circadian rhythm biomarkers and immune cell infiltration in rheumatoid arthritis |
title_fullStr | Identifying hub circadian rhythm biomarkers and immune cell infiltration in rheumatoid arthritis |
title_full_unstemmed | Identifying hub circadian rhythm biomarkers and immune cell infiltration in rheumatoid arthritis |
title_short | Identifying hub circadian rhythm biomarkers and immune cell infiltration in rheumatoid arthritis |
title_sort | identifying hub circadian rhythm biomarkers and immune cell infiltration in rheumatoid arthritis |
topic | Immunology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9550876/ https://www.ncbi.nlm.nih.gov/pubmed/36238290 http://dx.doi.org/10.3389/fimmu.2022.1004883 |
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