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Identifying key genes in CD4(+) T cells of systemic lupus erythematosus by integrated bioinformatics analysis
Systemic lupus erythematosus (SLE) is a chronic autoimmune disease characterized by excessive activation of T and B lymphocytes and breakdown of immune tolerance to autoantigens. Despite several mechanisms including the genetic alterations and inflammatory responses have been reported, the overall s...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9420982/ https://www.ncbi.nlm.nih.gov/pubmed/36046235 http://dx.doi.org/10.3389/fgene.2022.941221 |
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author | Li, Zutong Wang, Zhilong Sun, Tian Liu, Shanshan Ding, Shuai Sun, Lingyun |
author_facet | Li, Zutong Wang, Zhilong Sun, Tian Liu, Shanshan Ding, Shuai Sun, Lingyun |
author_sort | Li, Zutong |
collection | PubMed |
description | Systemic lupus erythematosus (SLE) is a chronic autoimmune disease characterized by excessive activation of T and B lymphocytes and breakdown of immune tolerance to autoantigens. Despite several mechanisms including the genetic alterations and inflammatory responses have been reported, the overall signature genes in CD4(+) T cells and how they affect the pathological process of SLE remain to be elucidated. This study aimed to identify the crucial genes, potential biological processes and pathways underlying SLE pathogenesis by integrated bioinformatics. The gene expression profiles of isolated peripheral CD4(+) T cells from SLE patients with different disease activity and healthy controls (GSE97263) were analyzed, and 14 co-expression modules were identified using weighted gene co-expression network analysis (WGCNA). Some of these modules showed significantly positive or negative correlations with SLE disease activity, and primarily enriched in the regulation of type I interferon and immune responses. Next, combining time course sequencing (TCseq) with differentially expressed gene (DEG) analysis, crucial genes in lupus CD4(+) T cells were revealed, including some interferon signature genes (ISGs). Among these genes, we identified 4 upregulated genes (PLSCR1, IFI35, BATF2 and CLDN5) and 2 downregulated genes (GDF7 and DERL3) as newfound key genes. The elevated genes showed close relationship with the SLE disease activity. In general, our study identified 6 novel biomarkers in CD4(+) T cells that might contribute to the diagnosis and treatment of SLE. |
format | Online Article Text |
id | pubmed-9420982 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-94209822022-08-30 Identifying key genes in CD4(+) T cells of systemic lupus erythematosus by integrated bioinformatics analysis Li, Zutong Wang, Zhilong Sun, Tian Liu, Shanshan Ding, Shuai Sun, Lingyun Front Genet Genetics Systemic lupus erythematosus (SLE) is a chronic autoimmune disease characterized by excessive activation of T and B lymphocytes and breakdown of immune tolerance to autoantigens. Despite several mechanisms including the genetic alterations and inflammatory responses have been reported, the overall signature genes in CD4(+) T cells and how they affect the pathological process of SLE remain to be elucidated. This study aimed to identify the crucial genes, potential biological processes and pathways underlying SLE pathogenesis by integrated bioinformatics. The gene expression profiles of isolated peripheral CD4(+) T cells from SLE patients with different disease activity and healthy controls (GSE97263) were analyzed, and 14 co-expression modules were identified using weighted gene co-expression network analysis (WGCNA). Some of these modules showed significantly positive or negative correlations with SLE disease activity, and primarily enriched in the regulation of type I interferon and immune responses. Next, combining time course sequencing (TCseq) with differentially expressed gene (DEG) analysis, crucial genes in lupus CD4(+) T cells were revealed, including some interferon signature genes (ISGs). Among these genes, we identified 4 upregulated genes (PLSCR1, IFI35, BATF2 and CLDN5) and 2 downregulated genes (GDF7 and DERL3) as newfound key genes. The elevated genes showed close relationship with the SLE disease activity. In general, our study identified 6 novel biomarkers in CD4(+) T cells that might contribute to the diagnosis and treatment of SLE. Frontiers Media S.A. 2022-08-15 /pmc/articles/PMC9420982/ /pubmed/36046235 http://dx.doi.org/10.3389/fgene.2022.941221 Text en Copyright © 2022 Li, Wang, Sun, Liu, Ding and Sun. 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 | Genetics Li, Zutong Wang, Zhilong Sun, Tian Liu, Shanshan Ding, Shuai Sun, Lingyun Identifying key genes in CD4(+) T cells of systemic lupus erythematosus by integrated bioinformatics analysis |
title | Identifying key genes in CD4(+) T cells of systemic lupus erythematosus by integrated bioinformatics analysis |
title_full | Identifying key genes in CD4(+) T cells of systemic lupus erythematosus by integrated bioinformatics analysis |
title_fullStr | Identifying key genes in CD4(+) T cells of systemic lupus erythematosus by integrated bioinformatics analysis |
title_full_unstemmed | Identifying key genes in CD4(+) T cells of systemic lupus erythematosus by integrated bioinformatics analysis |
title_short | Identifying key genes in CD4(+) T cells of systemic lupus erythematosus by integrated bioinformatics analysis |
title_sort | identifying key genes in cd4(+) t cells of systemic lupus erythematosus by integrated bioinformatics analysis |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9420982/ https://www.ncbi.nlm.nih.gov/pubmed/36046235 http://dx.doi.org/10.3389/fgene.2022.941221 |
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