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Identification of key biomarkers and immune infiltration in systemic lupus erythematosus by integrated bioinformatics analysis

BACKGROUND: Systemic lupus erythematosus (SLE) is a multisystemic, chronic inflammatory disease characterized by destructive systemic organ involvement, which could cause the decreased functional capacity, increased morbidity and mortality. Previous studies show that SLE is characterized by autoimmu...

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Autores principales: Zhao, Xingwang, Zhang, Longlong, Wang, Juan, Zhang, Min, Song, Zhiqiang, Ni, Bing, You, Yi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7814551/
https://www.ncbi.nlm.nih.gov/pubmed/33468161
http://dx.doi.org/10.1186/s12967-020-02698-x
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author Zhao, Xingwang
Zhang, Longlong
Wang, Juan
Zhang, Min
Song, Zhiqiang
Ni, Bing
You, Yi
author_facet Zhao, Xingwang
Zhang, Longlong
Wang, Juan
Zhang, Min
Song, Zhiqiang
Ni, Bing
You, Yi
author_sort Zhao, Xingwang
collection PubMed
description BACKGROUND: Systemic lupus erythematosus (SLE) is a multisystemic, chronic inflammatory disease characterized by destructive systemic organ involvement, which could cause the decreased functional capacity, increased morbidity and mortality. Previous studies show that SLE is characterized by autoimmune, inflammatory processes, and tissue destruction. Some seriously-ill patients could develop into lupus nephritis. However, the cause and underlying molecular events of SLE needs to be further resolved. METHODS: The expression profiles of GSE144390, GSE4588, GSE50772 and GSE81622 were downloaded from the Gene Expression Omnibus (GEO) database to obtain differentially expressed genes (DEGs) between SLE and healthy samples. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments of DEGs were performed by metascape etc. online analyses. The protein–protein interaction (PPI) networks of the DEGs were constructed by GENEMANIA software. We performed Gene Set Enrichment Analysis (GSEA) to further understand the functions of the hub gene, Weighted gene co‐expression network analysis (WGCNA) would be utilized to build a gene co‐expression network, and the most significant module and hub genes was identified. CIBERSORT tools have facilitated the analysis of immune cell infiltration patterns of diseases. The receiver operating characteristic (ROC) analyses were conducted to explore the value of DEGs for SLE diagnosis. RESULTS: In total, 6 DEGs (IFI27, IFI44, IFI44L, IFI6, EPSTI1 and OAS1) were screened, Biological functions analysis identified key related pathways, gene modules and co‐expression networks in SLE. IFI27 may be closely correlated with the occurrence of SLE. We found that an increased infiltration of moncytes, while NK cells resting infiltrated less may be related to the occurrence of SLE. CONCLUSION: IFI27 may be closely related pathogenesis of SLE, and represents a new candidate molecular marker of the occurrence and progression of SLE. Moreover immune cell infiltration plays important role in the progession of SLE.
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spelling pubmed-78145512021-01-19 Identification of key biomarkers and immune infiltration in systemic lupus erythematosus by integrated bioinformatics analysis Zhao, Xingwang Zhang, Longlong Wang, Juan Zhang, Min Song, Zhiqiang Ni, Bing You, Yi J Transl Med Research BACKGROUND: Systemic lupus erythematosus (SLE) is a multisystemic, chronic inflammatory disease characterized by destructive systemic organ involvement, which could cause the decreased functional capacity, increased morbidity and mortality. Previous studies show that SLE is characterized by autoimmune, inflammatory processes, and tissue destruction. Some seriously-ill patients could develop into lupus nephritis. However, the cause and underlying molecular events of SLE needs to be further resolved. METHODS: The expression profiles of GSE144390, GSE4588, GSE50772 and GSE81622 were downloaded from the Gene Expression Omnibus (GEO) database to obtain differentially expressed genes (DEGs) between SLE and healthy samples. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments of DEGs were performed by metascape etc. online analyses. The protein–protein interaction (PPI) networks of the DEGs were constructed by GENEMANIA software. We performed Gene Set Enrichment Analysis (GSEA) to further understand the functions of the hub gene, Weighted gene co‐expression network analysis (WGCNA) would be utilized to build a gene co‐expression network, and the most significant module and hub genes was identified. CIBERSORT tools have facilitated the analysis of immune cell infiltration patterns of diseases. The receiver operating characteristic (ROC) analyses were conducted to explore the value of DEGs for SLE diagnosis. RESULTS: In total, 6 DEGs (IFI27, IFI44, IFI44L, IFI6, EPSTI1 and OAS1) were screened, Biological functions analysis identified key related pathways, gene modules and co‐expression networks in SLE. IFI27 may be closely correlated with the occurrence of SLE. We found that an increased infiltration of moncytes, while NK cells resting infiltrated less may be related to the occurrence of SLE. CONCLUSION: IFI27 may be closely related pathogenesis of SLE, and represents a new candidate molecular marker of the occurrence and progression of SLE. Moreover immune cell infiltration plays important role in the progession of SLE. BioMed Central 2021-01-19 /pmc/articles/PMC7814551/ /pubmed/33468161 http://dx.doi.org/10.1186/s12967-020-02698-x Text en © The Author(s) 2021 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/. The Creative Commons Public Domain Dedication waiver (http://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
Zhao, Xingwang
Zhang, Longlong
Wang, Juan
Zhang, Min
Song, Zhiqiang
Ni, Bing
You, Yi
Identification of key biomarkers and immune infiltration in systemic lupus erythematosus by integrated bioinformatics analysis
title Identification of key biomarkers and immune infiltration in systemic lupus erythematosus by integrated bioinformatics analysis
title_full Identification of key biomarkers and immune infiltration in systemic lupus erythematosus by integrated bioinformatics analysis
title_fullStr Identification of key biomarkers and immune infiltration in systemic lupus erythematosus by integrated bioinformatics analysis
title_full_unstemmed Identification of key biomarkers and immune infiltration in systemic lupus erythematosus by integrated bioinformatics analysis
title_short Identification of key biomarkers and immune infiltration in systemic lupus erythematosus by integrated bioinformatics analysis
title_sort identification of key biomarkers and immune infiltration in systemic lupus erythematosus by integrated bioinformatics analysis
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7814551/
https://www.ncbi.nlm.nih.gov/pubmed/33468161
http://dx.doi.org/10.1186/s12967-020-02698-x
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