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Identification of differentially expressed genes, signaling pathways and immune infiltration in rheumatoid arthritis by integrated bioinformatics analysis

BACKGROUND: The disability rate associated with rheumatoid arthritis (RA) ranks high among inflammatory joint diseases. However, the cause and potential molecular events are as yet not clear. Here, we aimed to identify differentially expressed genes (DEGs), pathways and immune infiltration involved...

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Autores principales: Ge, Yanzhi, Zhou, Li, Chen, Zuxiang, Mao, Yingying, Li, Ting, Tong, Peijian, Shan, Letian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7784358/
https://www.ncbi.nlm.nih.gov/pubmed/33397492
http://dx.doi.org/10.1186/s41065-020-00169-3
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author Ge, Yanzhi
Zhou, Li
Chen, Zuxiang
Mao, Yingying
Li, Ting
Tong, Peijian
Shan, Letian
author_facet Ge, Yanzhi
Zhou, Li
Chen, Zuxiang
Mao, Yingying
Li, Ting
Tong, Peijian
Shan, Letian
author_sort Ge, Yanzhi
collection PubMed
description BACKGROUND: The disability rate associated with rheumatoid arthritis (RA) ranks high among inflammatory joint diseases. However, the cause and potential molecular events are as yet not clear. Here, we aimed to identify differentially expressed genes (DEGs), pathways and immune infiltration involved in RA utilizing integrated bioinformatics analysis and investigating potential molecular mechanisms. MATERIALS AND METHODS: The expression profiles of GSE55235, GSE55457, GSE55584 and GSE77298 were downloaded from the Gene Expression Omnibus database, which contained 76 synovial membrane samples, including 49 RA samples and 27 normal controls. The microarray datasets were consolidated and DEGs were acquired and further analyzed by bioinformatics techniques. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of DEGs were performed using R (version 3.6.1) software, respectively. The protein-protein interaction (PPI) network of DEGs were developed utilizing the STRING database. Finally, the CIBERSORT was used to evaluate the infiltration of immune cells in RA. RESULTS: A total of 828 DEGs were recognized, with 758 up-regulated and 70 down-regulated. GO and KEGG pathway analyses demonstrated that these DEGs focused primarily on cytokine receptor activity and relevant signaling pathways. The 30 most firmly related genes among DEGs were identified from the PPI network. The principal component analysis showed that there was a significant difference between the two tissues in infiltration immune. CONCLUSION: This study shows that screening for DEGs, pathways and immune infiltration utilizing integrated bioinformatics analyses could aid in the comprehension of the molecular mechanisms involved in RA development. Besides, our study provides valuable data related to DEGs, pathways and immune infiltration of RA and may provide new insight into the understanding of molecular mechanisms.
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spelling pubmed-77843582021-01-14 Identification of differentially expressed genes, signaling pathways and immune infiltration in rheumatoid arthritis by integrated bioinformatics analysis Ge, Yanzhi Zhou, Li Chen, Zuxiang Mao, Yingying Li, Ting Tong, Peijian Shan, Letian Hereditas Research BACKGROUND: The disability rate associated with rheumatoid arthritis (RA) ranks high among inflammatory joint diseases. However, the cause and potential molecular events are as yet not clear. Here, we aimed to identify differentially expressed genes (DEGs), pathways and immune infiltration involved in RA utilizing integrated bioinformatics analysis and investigating potential molecular mechanisms. MATERIALS AND METHODS: The expression profiles of GSE55235, GSE55457, GSE55584 and GSE77298 were downloaded from the Gene Expression Omnibus database, which contained 76 synovial membrane samples, including 49 RA samples and 27 normal controls. The microarray datasets were consolidated and DEGs were acquired and further analyzed by bioinformatics techniques. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of DEGs were performed using R (version 3.6.1) software, respectively. The protein-protein interaction (PPI) network of DEGs were developed utilizing the STRING database. Finally, the CIBERSORT was used to evaluate the infiltration of immune cells in RA. RESULTS: A total of 828 DEGs were recognized, with 758 up-regulated and 70 down-regulated. GO and KEGG pathway analyses demonstrated that these DEGs focused primarily on cytokine receptor activity and relevant signaling pathways. The 30 most firmly related genes among DEGs were identified from the PPI network. The principal component analysis showed that there was a significant difference between the two tissues in infiltration immune. CONCLUSION: This study shows that screening for DEGs, pathways and immune infiltration utilizing integrated bioinformatics analyses could aid in the comprehension of the molecular mechanisms involved in RA development. Besides, our study provides valuable data related to DEGs, pathways and immune infiltration of RA and may provide new insight into the understanding of molecular mechanisms. BioMed Central 2021-01-04 /pmc/articles/PMC7784358/ /pubmed/33397492 http://dx.doi.org/10.1186/s41065-020-00169-3 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
Ge, Yanzhi
Zhou, Li
Chen, Zuxiang
Mao, Yingying
Li, Ting
Tong, Peijian
Shan, Letian
Identification of differentially expressed genes, signaling pathways and immune infiltration in rheumatoid arthritis by integrated bioinformatics analysis
title Identification of differentially expressed genes, signaling pathways and immune infiltration in rheumatoid arthritis by integrated bioinformatics analysis
title_full Identification of differentially expressed genes, signaling pathways and immune infiltration in rheumatoid arthritis by integrated bioinformatics analysis
title_fullStr Identification of differentially expressed genes, signaling pathways and immune infiltration in rheumatoid arthritis by integrated bioinformatics analysis
title_full_unstemmed Identification of differentially expressed genes, signaling pathways and immune infiltration in rheumatoid arthritis by integrated bioinformatics analysis
title_short Identification of differentially expressed genes, signaling pathways and immune infiltration in rheumatoid arthritis by integrated bioinformatics analysis
title_sort identification of differentially expressed genes, signaling pathways and immune infiltration in rheumatoid arthritis by integrated bioinformatics analysis
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7784358/
https://www.ncbi.nlm.nih.gov/pubmed/33397492
http://dx.doi.org/10.1186/s41065-020-00169-3
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