Cargando…

Identification of Diagnostic Signatures and Immune Cell Infiltration Characteristics in Rheumatoid Arthritis by Integrating Bioinformatic Analysis and Machine-Learning Strategies

BACKGROUND: Rheumatoid arthritis (RA) refers to an autoimmune rheumatic disease that imposes a huge burden on patients and society. Early RA diagnosis is critical to preventing disease progression and selecting optimal therapeutic strategies more effectively. In the present study, the aim was at exa...

Descripción completa

Detalles Bibliográficos
Autores principales: Yu, Rongguo, Zhang, Jiayu, Zhuo, Youguang, Hong, Xu, Ye, Jie, Tang, Susu, Zhang, Yiyuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8526926/
https://www.ncbi.nlm.nih.gov/pubmed/34691030
http://dx.doi.org/10.3389/fimmu.2021.724934
_version_ 1784585968764846080
author Yu, Rongguo
Zhang, Jiayu
Zhuo, Youguang
Hong, Xu
Ye, Jie
Tang, Susu
Zhang, Yiyuan
author_facet Yu, Rongguo
Zhang, Jiayu
Zhuo, Youguang
Hong, Xu
Ye, Jie
Tang, Susu
Zhang, Yiyuan
author_sort Yu, Rongguo
collection PubMed
description BACKGROUND: Rheumatoid arthritis (RA) refers to an autoimmune rheumatic disease that imposes a huge burden on patients and society. Early RA diagnosis is critical to preventing disease progression and selecting optimal therapeutic strategies more effectively. In the present study, the aim was at examining RA’s diagnostic signatures and the effect of immune cell infiltration in this pathology. METHODS: Gene Expression Omnibus (GEO) database provided three datasets of gene expressions. Firstly, this study adopted R software for identifying differentially expressed genes (DEGs) and conducting functional correlation analyses. Subsequently, we integrated bioinformatic analysis and machine-learning strategies for screening and determining RA’s diagnostic signatures and further verify by qRT-PCR. The diagnostic values were assessed through receiver operating characteristic (ROC) curves. Moreover, this study employed cell-type identification by estimating relative subsets of RNA transcript (CIBERSORT) website for assessing the inflammatory state of RA, and an investigation was conducted on the relationship of diagnostic signatures and infiltrating immune cells. RESULTS: On the whole, 54 robust DEGs received the recognition. Lymphocyte-specific protein 1 (LSP1), Granulysin (GNLY), and Mesenchymal homobox 2 (MEOX2) (AUC = 0.955) were regarded as RA’s diagnostic markers and showed their statistically significant difference by qRT-PCR. As indicated from the immune cell infiltration analysis, resting NK cells, neutrophils, activated NK cells, T cells CD8, memory B cells, and M0 macrophages may be involved in the development of RA. Additionally, all diagnostic signatures might be different degrees of correlation with immune cells. CONCLUSIONS: In conclusion, LSP1, GNLY, and MEOX2 are likely to be available in terms of diagnosing and treating RA, and the infiltration of immune cells mentioned above may critically impact RA development and occurrence.
format Online
Article
Text
id pubmed-8526926
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-85269262021-10-21 Identification of Diagnostic Signatures and Immune Cell Infiltration Characteristics in Rheumatoid Arthritis by Integrating Bioinformatic Analysis and Machine-Learning Strategies Yu, Rongguo Zhang, Jiayu Zhuo, Youguang Hong, Xu Ye, Jie Tang, Susu Zhang, Yiyuan Front Immunol Immunology BACKGROUND: Rheumatoid arthritis (RA) refers to an autoimmune rheumatic disease that imposes a huge burden on patients and society. Early RA diagnosis is critical to preventing disease progression and selecting optimal therapeutic strategies more effectively. In the present study, the aim was at examining RA’s diagnostic signatures and the effect of immune cell infiltration in this pathology. METHODS: Gene Expression Omnibus (GEO) database provided three datasets of gene expressions. Firstly, this study adopted R software for identifying differentially expressed genes (DEGs) and conducting functional correlation analyses. Subsequently, we integrated bioinformatic analysis and machine-learning strategies for screening and determining RA’s diagnostic signatures and further verify by qRT-PCR. The diagnostic values were assessed through receiver operating characteristic (ROC) curves. Moreover, this study employed cell-type identification by estimating relative subsets of RNA transcript (CIBERSORT) website for assessing the inflammatory state of RA, and an investigation was conducted on the relationship of diagnostic signatures and infiltrating immune cells. RESULTS: On the whole, 54 robust DEGs received the recognition. Lymphocyte-specific protein 1 (LSP1), Granulysin (GNLY), and Mesenchymal homobox 2 (MEOX2) (AUC = 0.955) were regarded as RA’s diagnostic markers and showed their statistically significant difference by qRT-PCR. As indicated from the immune cell infiltration analysis, resting NK cells, neutrophils, activated NK cells, T cells CD8, memory B cells, and M0 macrophages may be involved in the development of RA. Additionally, all diagnostic signatures might be different degrees of correlation with immune cells. CONCLUSIONS: In conclusion, LSP1, GNLY, and MEOX2 are likely to be available in terms of diagnosing and treating RA, and the infiltration of immune cells mentioned above may critically impact RA development and occurrence. Frontiers Media S.A. 2021-10-06 /pmc/articles/PMC8526926/ /pubmed/34691030 http://dx.doi.org/10.3389/fimmu.2021.724934 Text en Copyright © 2021 Yu, Zhang, Zhuo, Hong, Ye, Tang 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
Yu, Rongguo
Zhang, Jiayu
Zhuo, Youguang
Hong, Xu
Ye, Jie
Tang, Susu
Zhang, Yiyuan
Identification of Diagnostic Signatures and Immune Cell Infiltration Characteristics in Rheumatoid Arthritis by Integrating Bioinformatic Analysis and Machine-Learning Strategies
title Identification of Diagnostic Signatures and Immune Cell Infiltration Characteristics in Rheumatoid Arthritis by Integrating Bioinformatic Analysis and Machine-Learning Strategies
title_full Identification of Diagnostic Signatures and Immune Cell Infiltration Characteristics in Rheumatoid Arthritis by Integrating Bioinformatic Analysis and Machine-Learning Strategies
title_fullStr Identification of Diagnostic Signatures and Immune Cell Infiltration Characteristics in Rheumatoid Arthritis by Integrating Bioinformatic Analysis and Machine-Learning Strategies
title_full_unstemmed Identification of Diagnostic Signatures and Immune Cell Infiltration Characteristics in Rheumatoid Arthritis by Integrating Bioinformatic Analysis and Machine-Learning Strategies
title_short Identification of Diagnostic Signatures and Immune Cell Infiltration Characteristics in Rheumatoid Arthritis by Integrating Bioinformatic Analysis and Machine-Learning Strategies
title_sort identification of diagnostic signatures and immune cell infiltration characteristics in rheumatoid arthritis by integrating bioinformatic analysis and machine-learning strategies
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8526926/
https://www.ncbi.nlm.nih.gov/pubmed/34691030
http://dx.doi.org/10.3389/fimmu.2021.724934
work_keys_str_mv AT yurongguo identificationofdiagnosticsignaturesandimmunecellinfiltrationcharacteristicsinrheumatoidarthritisbyintegratingbioinformaticanalysisandmachinelearningstrategies
AT zhangjiayu identificationofdiagnosticsignaturesandimmunecellinfiltrationcharacteristicsinrheumatoidarthritisbyintegratingbioinformaticanalysisandmachinelearningstrategies
AT zhuoyouguang identificationofdiagnosticsignaturesandimmunecellinfiltrationcharacteristicsinrheumatoidarthritisbyintegratingbioinformaticanalysisandmachinelearningstrategies
AT hongxu identificationofdiagnosticsignaturesandimmunecellinfiltrationcharacteristicsinrheumatoidarthritisbyintegratingbioinformaticanalysisandmachinelearningstrategies
AT yejie identificationofdiagnosticsignaturesandimmunecellinfiltrationcharacteristicsinrheumatoidarthritisbyintegratingbioinformaticanalysisandmachinelearningstrategies
AT tangsusu identificationofdiagnosticsignaturesandimmunecellinfiltrationcharacteristicsinrheumatoidarthritisbyintegratingbioinformaticanalysisandmachinelearningstrategies
AT zhangyiyuan identificationofdiagnosticsignaturesandimmunecellinfiltrationcharacteristicsinrheumatoidarthritisbyintegratingbioinformaticanalysisandmachinelearningstrategies