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Significance of pyroptosis-related gene in the diagnosis and classification of rheumatoid arthritis

BACKGROUND: Rheumatoid arthritis (RA), a chronic autoimmune inflammatory disease, is often characterized by persistent morning stiffness, joint pain, and swelling. Early diagnosis and timely treatment of RA can effectively delay the progression of the condition and significantly reduce the incidence...

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Autores principales: Li, Jian, Cui, Yongfeng, Jin, Xin, Ruan, Hongfeng, He, Dongan, Che, Xiaoqian, Gao, Jiawei, Zhang, Haiming, Guo, Jiandong, Zhang, Jinxi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10057543/
https://www.ncbi.nlm.nih.gov/pubmed/37008939
http://dx.doi.org/10.3389/fendo.2023.1144250
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author Li, Jian
Cui, Yongfeng
Jin, Xin
Ruan, Hongfeng
He, Dongan
Che, Xiaoqian
Gao, Jiawei
Zhang, Haiming
Guo, Jiandong
Zhang, Jinxi
author_facet Li, Jian
Cui, Yongfeng
Jin, Xin
Ruan, Hongfeng
He, Dongan
Che, Xiaoqian
Gao, Jiawei
Zhang, Haiming
Guo, Jiandong
Zhang, Jinxi
author_sort Li, Jian
collection PubMed
description BACKGROUND: Rheumatoid arthritis (RA), a chronic autoimmune inflammatory disease, is often characterized by persistent morning stiffness, joint pain, and swelling. Early diagnosis and timely treatment of RA can effectively delay the progression of the condition and significantly reduce the incidence of disability. In the study, we explored the function of pyroptosis-related genes (PRGs) in the diagnosis and classification of rheumatoid arthritis based on Gene Expression Omnibus (GEO) datasets. METHOD: We downloaded the GSE93272 dataset from the GEO database, which contains 35 healthy controls and 67 RA patients. Firstly, the GSE93272 was normalized by the R software “limma” package. Then, we screened PRGs by SVM-RFE, LASSO, and RF algorithms. To further investigate the prevalence of RA, we established a nomogram model. Besides, we grouped gene expression profiles into two clusters and explored their relationship with infiltrating immune cells. Finally, we analyzed the relationship between the two clusters and the cytokines. RESULT: CHMP3, TP53, AIM2, NLRP1, and PLCG1 were identified as PRGs. The nomogram model revealed that decision-making based on established model might be beneficial for RA patients, and the predictive power of the nomogram model was significant. In addition, we identified two different pyroptosis patterns (pyroptosis clusters A and B) based on the 5 PRGs. We found that eosinophil, gamma delta T cell, macrophage, natural killer cell, regulatory T cell, type 17 T helper cell, and type 2 T helper cell were significant high expressed in cluster B. And, we identified gene clusters A and B based on 56 differentially expressed genes (DEGs) between pyroptosis cluster A and B. And we calculated the pyroptosis score for each sample to quantify the different patterns. The patients in pyroptosis cluster B or gene cluster B had higher pyroptosis scores than those in pyroptosis cluster A or gene cluster A. CONCLUSION: In summary, PRGs play vital roles in the development and occurrence of RA. Our findings might provide novel views for the immunotherapy strategies with RA.
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spelling pubmed-100575432023-03-30 Significance of pyroptosis-related gene in the diagnosis and classification of rheumatoid arthritis Li, Jian Cui, Yongfeng Jin, Xin Ruan, Hongfeng He, Dongan Che, Xiaoqian Gao, Jiawei Zhang, Haiming Guo, Jiandong Zhang, Jinxi Front Endocrinol (Lausanne) Endocrinology BACKGROUND: Rheumatoid arthritis (RA), a chronic autoimmune inflammatory disease, is often characterized by persistent morning stiffness, joint pain, and swelling. Early diagnosis and timely treatment of RA can effectively delay the progression of the condition and significantly reduce the incidence of disability. In the study, we explored the function of pyroptosis-related genes (PRGs) in the diagnosis and classification of rheumatoid arthritis based on Gene Expression Omnibus (GEO) datasets. METHOD: We downloaded the GSE93272 dataset from the GEO database, which contains 35 healthy controls and 67 RA patients. Firstly, the GSE93272 was normalized by the R software “limma” package. Then, we screened PRGs by SVM-RFE, LASSO, and RF algorithms. To further investigate the prevalence of RA, we established a nomogram model. Besides, we grouped gene expression profiles into two clusters and explored their relationship with infiltrating immune cells. Finally, we analyzed the relationship between the two clusters and the cytokines. RESULT: CHMP3, TP53, AIM2, NLRP1, and PLCG1 were identified as PRGs. The nomogram model revealed that decision-making based on established model might be beneficial for RA patients, and the predictive power of the nomogram model was significant. In addition, we identified two different pyroptosis patterns (pyroptosis clusters A and B) based on the 5 PRGs. We found that eosinophil, gamma delta T cell, macrophage, natural killer cell, regulatory T cell, type 17 T helper cell, and type 2 T helper cell were significant high expressed in cluster B. And, we identified gene clusters A and B based on 56 differentially expressed genes (DEGs) between pyroptosis cluster A and B. And we calculated the pyroptosis score for each sample to quantify the different patterns. The patients in pyroptosis cluster B or gene cluster B had higher pyroptosis scores than those in pyroptosis cluster A or gene cluster A. CONCLUSION: In summary, PRGs play vital roles in the development and occurrence of RA. Our findings might provide novel views for the immunotherapy strategies with RA. Frontiers Media S.A. 2023-03-15 /pmc/articles/PMC10057543/ /pubmed/37008939 http://dx.doi.org/10.3389/fendo.2023.1144250 Text en Copyright © 2023 Li, Cui, Jin, Ruan, He, Che, Gao, Zhang, Guo 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 Endocrinology
Li, Jian
Cui, Yongfeng
Jin, Xin
Ruan, Hongfeng
He, Dongan
Che, Xiaoqian
Gao, Jiawei
Zhang, Haiming
Guo, Jiandong
Zhang, Jinxi
Significance of pyroptosis-related gene in the diagnosis and classification of rheumatoid arthritis
title Significance of pyroptosis-related gene in the diagnosis and classification of rheumatoid arthritis
title_full Significance of pyroptosis-related gene in the diagnosis and classification of rheumatoid arthritis
title_fullStr Significance of pyroptosis-related gene in the diagnosis and classification of rheumatoid arthritis
title_full_unstemmed Significance of pyroptosis-related gene in the diagnosis and classification of rheumatoid arthritis
title_short Significance of pyroptosis-related gene in the diagnosis and classification of rheumatoid arthritis
title_sort significance of pyroptosis-related gene in the diagnosis and classification of rheumatoid arthritis
topic Endocrinology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10057543/
https://www.ncbi.nlm.nih.gov/pubmed/37008939
http://dx.doi.org/10.3389/fendo.2023.1144250
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