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A comprehensive analysis of biomarkers associated with synovitis and chondrocyte apoptosis in osteoarthritis

INTRODUCTION: Osteoarthritis (OA) is a chronic disease with high morbidity and disability rates whose molecular mechanism remains unclear. This study sought to identify OA markers associated with synovitis and cartilage apoptosis by bioinformatics analysis. METHODS: A total of five gene-expression p...

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Autores principales: Yang, Ling, Yu, Xueyuan, Liu, Meng, Cao, Yang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10401591/
https://www.ncbi.nlm.nih.gov/pubmed/37545537
http://dx.doi.org/10.3389/fimmu.2023.1149686
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author Yang, Ling
Yu, Xueyuan
Liu, Meng
Cao, Yang
author_facet Yang, Ling
Yu, Xueyuan
Liu, Meng
Cao, Yang
author_sort Yang, Ling
collection PubMed
description INTRODUCTION: Osteoarthritis (OA) is a chronic disease with high morbidity and disability rates whose molecular mechanism remains unclear. This study sought to identify OA markers associated with synovitis and cartilage apoptosis by bioinformatics analysis. METHODS: A total of five gene-expression profiles were selected from the Gene Expression Omnibus database. We combined the GEO with the GeneCards database and performed Gene Ontology and Kyoto Encyclopedia of Genes and Genome analyses; then, the least absolute shrinkage and selection operator (LASSO) algorithm was used to identify the characteristic genes, and a predictive risk score was established. We used the uniform manifold approximation and projection (UMAP) method to identify subtypes of OA patients, while the CytoHubba algorithm and GOSemSim R package were used to screen out hub genes. Next, an immunological assessment was performed using single-sample gene set enrichment analysis and CIBERSORTx. RESULTS: A total of 56OA-related differential genes were selected, and 10 characteristic genes were identified by the LASSO algorithm. OA samples were classified into cluster 1 and cluster 2 subtypes byUMAP, and the clustering results showed that the characteristic genes were significantly different between these groups. MYOC, CYP4B1, P2RY14, ADIPOQ, PLIN1, MFAP5, and LYVE1 were highly expressed in cluster 2, and ANKHLRC15, CEMIP, GPR88, CSN1S1, TAC1, and SPP1 were highly expressed in cluster 1. Protein–protein interaction network analysis showed that MMP9, COL1A, and IGF1 were high nodes, and the differential genes affected the IL-17 pathway and tumor necrosis factor pathway. The GOSemSim R package showed that ADIPOQ, COL1A, and SPP1 are closely related to the function of 31 hub genes. In addition, it was determined that mmp9 and Fos interact with multiple transcription factors, and the ssGSEA and CIBERSORTx algorithms revealed significant differences in immune infiltration between the two OA subtypes. Finally, a qPCR experiment was performed to explore the important genes in rat cartilage and synovium tissues; the qPCR results showed that COL1A and IL-17A were both highly expressed in synovitis tissues and cartilage tissues of OA rats, which is consistent with the predicted results. DISCUSSION: In the future, common therapeutic targets might be found forsimultaneous remissions of both phenotypes of OA.
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spelling pubmed-104015912023-08-05 A comprehensive analysis of biomarkers associated with synovitis and chondrocyte apoptosis in osteoarthritis Yang, Ling Yu, Xueyuan Liu, Meng Cao, Yang Front Immunol Immunology INTRODUCTION: Osteoarthritis (OA) is a chronic disease with high morbidity and disability rates whose molecular mechanism remains unclear. This study sought to identify OA markers associated with synovitis and cartilage apoptosis by bioinformatics analysis. METHODS: A total of five gene-expression profiles were selected from the Gene Expression Omnibus database. We combined the GEO with the GeneCards database and performed Gene Ontology and Kyoto Encyclopedia of Genes and Genome analyses; then, the least absolute shrinkage and selection operator (LASSO) algorithm was used to identify the characteristic genes, and a predictive risk score was established. We used the uniform manifold approximation and projection (UMAP) method to identify subtypes of OA patients, while the CytoHubba algorithm and GOSemSim R package were used to screen out hub genes. Next, an immunological assessment was performed using single-sample gene set enrichment analysis and CIBERSORTx. RESULTS: A total of 56OA-related differential genes were selected, and 10 characteristic genes were identified by the LASSO algorithm. OA samples were classified into cluster 1 and cluster 2 subtypes byUMAP, and the clustering results showed that the characteristic genes were significantly different between these groups. MYOC, CYP4B1, P2RY14, ADIPOQ, PLIN1, MFAP5, and LYVE1 were highly expressed in cluster 2, and ANKHLRC15, CEMIP, GPR88, CSN1S1, TAC1, and SPP1 were highly expressed in cluster 1. Protein–protein interaction network analysis showed that MMP9, COL1A, and IGF1 were high nodes, and the differential genes affected the IL-17 pathway and tumor necrosis factor pathway. The GOSemSim R package showed that ADIPOQ, COL1A, and SPP1 are closely related to the function of 31 hub genes. In addition, it was determined that mmp9 and Fos interact with multiple transcription factors, and the ssGSEA and CIBERSORTx algorithms revealed significant differences in immune infiltration between the two OA subtypes. Finally, a qPCR experiment was performed to explore the important genes in rat cartilage and synovium tissues; the qPCR results showed that COL1A and IL-17A were both highly expressed in synovitis tissues and cartilage tissues of OA rats, which is consistent with the predicted results. DISCUSSION: In the future, common therapeutic targets might be found forsimultaneous remissions of both phenotypes of OA. Frontiers Media S.A. 2023-07-21 /pmc/articles/PMC10401591/ /pubmed/37545537 http://dx.doi.org/10.3389/fimmu.2023.1149686 Text en Copyright © 2023 Yang, Yu, Liu and Cao 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
Yang, Ling
Yu, Xueyuan
Liu, Meng
Cao, Yang
A comprehensive analysis of biomarkers associated with synovitis and chondrocyte apoptosis in osteoarthritis
title A comprehensive analysis of biomarkers associated with synovitis and chondrocyte apoptosis in osteoarthritis
title_full A comprehensive analysis of biomarkers associated with synovitis and chondrocyte apoptosis in osteoarthritis
title_fullStr A comprehensive analysis of biomarkers associated with synovitis and chondrocyte apoptosis in osteoarthritis
title_full_unstemmed A comprehensive analysis of biomarkers associated with synovitis and chondrocyte apoptosis in osteoarthritis
title_short A comprehensive analysis of biomarkers associated with synovitis and chondrocyte apoptosis in osteoarthritis
title_sort comprehensive analysis of biomarkers associated with synovitis and chondrocyte apoptosis in osteoarthritis
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10401591/
https://www.ncbi.nlm.nih.gov/pubmed/37545537
http://dx.doi.org/10.3389/fimmu.2023.1149686
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