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Identification and Analysis of Neutrophil Extracellular Trap-Related Genes in Osteoarthritis by Bioinformatics and Experimental Verification

BACKGROUND: Osteoarthritis (OA) is a common joint disease with long-term pain and dysfunction that negatively affects the quality of life of patients. Neutrophil extracellular traps (NETs), consisting of DNA, proteins and cytoplasm, are released by neutrophils and play an important role in a variety...

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Autores principales: Luan, Tiankuo, Yang, Xian, Kuang, Ge, Wang, Ting, He, Jiaming, Liu, Zhibo, Gong, Xia, Wan, Jingyuan, Li, Ke
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10476866/
https://www.ncbi.nlm.nih.gov/pubmed/37671131
http://dx.doi.org/10.2147/JIR.S414452
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author Luan, Tiankuo
Yang, Xian
Kuang, Ge
Wang, Ting
He, Jiaming
Liu, Zhibo
Gong, Xia
Wan, Jingyuan
Li, Ke
author_facet Luan, Tiankuo
Yang, Xian
Kuang, Ge
Wang, Ting
He, Jiaming
Liu, Zhibo
Gong, Xia
Wan, Jingyuan
Li, Ke
author_sort Luan, Tiankuo
collection PubMed
description BACKGROUND: Osteoarthritis (OA) is a common joint disease with long-term pain and dysfunction that negatively affects the quality of life of patients. Neutrophil extracellular traps (NETs), consisting of DNA, proteins and cytoplasm, are released by neutrophils and play an important role in a variety of diseases. However, the relationship between OA and NETs is unclear. METHODS: In our study, we used bioinformatics to explore the relationship between OA and NETs and the potential biological markers. GSE55235, GSE55457, GSE117999 and GSE98918 were downloaded from the Gene Expression Omnibus (GEO) database for subsequent analysis.After differential analysis of OA expression matrices, intersection with NET-related genes (NRGs) was taken to identify Differentially expressed NRGs (DE-NRGs) in OA processes. Evaluation of immune cell infiltration by ssGSEA and CIBERSORT algorithm. The GSVA method was used to analyze the activity changes of Neutrophils pathway, Neutrophil degranulation and Neutrophil granule constituents pathway. RESULTS: Based on RandomForest (RF), Least Absolute Shrinkage and Selection Operator (LASSO), and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) learning algorithms, five core genes (CRISPLD2, IL1B, SLC25A37, MMP9, and TLR7) were identified to construct an OA-related nomogram model for predicting OA progression. ROC curve results for these genes validated the nomogram’s reliability. Correlation analysis, functional enrichment, and drug predictions were performed for the core genes. TLR7 emerged as a key focus due to its high importance ranking in RF and SVM-RFE analyses. Gene Set Enrichment Analysis (GSEA) revealed a strong association between TLR7 and the Neutrophil extracellular trap pathway. Expression of core genes was demonstrated in mice OA models and human OA samples. TLR7 expression in ATDC5 cell line was significantly higher than control after TNFα induction, along with increased IL6 and MMP13. CONCLUSION: TLR7 may be related to NETs and affects OA.
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spelling pubmed-104768662023-09-05 Identification and Analysis of Neutrophil Extracellular Trap-Related Genes in Osteoarthritis by Bioinformatics and Experimental Verification Luan, Tiankuo Yang, Xian Kuang, Ge Wang, Ting He, Jiaming Liu, Zhibo Gong, Xia Wan, Jingyuan Li, Ke J Inflamm Res Original Research BACKGROUND: Osteoarthritis (OA) is a common joint disease with long-term pain and dysfunction that negatively affects the quality of life of patients. Neutrophil extracellular traps (NETs), consisting of DNA, proteins and cytoplasm, are released by neutrophils and play an important role in a variety of diseases. However, the relationship between OA and NETs is unclear. METHODS: In our study, we used bioinformatics to explore the relationship between OA and NETs and the potential biological markers. GSE55235, GSE55457, GSE117999 and GSE98918 were downloaded from the Gene Expression Omnibus (GEO) database for subsequent analysis.After differential analysis of OA expression matrices, intersection with NET-related genes (NRGs) was taken to identify Differentially expressed NRGs (DE-NRGs) in OA processes. Evaluation of immune cell infiltration by ssGSEA and CIBERSORT algorithm. The GSVA method was used to analyze the activity changes of Neutrophils pathway, Neutrophil degranulation and Neutrophil granule constituents pathway. RESULTS: Based on RandomForest (RF), Least Absolute Shrinkage and Selection Operator (LASSO), and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) learning algorithms, five core genes (CRISPLD2, IL1B, SLC25A37, MMP9, and TLR7) were identified to construct an OA-related nomogram model for predicting OA progression. ROC curve results for these genes validated the nomogram’s reliability. Correlation analysis, functional enrichment, and drug predictions were performed for the core genes. TLR7 emerged as a key focus due to its high importance ranking in RF and SVM-RFE analyses. Gene Set Enrichment Analysis (GSEA) revealed a strong association between TLR7 and the Neutrophil extracellular trap pathway. Expression of core genes was demonstrated in mice OA models and human OA samples. TLR7 expression in ATDC5 cell line was significantly higher than control after TNFα induction, along with increased IL6 and MMP13. CONCLUSION: TLR7 may be related to NETs and affects OA. Dove 2023-08-31 /pmc/articles/PMC10476866/ /pubmed/37671131 http://dx.doi.org/10.2147/JIR.S414452 Text en © 2023 Luan et al. https://creativecommons.org/licenses/by-nc/3.0/This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/ (https://creativecommons.org/licenses/by-nc/3.0/) ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
spellingShingle Original Research
Luan, Tiankuo
Yang, Xian
Kuang, Ge
Wang, Ting
He, Jiaming
Liu, Zhibo
Gong, Xia
Wan, Jingyuan
Li, Ke
Identification and Analysis of Neutrophil Extracellular Trap-Related Genes in Osteoarthritis by Bioinformatics and Experimental Verification
title Identification and Analysis of Neutrophil Extracellular Trap-Related Genes in Osteoarthritis by Bioinformatics and Experimental Verification
title_full Identification and Analysis of Neutrophil Extracellular Trap-Related Genes in Osteoarthritis by Bioinformatics and Experimental Verification
title_fullStr Identification and Analysis of Neutrophil Extracellular Trap-Related Genes in Osteoarthritis by Bioinformatics and Experimental Verification
title_full_unstemmed Identification and Analysis of Neutrophil Extracellular Trap-Related Genes in Osteoarthritis by Bioinformatics and Experimental Verification
title_short Identification and Analysis of Neutrophil Extracellular Trap-Related Genes in Osteoarthritis by Bioinformatics and Experimental Verification
title_sort identification and analysis of neutrophil extracellular trap-related genes in osteoarthritis by bioinformatics and experimental verification
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10476866/
https://www.ncbi.nlm.nih.gov/pubmed/37671131
http://dx.doi.org/10.2147/JIR.S414452
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