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Identification of Key Diagnostic Markers and Immune Infiltration in Osteoarthritis
Background: Osteoarthritis (OA) is a worldwide chronic disease of the articulating joints. An increasing body of data demonstrates the immune system's involvement in osteoarthritis. The molecular mechanisms of OA are still unclear. This study aimed to search for OA immune-related hub genes and...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Bentham Science Publishers
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10202085/ https://www.ncbi.nlm.nih.gov/pubmed/35473522 http://dx.doi.org/10.2174/1386207325666220426083526 |
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author | Yan, Mingyue Zhao, Haibo Sun, Zewen Chen, Jinli Zhang, Yi Gao, Jiake Yu, Tengbo |
author_facet | Yan, Mingyue Zhao, Haibo Sun, Zewen Chen, Jinli Zhang, Yi Gao, Jiake Yu, Tengbo |
author_sort | Yan, Mingyue |
collection | PubMed |
description | Background: Osteoarthritis (OA) is a worldwide chronic disease of the articulating joints. An increasing body of data demonstrates the immune system's involvement in osteoarthritis. The molecular mechanisms of OA are still unclear. This study aimed to search for OA immune-related hub genes and determine appropriate diagnostic markers to help the detection and treatment of the disease. Methods: Gene expression data were downloaded from the GEO database. Firstly, we analyzed and identified the differentially expressed genes(DEGs)using R packages. Meanwhile, ssGSEA was used to determine the activation degree of immune-related genes (IRGs), and WGCNA analysis was applied to search for co-expressed gene modules associated with immune cells. Then, critical networks and hub genes were found in the PPI network. Gene Ontology (GO) annotation and Kyoto Encyclopedia of Gene and Genomes (KEGG) pathway enrichment analyzed the biological functions of genes. The ability of the hub genes to differentiate OA from controls was assessed by the area under the ROC curve. A miRNA and transcription factor (TF) regulatory network was constructed according to their relationship with hub genes. Finally, the validation of hub genes was carried out by qPCR. Results: In total, 353 DEGs were identified in OA patients compared with controls, including 222 upregulated and 131 downregulated genes. WGCNA successfully identified 34 main functional modules involved in the pathogenesis of OA. The most crucial functional module involved in OA included 89 genes. 19 immune-related genes were obtained by overlapping DEGs with the darkgrey module. The String database was constructed using the protein-protein interaction (PPI) network of 19 target genes, and 7 hub genes were identified by MCODE. ROC curve showed that 7 hub genes were potential biomarkers of OA. The expression levels of hub genes were validated by qPCR, and the results were consistent with those from bioinformatic analyses. Conclusion: Immune-related hub genes, including TYROBP, ITGAM, ITGB2, C1QC, MARCO, C1QB, and TLR8, may play critical roles in OA development. ITGAM had the highest correction on immune cells. |
format | Online Article Text |
id | pubmed-10202085 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Bentham Science Publishers |
record_format | MEDLINE/PubMed |
spelling | pubmed-102020852023-05-23 Identification of Key Diagnostic Markers and Immune Infiltration in Osteoarthritis Yan, Mingyue Zhao, Haibo Sun, Zewen Chen, Jinli Zhang, Yi Gao, Jiake Yu, Tengbo Comb Chem High Throughput Screen Chemistry, Combinatorial Chemistry and High Throughput Screening Biochemical Research Methods, Chemistry, Applied Chemistry, Pharmacology Background: Osteoarthritis (OA) is a worldwide chronic disease of the articulating joints. An increasing body of data demonstrates the immune system's involvement in osteoarthritis. The molecular mechanisms of OA are still unclear. This study aimed to search for OA immune-related hub genes and determine appropriate diagnostic markers to help the detection and treatment of the disease. Methods: Gene expression data were downloaded from the GEO database. Firstly, we analyzed and identified the differentially expressed genes(DEGs)using R packages. Meanwhile, ssGSEA was used to determine the activation degree of immune-related genes (IRGs), and WGCNA analysis was applied to search for co-expressed gene modules associated with immune cells. Then, critical networks and hub genes were found in the PPI network. Gene Ontology (GO) annotation and Kyoto Encyclopedia of Gene and Genomes (KEGG) pathway enrichment analyzed the biological functions of genes. The ability of the hub genes to differentiate OA from controls was assessed by the area under the ROC curve. A miRNA and transcription factor (TF) regulatory network was constructed according to their relationship with hub genes. Finally, the validation of hub genes was carried out by qPCR. Results: In total, 353 DEGs were identified in OA patients compared with controls, including 222 upregulated and 131 downregulated genes. WGCNA successfully identified 34 main functional modules involved in the pathogenesis of OA. The most crucial functional module involved in OA included 89 genes. 19 immune-related genes were obtained by overlapping DEGs with the darkgrey module. The String database was constructed using the protein-protein interaction (PPI) network of 19 target genes, and 7 hub genes were identified by MCODE. ROC curve showed that 7 hub genes were potential biomarkers of OA. The expression levels of hub genes were validated by qPCR, and the results were consistent with those from bioinformatic analyses. Conclusion: Immune-related hub genes, including TYROBP, ITGAM, ITGB2, C1QC, MARCO, C1QB, and TLR8, may play critical roles in OA development. ITGAM had the highest correction on immune cells. Bentham Science Publishers 2023-01-05 2023-01-05 /pmc/articles/PMC10202085/ /pubmed/35473522 http://dx.doi.org/10.2174/1386207325666220426083526 Text en https://creativecommons.org/licenses/by/4.0/This is an Open Access article published under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/legalcode |
spellingShingle | Chemistry, Combinatorial Chemistry and High Throughput Screening Biochemical Research Methods, Chemistry, Applied Chemistry, Pharmacology Yan, Mingyue Zhao, Haibo Sun, Zewen Chen, Jinli Zhang, Yi Gao, Jiake Yu, Tengbo Identification of Key Diagnostic Markers and Immune Infiltration in Osteoarthritis |
title | Identification of Key Diagnostic Markers and Immune Infiltration in Osteoarthritis |
title_full | Identification of Key Diagnostic Markers and Immune Infiltration in Osteoarthritis |
title_fullStr | Identification of Key Diagnostic Markers and Immune Infiltration in Osteoarthritis |
title_full_unstemmed | Identification of Key Diagnostic Markers and Immune Infiltration in Osteoarthritis |
title_short | Identification of Key Diagnostic Markers and Immune Infiltration in Osteoarthritis |
title_sort | identification of key diagnostic markers and immune infiltration in osteoarthritis |
topic | Chemistry, Combinatorial Chemistry and High Throughput Screening Biochemical Research Methods, Chemistry, Applied Chemistry, Pharmacology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10202085/ https://www.ncbi.nlm.nih.gov/pubmed/35473522 http://dx.doi.org/10.2174/1386207325666220426083526 |
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