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Identification of the circRNA–miRNA–mRNA regulatory network in osteoarthritis using bioinformatics analysis
Background: Osteoarthritis (OA) is a degenerative joint disease that seriously affects the quality of people. Unfortunately, the pathogenesis of OA has not been fully known. Therefore, this study aimed to construct a ceRNA regulatory network related to OA to explore the pathogenesis of OA. Methods:...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9523487/ https://www.ncbi.nlm.nih.gov/pubmed/36186471 http://dx.doi.org/10.3389/fgene.2022.994163 |
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author | Xu, Wen-Bin Kotheeranurak, Vit Zhang, Huang-Lin Feng, Jin-Yi Liu, Jing-Wei Chen, Chien-Min Lin, Guang-Xun Rui, Gang |
author_facet | Xu, Wen-Bin Kotheeranurak, Vit Zhang, Huang-Lin Feng, Jin-Yi Liu, Jing-Wei Chen, Chien-Min Lin, Guang-Xun Rui, Gang |
author_sort | Xu, Wen-Bin |
collection | PubMed |
description | Background: Osteoarthritis (OA) is a degenerative joint disease that seriously affects the quality of people. Unfortunately, the pathogenesis of OA has not been fully known. Therefore, this study aimed to construct a ceRNA regulatory network related to OA to explore the pathogenesis of OA. Methods: Differentially expressed circRNAs (DEcircRNAs), microRNAs (DEmiRNAs), and mRNAs (DEmRNAs) were obtained from the Gene Expression Omnibus microarray data (GSE175959, GSE105027, and GSE169077). The miRNA response elements and target mRNAs were identified using bioinformatics approaches. Additionally, a circRNA–miRNA–mRNA network was established using Cytoscape version 3.8.0. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of mRNAs in the network were conducted to explore the possible mechanisms underlying OA development. Protein–protein interaction (PPI) analysis was performed to determine the hub genes. Based on the hub genes, a sub network was constructed using Cytoscape 3.8.0 version. Finally, connectivity map (CMap) and drug–gene interaction database (DGIdb) analyses were performed to identify the potential therapeutic targets for OA. Results: Altogether, five DEcircRNAs, 89 DEmiRNAs, and 345 DEmRNAs were identified. Moreover, a circRNA–miRNA–mRNA network was established using three circRNAs, seven miRNAs, and 37 mRNAs. GO and KEGG analyses demonstrated that the mRNAs in the network could be related to the occurrence and development of OA. PPI analysis was performed and six key genes, namely serpin family H member 1 [SERPINH1], collagen type VIII alpha 2 chain [COL8A2], collagen type XV alpha 1 chain [COL15A1], collagen type VI alpha 3 chain [COL6A3], collagen type V alpha 1 chain [COL5A1], and collagen type XI alpha 1 chain [COL11A1], were identified. Furthermore, a circRNA–miRNA–hub gene subnetwork was established in accordance with two circRNAs (hsa_circ_0075320 and hsa_circ_0051428), two miRNAs (hsa-miR-6124 and hsa-miR-1207-5p), and six hub genes (COL11A1, SERPINH1, COL6A3, COL5A1, COL8A2, and COL15A1). Finally, three chemicals (noscapine, diazepam, and TG100-115) based on CMap analysis and two drugs (collagenase Clostridium histolyticum and ocriplasmin) based on DGIdb were discovered as potential treatment options for OA. Conclusion: This study presents novel perspectives on the pathogenesis and treatment of OA based on circRNA-related competitive endogenous RNA regulatory networks. |
format | Online Article Text |
id | pubmed-9523487 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95234872022-10-01 Identification of the circRNA–miRNA–mRNA regulatory network in osteoarthritis using bioinformatics analysis Xu, Wen-Bin Kotheeranurak, Vit Zhang, Huang-Lin Feng, Jin-Yi Liu, Jing-Wei Chen, Chien-Min Lin, Guang-Xun Rui, Gang Front Genet Genetics Background: Osteoarthritis (OA) is a degenerative joint disease that seriously affects the quality of people. Unfortunately, the pathogenesis of OA has not been fully known. Therefore, this study aimed to construct a ceRNA regulatory network related to OA to explore the pathogenesis of OA. Methods: Differentially expressed circRNAs (DEcircRNAs), microRNAs (DEmiRNAs), and mRNAs (DEmRNAs) were obtained from the Gene Expression Omnibus microarray data (GSE175959, GSE105027, and GSE169077). The miRNA response elements and target mRNAs were identified using bioinformatics approaches. Additionally, a circRNA–miRNA–mRNA network was established using Cytoscape version 3.8.0. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of mRNAs in the network were conducted to explore the possible mechanisms underlying OA development. Protein–protein interaction (PPI) analysis was performed to determine the hub genes. Based on the hub genes, a sub network was constructed using Cytoscape 3.8.0 version. Finally, connectivity map (CMap) and drug–gene interaction database (DGIdb) analyses were performed to identify the potential therapeutic targets for OA. Results: Altogether, five DEcircRNAs, 89 DEmiRNAs, and 345 DEmRNAs were identified. Moreover, a circRNA–miRNA–mRNA network was established using three circRNAs, seven miRNAs, and 37 mRNAs. GO and KEGG analyses demonstrated that the mRNAs in the network could be related to the occurrence and development of OA. PPI analysis was performed and six key genes, namely serpin family H member 1 [SERPINH1], collagen type VIII alpha 2 chain [COL8A2], collagen type XV alpha 1 chain [COL15A1], collagen type VI alpha 3 chain [COL6A3], collagen type V alpha 1 chain [COL5A1], and collagen type XI alpha 1 chain [COL11A1], were identified. Furthermore, a circRNA–miRNA–hub gene subnetwork was established in accordance with two circRNAs (hsa_circ_0075320 and hsa_circ_0051428), two miRNAs (hsa-miR-6124 and hsa-miR-1207-5p), and six hub genes (COL11A1, SERPINH1, COL6A3, COL5A1, COL8A2, and COL15A1). Finally, three chemicals (noscapine, diazepam, and TG100-115) based on CMap analysis and two drugs (collagenase Clostridium histolyticum and ocriplasmin) based on DGIdb were discovered as potential treatment options for OA. Conclusion: This study presents novel perspectives on the pathogenesis and treatment of OA based on circRNA-related competitive endogenous RNA regulatory networks. Frontiers Media S.A. 2022-09-16 /pmc/articles/PMC9523487/ /pubmed/36186471 http://dx.doi.org/10.3389/fgene.2022.994163 Text en Copyright © 2022 Xu, Kotheeranurak, Zhang, Feng, Liu, Chen, Lin and Rui. 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 | Genetics Xu, Wen-Bin Kotheeranurak, Vit Zhang, Huang-Lin Feng, Jin-Yi Liu, Jing-Wei Chen, Chien-Min Lin, Guang-Xun Rui, Gang Identification of the circRNA–miRNA–mRNA regulatory network in osteoarthritis using bioinformatics analysis |
title | Identification of the circRNA–miRNA–mRNA regulatory network in osteoarthritis using bioinformatics analysis |
title_full | Identification of the circRNA–miRNA–mRNA regulatory network in osteoarthritis using bioinformatics analysis |
title_fullStr | Identification of the circRNA–miRNA–mRNA regulatory network in osteoarthritis using bioinformatics analysis |
title_full_unstemmed | Identification of the circRNA–miRNA–mRNA regulatory network in osteoarthritis using bioinformatics analysis |
title_short | Identification of the circRNA–miRNA–mRNA regulatory network in osteoarthritis using bioinformatics analysis |
title_sort | identification of the circrna–mirna–mrna regulatory network in osteoarthritis using bioinformatics analysis |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9523487/ https://www.ncbi.nlm.nih.gov/pubmed/36186471 http://dx.doi.org/10.3389/fgene.2022.994163 |
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