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Investigation of candidate genes for osteoarthritis based on gene expression profiles

OBJECTIVE: To explore the mechanism of osteoarthritis (OA) and provide valid biological information for further investigation. METHODS: Gene expression profile of GSE46750 was downloaded from Gene Expression Omnibus database. The Linear Models for Microarray Data (limma) package (Bioconductor projec...

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Detalles Bibliográficos
Autores principales: Dong, Shuanghai, Xia, Tian, Wang, Lei, Zhao, Qinghua, Tian, Jiwei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Turkish Association of Orthopaedics and Traumatology 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6197383/
https://www.ncbi.nlm.nih.gov/pubmed/27866912
http://dx.doi.org/10.1016/j.aott.2016.04.002
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author Dong, Shuanghai
Xia, Tian
Wang, Lei
Zhao, Qinghua
Tian, Jiwei
author_facet Dong, Shuanghai
Xia, Tian
Wang, Lei
Zhao, Qinghua
Tian, Jiwei
author_sort Dong, Shuanghai
collection PubMed
description OBJECTIVE: To explore the mechanism of osteoarthritis (OA) and provide valid biological information for further investigation. METHODS: Gene expression profile of GSE46750 was downloaded from Gene Expression Omnibus database. The Linear Models for Microarray Data (limma) package (Bioconductor project, http://www.bioconductor.org/packages/release/bioc/html/limma.html) was used to identify differentially expressed genes (DEGs) in inflamed OA samples. Gene Ontology function enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis of DEGs were performed based on Database for Annotation, Visualization and Integrated Discovery data, and protein–protein interaction (PPI) network was constructed based on the Search Tool for the Retrieval of Interacting Genes/Proteins database. Regulatory network was screened based on Encyclopedia of DNA Elements. Molecular Complex Detection was used for sub-network screening. Two sub-networks with highest node degree were integrated with transcriptional regulatory network and KEGG functional enrichment analysis was processed for 2 modules. RESULTS: In total, 401 up- and 196 down-regulated DEGs were obtained. Up-regulated DEGs were involved in inflammatory response, while down-regulated DEGs were involved in cell cycle. PPI network with 2392 protein interactions was constructed. Moreover, 10 genes including Interleukin 6 (IL6) and Aurora B kinase (AURKB) were found to be outstanding in PPI network. There are 214 up- and 8 down-regulated transcription factor (TF)-target pairs in the TF regulatory network. Module 1 had TFs including SPI1, PRDM1, and FOS, while module 2 contained FOSL1. The nodes in module 1 were enriched in chemokine signaling pathway, while the nodes in module 2 were mainly enriched in cell cycle. CONCLUSION: The screened DEGs including IL6, AGT, and AURKB might be potential biomarkers for gene therapy for OA by being regulated by TFs such as FOS and SPI1, and participating in the cell cycle and cytokine–cytokine receptor interaction pathway.
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spelling pubmed-61973832018-10-24 Investigation of candidate genes for osteoarthritis based on gene expression profiles Dong, Shuanghai Xia, Tian Wang, Lei Zhao, Qinghua Tian, Jiwei Acta Orthop Traumatol Turc Original Article OBJECTIVE: To explore the mechanism of osteoarthritis (OA) and provide valid biological information for further investigation. METHODS: Gene expression profile of GSE46750 was downloaded from Gene Expression Omnibus database. The Linear Models for Microarray Data (limma) package (Bioconductor project, http://www.bioconductor.org/packages/release/bioc/html/limma.html) was used to identify differentially expressed genes (DEGs) in inflamed OA samples. Gene Ontology function enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis of DEGs were performed based on Database for Annotation, Visualization and Integrated Discovery data, and protein–protein interaction (PPI) network was constructed based on the Search Tool for the Retrieval of Interacting Genes/Proteins database. Regulatory network was screened based on Encyclopedia of DNA Elements. Molecular Complex Detection was used for sub-network screening. Two sub-networks with highest node degree were integrated with transcriptional regulatory network and KEGG functional enrichment analysis was processed for 2 modules. RESULTS: In total, 401 up- and 196 down-regulated DEGs were obtained. Up-regulated DEGs were involved in inflammatory response, while down-regulated DEGs were involved in cell cycle. PPI network with 2392 protein interactions was constructed. Moreover, 10 genes including Interleukin 6 (IL6) and Aurora B kinase (AURKB) were found to be outstanding in PPI network. There are 214 up- and 8 down-regulated transcription factor (TF)-target pairs in the TF regulatory network. Module 1 had TFs including SPI1, PRDM1, and FOS, while module 2 contained FOSL1. The nodes in module 1 were enriched in chemokine signaling pathway, while the nodes in module 2 were mainly enriched in cell cycle. CONCLUSION: The screened DEGs including IL6, AGT, and AURKB might be potential biomarkers for gene therapy for OA by being regulated by TFs such as FOS and SPI1, and participating in the cell cycle and cytokine–cytokine receptor interaction pathway. Turkish Association of Orthopaedics and Traumatology 2016-12 2016-11-18 /pmc/articles/PMC6197383/ /pubmed/27866912 http://dx.doi.org/10.1016/j.aott.2016.04.002 Text en © 2016 Turkish Association of Orthopaedics and Traumatology. Publishing services by Elsevier B.V. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Original Article
Dong, Shuanghai
Xia, Tian
Wang, Lei
Zhao, Qinghua
Tian, Jiwei
Investigation of candidate genes for osteoarthritis based on gene expression profiles
title Investigation of candidate genes for osteoarthritis based on gene expression profiles
title_full Investigation of candidate genes for osteoarthritis based on gene expression profiles
title_fullStr Investigation of candidate genes for osteoarthritis based on gene expression profiles
title_full_unstemmed Investigation of candidate genes for osteoarthritis based on gene expression profiles
title_short Investigation of candidate genes for osteoarthritis based on gene expression profiles
title_sort investigation of candidate genes for osteoarthritis based on gene expression profiles
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6197383/
https://www.ncbi.nlm.nih.gov/pubmed/27866912
http://dx.doi.org/10.1016/j.aott.2016.04.002
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