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Identification of key candidate genes in local dorsal root ganglion inflammation by integrated bioinformatics analysis

The purpose of the present study was to identify potential markers of local dorsal root ganglion (DRG) inflammation to aid diagnosis, treatment and prognosis evaluation of DRG pain. A localized inflammation of the DRG (LID) rat model was used to study the contribution of inflammation to pain. The da...

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Autores principales: Chen, Linhai, Zheng, Junshui, Yang, Zhuan, Chen, Weiwei, Wang, Yangjian, Wei, Peng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: D.A. Spandidos 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8193217/
https://www.ncbi.nlm.nih.gov/pubmed/34131444
http://dx.doi.org/10.3892/etm.2021.10253
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author Chen, Linhai
Zheng, Junshui
Yang, Zhuan
Chen, Weiwei
Wang, Yangjian
Wei, Peng
author_facet Chen, Linhai
Zheng, Junshui
Yang, Zhuan
Chen, Weiwei
Wang, Yangjian
Wei, Peng
author_sort Chen, Linhai
collection PubMed
description The purpose of the present study was to identify potential markers of local dorsal root ganglion (DRG) inflammation to aid diagnosis, treatment and prognosis evaluation of DRG pain. A localized inflammation of the DRG (LID) rat model was used to study the contribution of inflammation to pain. The dataset GSE38859 was obtained from the Gene Expression Omnibus database. Pre-treatment standardization of gene expression data for each experiment was performed using the R/Bioconductor Limma package. Differentially expressed genes (DEGs) were identified between a LID model and a sham surgery control group. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of DEGs and gene set enrichment analysis (GSEA) were carried out using the ‘clusterProfiler’ package in R. Using the Search Tool for Retrieval of Interacting Genes, a protein-protein interaction network was constructed and visualized. Candidate genes with the highest potential validity were validated using reverse transcription-quantitative PCR and western blotting. In total, 66 DEGs were enriched in GO terms related to inflammation and the immune response processes. KEGG analysis revealed 14 associated signaling pathway terms. Protein-protein interaction network analysis revealed 9 node genes, 3 of which were among the top 10 DEGs. Matrix metallopeptidase 9, chemokine CXCL9, and complement component 3 were identified as key regulators of DRG inflammatory pain progression.
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spelling pubmed-81932172021-06-14 Identification of key candidate genes in local dorsal root ganglion inflammation by integrated bioinformatics analysis Chen, Linhai Zheng, Junshui Yang, Zhuan Chen, Weiwei Wang, Yangjian Wei, Peng Exp Ther Med Articles The purpose of the present study was to identify potential markers of local dorsal root ganglion (DRG) inflammation to aid diagnosis, treatment and prognosis evaluation of DRG pain. A localized inflammation of the DRG (LID) rat model was used to study the contribution of inflammation to pain. The dataset GSE38859 was obtained from the Gene Expression Omnibus database. Pre-treatment standardization of gene expression data for each experiment was performed using the R/Bioconductor Limma package. Differentially expressed genes (DEGs) were identified between a LID model and a sham surgery control group. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of DEGs and gene set enrichment analysis (GSEA) were carried out using the ‘clusterProfiler’ package in R. Using the Search Tool for Retrieval of Interacting Genes, a protein-protein interaction network was constructed and visualized. Candidate genes with the highest potential validity were validated using reverse transcription-quantitative PCR and western blotting. In total, 66 DEGs were enriched in GO terms related to inflammation and the immune response processes. KEGG analysis revealed 14 associated signaling pathway terms. Protein-protein interaction network analysis revealed 9 node genes, 3 of which were among the top 10 DEGs. Matrix metallopeptidase 9, chemokine CXCL9, and complement component 3 were identified as key regulators of DRG inflammatory pain progression. D.A. Spandidos 2021-08 2021-06-02 /pmc/articles/PMC8193217/ /pubmed/34131444 http://dx.doi.org/10.3892/etm.2021.10253 Text en Copyright: © Chen et al. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
spellingShingle Articles
Chen, Linhai
Zheng, Junshui
Yang, Zhuan
Chen, Weiwei
Wang, Yangjian
Wei, Peng
Identification of key candidate genes in local dorsal root ganglion inflammation by integrated bioinformatics analysis
title Identification of key candidate genes in local dorsal root ganglion inflammation by integrated bioinformatics analysis
title_full Identification of key candidate genes in local dorsal root ganglion inflammation by integrated bioinformatics analysis
title_fullStr Identification of key candidate genes in local dorsal root ganglion inflammation by integrated bioinformatics analysis
title_full_unstemmed Identification of key candidate genes in local dorsal root ganglion inflammation by integrated bioinformatics analysis
title_short Identification of key candidate genes in local dorsal root ganglion inflammation by integrated bioinformatics analysis
title_sort identification of key candidate genes in local dorsal root ganglion inflammation by integrated bioinformatics analysis
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8193217/
https://www.ncbi.nlm.nih.gov/pubmed/34131444
http://dx.doi.org/10.3892/etm.2021.10253
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