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Bioinformatics approach reveals the key role of C-X-C motif chemokine receptor 2 in endometriosis development

Endometriosis is a common gynecological disease, affecting 6–10% of women of reproductive age. The precise mechanisms underlying the development of endometriosis remain unclear. In the present study, a bioinformatics approach was applied to systematically identify the pathways and genes involved in...

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Autores principales: Tan, Aili, Luo, Ruoyu, Liang, Hua, Li, Mengru, Ruan, Peng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: D.A. Spandidos 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6102705/
https://www.ncbi.nlm.nih.gov/pubmed/30015967
http://dx.doi.org/10.3892/mmr.2018.9275
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author Tan, Aili
Luo, Ruoyu
Liang, Hua
Li, Mengru
Ruan, Peng
author_facet Tan, Aili
Luo, Ruoyu
Liang, Hua
Li, Mengru
Ruan, Peng
author_sort Tan, Aili
collection PubMed
description Endometriosis is a common gynecological disease, affecting 6–10% of women of reproductive age. The precise mechanisms underlying the development of endometriosis remain unclear. In the present study, a bioinformatics approach was applied to systematically identify the pathways and genes involved in the development of endometriosis and to discover potential biomarkers. The gene expression profiles of GSE6364, a microarray dataset of endometrial biopsies obtained from women with or without endometriosis, was downloaded from the Gene Expression Omnibus DataSets database that stores original submitter-supplied records (series, samples and platforms), as well as curated datasets. Differentially expressed gene (DEG) analysis was performed with GEO2R. DAVID was used to analyze the gene ontology enrichment of the DEGs. Gene Set Enrichment Analysis (GSEA) was conducted using the GSEA v3.0 software. Protein-protein interactions (PPI) were evaluated with the Search Tool for the Retrieval of Interacting Genes, and PPI network visualization was performed with Cytoscape. In addition, Cell Counting kit-8 and Transwell assays were performed on human endometrial stromal cells (HESCs). A total of 172 DEGs were extracted. Inflammatory response genes were significantly upregulated in the endometriosis tissues and C-X-C motif chemokine receptor 2 (CXCR2), was one of the most up-regulated genes according to DEG analysis. Cell-based experiments confirmed that CXCR2 promoted the proliferation, migration and invasion of HESCs. In conclusion, a bioinformatics approach combined with in vitro experiments in the present study revealed that CXCR2 may be associated with the development of endometriosis and has potential as a biomarker for the diagnosis of endometriosis.
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spelling pubmed-61027052018-08-23 Bioinformatics approach reveals the key role of C-X-C motif chemokine receptor 2 in endometriosis development Tan, Aili Luo, Ruoyu Liang, Hua Li, Mengru Ruan, Peng Mol Med Rep Articles Endometriosis is a common gynecological disease, affecting 6–10% of women of reproductive age. The precise mechanisms underlying the development of endometriosis remain unclear. In the present study, a bioinformatics approach was applied to systematically identify the pathways and genes involved in the development of endometriosis and to discover potential biomarkers. The gene expression profiles of GSE6364, a microarray dataset of endometrial biopsies obtained from women with or without endometriosis, was downloaded from the Gene Expression Omnibus DataSets database that stores original submitter-supplied records (series, samples and platforms), as well as curated datasets. Differentially expressed gene (DEG) analysis was performed with GEO2R. DAVID was used to analyze the gene ontology enrichment of the DEGs. Gene Set Enrichment Analysis (GSEA) was conducted using the GSEA v3.0 software. Protein-protein interactions (PPI) were evaluated with the Search Tool for the Retrieval of Interacting Genes, and PPI network visualization was performed with Cytoscape. In addition, Cell Counting kit-8 and Transwell assays were performed on human endometrial stromal cells (HESCs). A total of 172 DEGs were extracted. Inflammatory response genes were significantly upregulated in the endometriosis tissues and C-X-C motif chemokine receptor 2 (CXCR2), was one of the most up-regulated genes according to DEG analysis. Cell-based experiments confirmed that CXCR2 promoted the proliferation, migration and invasion of HESCs. In conclusion, a bioinformatics approach combined with in vitro experiments in the present study revealed that CXCR2 may be associated with the development of endometriosis and has potential as a biomarker for the diagnosis of endometriosis. D.A. Spandidos 2018-09 2018-07-12 /pmc/articles/PMC6102705/ /pubmed/30015967 http://dx.doi.org/10.3892/mmr.2018.9275 Text en Copyright: © Tan et al. 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
Tan, Aili
Luo, Ruoyu
Liang, Hua
Li, Mengru
Ruan, Peng
Bioinformatics approach reveals the key role of C-X-C motif chemokine receptor 2 in endometriosis development
title Bioinformatics approach reveals the key role of C-X-C motif chemokine receptor 2 in endometriosis development
title_full Bioinformatics approach reveals the key role of C-X-C motif chemokine receptor 2 in endometriosis development
title_fullStr Bioinformatics approach reveals the key role of C-X-C motif chemokine receptor 2 in endometriosis development
title_full_unstemmed Bioinformatics approach reveals the key role of C-X-C motif chemokine receptor 2 in endometriosis development
title_short Bioinformatics approach reveals the key role of C-X-C motif chemokine receptor 2 in endometriosis development
title_sort bioinformatics approach reveals the key role of c-x-c motif chemokine receptor 2 in endometriosis development
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6102705/
https://www.ncbi.nlm.nih.gov/pubmed/30015967
http://dx.doi.org/10.3892/mmr.2018.9275
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