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Comprehensive bioinformatics analysis of acquired progesterone resistance in endometrial cancer cell line

BACKGROUND: Progesterone resistance is a problem in endometrial carcinoma, and its underlying molecular mechanisms remain poorly understood. The aim of this study was to elucidate the molecular mechanisms of progesterone resistance and to identify the key genes and pathways mediating progesterone re...

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Autores principales: Li, Wenzhi, Wang, Shufen, Qiu, Chunping, Liu, Zhiming, Zhou, Qing, Kong, Deshui, Ma, Xiaohong, Jiang, Jie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6391799/
https://www.ncbi.nlm.nih.gov/pubmed/30813939
http://dx.doi.org/10.1186/s12967-019-1814-6
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author Li, Wenzhi
Wang, Shufen
Qiu, Chunping
Liu, Zhiming
Zhou, Qing
Kong, Deshui
Ma, Xiaohong
Jiang, Jie
author_facet Li, Wenzhi
Wang, Shufen
Qiu, Chunping
Liu, Zhiming
Zhou, Qing
Kong, Deshui
Ma, Xiaohong
Jiang, Jie
author_sort Li, Wenzhi
collection PubMed
description BACKGROUND: Progesterone resistance is a problem in endometrial carcinoma, and its underlying molecular mechanisms remain poorly understood. The aim of this study was to elucidate the molecular mechanisms of progesterone resistance and to identify the key genes and pathways mediating progesterone resistance in endometrial cancer using bioinformatics analysis. METHODS: We developed a stable MPA (medroxyprogesterone acetate)-resistant endometrial cancer cell subline named IshikawaPR. Microarray analysis was used to identify differentially expressed genes (DEGs) from triplicate samples of Ishikawa and IshikawaPR cells. PANTHER, DAVID and Metascape were used to perform gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and cBioPortal for progesterone receptor (PGR) coexpression analysis. GEO microarray (GSE17025) was utilized for validation. The protein–protein interaction network (PPI) and modular analyses were performed using Metascape and Cytoscape. Further validation were performed by real-time polymerase chain reaction (RT-PCR). RESULTS: In total, 821 DEGs were found and further analyzed by GO, KEGG pathway enrichment and PPI analyses. We found that lipid metabolism, immune system and inflammation, extracellular environment-related processes and pathways accounted for a significant portion of the enriched terms. PGR coexpression analysis revealed 7 PGR coexpressed genes (ANO1, SOX17, CGNL1, DACH1, RUNDC3B, SH3YL1 and CRISPLD1) that were also dramatically changed in IshikawaPR cells. Kaplan–Meier survival statistics revealed clinical significance for 4 out of 7 target genes. Furthermore, 8 hub genes and 4 molecular complex detections (MCODEs) were identified. CONCLUSIONS: Using microarray and bioinformatics analyses, we identified DEGs and determined a comprehensive gene network of progesterone resistance. We offered several possible mechanisms of progesterone resistance and identified therapeutic and prognostic targets of progesterone resistance in endometrial cancer. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12967-019-1814-6) contains supplementary material, which is available to authorized users.
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spelling pubmed-63917992019-03-11 Comprehensive bioinformatics analysis of acquired progesterone resistance in endometrial cancer cell line Li, Wenzhi Wang, Shufen Qiu, Chunping Liu, Zhiming Zhou, Qing Kong, Deshui Ma, Xiaohong Jiang, Jie J Transl Med Research BACKGROUND: Progesterone resistance is a problem in endometrial carcinoma, and its underlying molecular mechanisms remain poorly understood. The aim of this study was to elucidate the molecular mechanisms of progesterone resistance and to identify the key genes and pathways mediating progesterone resistance in endometrial cancer using bioinformatics analysis. METHODS: We developed a stable MPA (medroxyprogesterone acetate)-resistant endometrial cancer cell subline named IshikawaPR. Microarray analysis was used to identify differentially expressed genes (DEGs) from triplicate samples of Ishikawa and IshikawaPR cells. PANTHER, DAVID and Metascape were used to perform gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and cBioPortal for progesterone receptor (PGR) coexpression analysis. GEO microarray (GSE17025) was utilized for validation. The protein–protein interaction network (PPI) and modular analyses were performed using Metascape and Cytoscape. Further validation were performed by real-time polymerase chain reaction (RT-PCR). RESULTS: In total, 821 DEGs were found and further analyzed by GO, KEGG pathway enrichment and PPI analyses. We found that lipid metabolism, immune system and inflammation, extracellular environment-related processes and pathways accounted for a significant portion of the enriched terms. PGR coexpression analysis revealed 7 PGR coexpressed genes (ANO1, SOX17, CGNL1, DACH1, RUNDC3B, SH3YL1 and CRISPLD1) that were also dramatically changed in IshikawaPR cells. Kaplan–Meier survival statistics revealed clinical significance for 4 out of 7 target genes. Furthermore, 8 hub genes and 4 molecular complex detections (MCODEs) were identified. CONCLUSIONS: Using microarray and bioinformatics analyses, we identified DEGs and determined a comprehensive gene network of progesterone resistance. We offered several possible mechanisms of progesterone resistance and identified therapeutic and prognostic targets of progesterone resistance in endometrial cancer. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12967-019-1814-6) contains supplementary material, which is available to authorized users. BioMed Central 2019-02-27 /pmc/articles/PMC6391799/ /pubmed/30813939 http://dx.doi.org/10.1186/s12967-019-1814-6 Text en © The Author(s) 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Li, Wenzhi
Wang, Shufen
Qiu, Chunping
Liu, Zhiming
Zhou, Qing
Kong, Deshui
Ma, Xiaohong
Jiang, Jie
Comprehensive bioinformatics analysis of acquired progesterone resistance in endometrial cancer cell line
title Comprehensive bioinformatics analysis of acquired progesterone resistance in endometrial cancer cell line
title_full Comprehensive bioinformatics analysis of acquired progesterone resistance in endometrial cancer cell line
title_fullStr Comprehensive bioinformatics analysis of acquired progesterone resistance in endometrial cancer cell line
title_full_unstemmed Comprehensive bioinformatics analysis of acquired progesterone resistance in endometrial cancer cell line
title_short Comprehensive bioinformatics analysis of acquired progesterone resistance in endometrial cancer cell line
title_sort comprehensive bioinformatics analysis of acquired progesterone resistance in endometrial cancer cell line
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6391799/
https://www.ncbi.nlm.nih.gov/pubmed/30813939
http://dx.doi.org/10.1186/s12967-019-1814-6
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