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Journal of Translational Medicine

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Open Access 01-12-2019 | Endometrial Cancer | Research

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

Authors: Wenzhi Li, Shufen Wang, Chunping Qiu, Zhiming Liu, Qing Zhou, Deshui Kong, Xiaohong Ma, Jie Jiang

Published in: Journal of Translational Medicine | Issue 1/2019

Abstract

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.

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Title: Comprehensive bioinformatics analysis of acquired progesterone resistance in endometrial cancer cell line
Authors: Wenzhi Li
Shufen Wang
Chunping Qiu
Zhiming Liu
Qing Zhou
Deshui Kong
Xiaohong Ma
Jie Jiang
Publication date: 01-12-2019
Publisher: BioMed Central
Keywords: Endometrial Cancer
Endometrial Cancer
Progesterone
Medroxyprogesterone Acetate
Published in: Journal of Translational Medicine / Issue 1/2019
Electronic ISSN: 1479-5876
DOI: https://doi.org/10.1186/s12967-019-1814-6

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