Comprehensive bioinformatics analysis of acquired progesterone resistance in endometrial cancer cell line
Comprehensive bioinformatics analysis of acquired progesterone resistance in endometrial cancer cell line
复制标题
子宫内膜癌细胞系获得性黄体酮耐药的综合生物信息学分析
DOI：
10.1186/s12967-019-1814-6
复制
发表时间：
2019-02-27
影响因子：
7.4
通讯作者：
Jiang, Jie
中科院分区：
文献类型：
--
作者：
Li, Wenzhi;Wang, Shufen;Jiang, Jie
背景孕酮的耐药性是子宫内膜癌的一个问题，其潜在的分子机制仍然了解不足。这项研究的目的是阐明孕激素耐药性的分子机制，并使用生物信息信息分析鉴定子宫内膜癌中介导孕酮耐药性的关键基因和途径。Methodswe开发了一种稳定的MPA（Medroxyroxyprogesterone乙酸） - 抗性子宫内膜癌细胞subline ishikikawapr。微阵列分析用于从Ishikawa和Ishikawapr细胞的一式三份样品中鉴定出差异表达的基因（DEG）。 Panther，David和Metascape用于执行基因和基因组（KEGG）途径富集分析的基因本体论（GO），孕酮受体（PGR）共表达分析的CBIOPOPTAL。 Geo微阵列（GSE17025）用于验证。蛋白质 - 蛋白质相互作用网络（PPI）和模块化分析是使用Metascape和Cytoscape进行的。通过实时聚合酶链反应（RT-PCR）进行进一步的验证。总计总计，发现了821度，并通过GO，KEGG途径富集和PPI分析进一步分析。我们发现脂质代谢，免疫系统和炎症，细胞外环境相关的过程和途径占富集术语的很大一部分。 PGR共表达分析揭示了7个PGR共表达基因（ANO1，SOX17，CGNL1，DACH1，RUNDC3B，SH3YL1和CRISPLD1），它们在Ishikawapr细胞中也发生了巨大变化。 Kaplan-Meier的生存统计数据显示，在7个目标基因中有4个临床意义。此外，确定了8个集线器基因和4个分子复合物检测（MCODES）。通过微阵列和生物信息学分析进行了结论，我们确定了DEGS并确定了孕酮耐药性的全面基因网络。我们提供了孕激素耐药性的几种可能机制，并确定了子宫内膜癌中孕酮耐药性的治疗和预后靶标。
BackgroundProgesterone 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.MethodsWe 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).ResultsIn 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.ConclusionsUsing 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.