Dataset Information

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

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.

SUBMITTER: Li W

PROVIDER: S-EPMC6391799 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Li Wenzhi W Wang Shufen S Qiu Chunping C Liu Zhiming Z Zhou Qing Q Kong Deshui D Ma Xiaohong X Jiang Jie J

Journal of translational medicine 20190227 1

<h4>Background</h4>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.<h4>Methods</h4>We developed a stable MPA (medroxyprogesterone acetate)-resistant endometrial cancer cell subline named IshikawaPR. ...[more]

PMID: 30813939

Similar Datasets

Project description:BackgroundEndometrial cancer (EC) is one of the female malignant tumors. Endometrial cancer predominately affects post-menopausal women. Bioinformatics analysis has been widely applied to screen and analyze genes in linkage to various types of cancer progression.MethodsDownload the gene expression profile from Gene Expression Omnibus (GEO). Calculate raw expression data according to pre-processing procedures. We performed the "limma" R language package to screen DEGs between Endometrial cancer tissue samples and normal uterus tissue samples. Enrichment of the functions and pathways was analyzed by using clusterprofiler. We utilized Search Tool for the Retrieval of Interacting Genes Database (STRING) to assess protein-protein interaction (PPI) information, and then we used plug-in Molecular Complex Detection (MCODE) to screen hub modules of PPI network in Cytoscape. We also performed functional analysis on the genes in the hub module by using clusterprofiler. Next, we utilized the "WGCNA" package in R to establish co-expression network for the DEGs. The Venn diagram was performed to overlap the gene in key module and hub PPI cluster. We validated the key genes in TCGA, GEPIA, UALCAN and Immunohistochemistry staining obtained from The Human Protein Atlas database. And then we did ROC curve analysis by SPSS. Gene set enrichment analysis (GSEA) and mutation analysis were also performed for hub genes.ResultsFunctional and pathway enrichment analysis demonstrated that the upregulated differentially expressed genes (DEGs) were significantly enriched in CXCR chemokine receptor binding, chemokine activity, chemokine receptor binding, G-protein coupled receptor binding, RAGE receptor binding, cytokine activity, microtubule binding, receptor regulator activity and microtubule motor activity, and the down-regulated genes were highly enriched in collagen binding. After using STRING software to construct PPI network, 30 prominent proteins were identified and the first two significant modules were selected. In co-expression network, 5 EC-related modules were identified. Among them, the turquoise module has the highest correlation with the EC. We further analyzed the genes in the PPI and turquoise module, and selected eleven key genes related to EC after validation of TCGA database, GEPIA, UALCAN and immunohistochemistry. Six of them had mutation significance.ConclusionsIn summary, these 11 genes may become new therapy targets for EC treatment.

Project description:Every year more than 42,000 women die of endometrial cancer, mainly due to recurrent or metastatic disease. The presence of tumor infiltrating lymphocytes (TILs) as well as progesterone receptor (PR) positivity has been correlated with improved prognosis. This study describes two mechanisms by which progesterone inhibits metastatic spread of endometrial cancer: by stimulating T-cell infiltration and by inhibiting epithelial-to-mesenchymal cell transition (EMT). Paraffin sections from patients with (n=9) or without (n=10) progressive endometrial cancer (recurrent or metastatic disease) were assessed for the presence of CD4+ (helper), CD8+ (cytotoxic) and Foxp3+ (regulatory) T-lymphocytes and PR expression. Progressive disease was observed to be associated with significant loss of TILs and loss of PR expression. Frozen tumor samples, used for genome-wide expression analysis, showed significant regulation of pathways involved in immunosurveillance, EMT and metastasis. For a number of genes, such as CXCL14, DKK1, DKK4 and WIF1, quantitive RT-PCR was performed to verify down regulation in progressive disease. To corroborate the role of progesterone in regulating invasion, Ishikawa (IK) endometrial cancer cell lines stably transfected with PRA (IKPRA), PRB (IKPRB) and PRA+PRB (IKPRAB) were cultured in the presence/absence of progesterone (MPA) and used for genome-wide expression analysis, Boyden- and wound healing migration assays, and IHC for known EMT makers. IKPRB and IKPRAB cell lines showed MPA induced inhibition of migration and loss of the mesenchymal marker vimentin at the invasive front of the wound healing assay. Furthermore, pathway analysis of significantly MPA-regulated genes showed significant down regulation of important pathways involved in EMT, immunesuppression and metastasis: such as IL6-, TGF-β and Wnt/β-catenin signalling. Intact progesterone signaling in non-progressive endometrial cancer seems to be an important factor stimulating immunosurveillance and inhibiting transition from an epithelial to a more mesenchymal, more invasive phenotype.

Project description:BackgroundWe sought to determine the prevalence of germline pathogenic variants (gPVs) in unselected patients with endometrial cancer (EC), define biallelic gPVs within tumors, and describe their associations with clinicopathologic features.MethodsGermline assessment of at least 76 cancer predisposition genes was performed in patients with EC undergoing clinical tumor-normal Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT) sequencing from January 1, 2015, to June 30, 2021. In patients with gPVs, biallelic alterations in ECs were identified through analysis of loss of heterozygosity and somatic PVs. Clinicopathologic variables were compared using nonparametric tests.ResultsOf 1625 patients with EC, 216 (13%) had gPVs, and 15 patients had 2 gPVs. There were 231 gPVs in 35 genes (75 [32%] high penetrance; 39 [17%] moderate penetrance; and 117 [51%] low, recessive, or uncertain penetrance). Compared with those without gPVs, patients with gPVs were younger (P = .002), more often White (P = .009), and less obese (P = .025) and had differences in distribution of tumor histology (P = .017) and molecular subtype (P < .001). Among 231 gPVs, 74 (32%) exhibited biallelic inactivation within tumors. For high-penetrance gPVs, 63% (47 of 75) of ECs had biallelic alterations, primarily affecting mismatch repair (MMR) and homologous recombination related genes, including BRCA1,BRCA2, RAD51D, and PALB2. Biallelic inactivation varied across molecular subtypes with highest rates in microsatellite instability-high (MSI-H) or copy-number (CN)-high subtypes (3 of 12 [25%] POLE, 30 of 77 [39%] MSI-H, 27 of 60 [45%] CN-high, 9 of 57 [16%] CN-low; P < .001).ConclusionsOf unselected patients with EC, 13% had gPVs, with 63% of gPVs in high-penetrance genes (MMR and homologous recombination) exhibiting biallelic inactivation, potentially driving cancer development. This supports germline assessment in EC given implications for treatment and cancer prevention.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets