Dataset Information

Hierarchical paracrine interaction of breast cancer associated fibroblasts with cancer cells via hMAPK-microRNAs to drive ER-negative breast cancer phenotype.

ABSTRACT: Multiple juxtacrine and paracrine interactions occur between cancer cells and non-cancer cells of the tumor microenvironment (TME) that direct tumor progression. Cancer Associated Fibroblasts (CAFs) are an integral component of the TME, and the majority of breast tumor stroma is comprised of CAFs. Heterotypic interactions between cancer cells and non-cancer cells of the TME occur via soluble agents, including cytokines, hormones, growth factors, and secreted microRNAs. We previously identified a microRNA signature indicative of hyperactive MAPK signaling (hMAPK-miRNA signature) that significantly associated with reduced recurrence-free and overall survival. Here we report that the hMAPK-miRNA signature associates with a high metric of stromal cell infiltrate, and we investigate the role of microRNAs, particularly hMAPK-microRNAs, secreted by CAFs on estrogen receptor (ER) expression in breast cancer cells. ER-positive MCF-7/ltE2- cells were treated with conditioned media (CM) from CAFs derived from breast cancers of different PAM50 subtypes (CAFBAS, CAFHER2, and CAFLA). CAF CM isolated specifically from ER-negative primary breast tumors led to ER repression in vitro. Nanoparticle tracking analysis and transmission electron microscopy confirmed the presence of CAF-secreted exosomes in CM and the uptake of these exosomes by the ER+ MCF-7/ltE2- cells. Differentially expressed microRNAs in CAF CM as well as in MCF-7/ltE2- cells treated with this CM were identified. Knockdown of miR-221/222 in CAFBAS resulted in knockdown of miR221/222 levels in the conditioned media and the CM from CAFBAS; miR221/222 knockdown rescued ER repression in ER-positive cell lines treated with CAFBAS-CM. Collectively, our results demonstrate that CAF-secreted microRNAs are directly involved in ER-repression, and may contribute to the MAPK-induced ER repression in breast cancer cells.

SUBMITTER: Shah SH

PROVIDER: S-EPMC4846097 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundTreatment strategies for various subtypes of breast cancer (BC) are different based on their distinct molecular characteristics. Therefore, it is very important to identify key differentially expressed genes (DEGs) between ER-positive/HER2-negative BC and ER-negative/HER2-negative BC.MethodsGene expression profiles of GSE22093 and GSE23988 were obtained from the Gene Expression Omnibus database. There were 74 ER-positive/HER2-negative BC tissues and 85 ER-negative/HER2-negative BC tissues in the two profile datasets. DEGs between ER-positive/HER2-negative tissues and ER-negative/HER2-negative BC tissues were identified by the GEO2R tool. The common DEGs among the two datasets were detected with Venn software online. Next, we made use of the Database for Annotation, Visualization and Integrated Discovery to analyze enriched Kyoto Encyclopedia of Gene and Genome (KEGG) pathways and gene ontology terms. Then, the protein-protein interactions (PPIs) of these DEGs were visualized by Cytoscape with the Search Tool for the Retrieval of Interacting Genes. Of the proteins in the PPI network, Molecular Complex Detection plug-in analysis identified nine core upregulated genes and one core downregulated gene. UALCAN and Gene Expression Profiling Interactive Analysis were applied to determine the expression of these 10 genes in BC. Furthermore, for the analysis of overall survival among those genes, the Kaplan-Meier method was implemented.ResultsNinety-three common DEGs (63 upregulated and 30 downregulated) were identified. KEGG pathway enrichment analysis showed that upregulated DEGs were particularly enriched in the progesterone-mediated oocyte maturation pathway. In addition, PGR might be a prognostic biomarker for ER-positive/HER2-negative BC. CCND1 is a poor prognostic biomarker for ER-positive/HER2-negative BC and ER-negative/HER2-negative BC. Moreover, TFF1, AGR2 and EGFR might be predictive biomarkers of node metastasis in ER-positive/HER2-negative BC and ER-negative/HER2-negative BC.ConclusionsCCND1, AGR2, PGR, TFF1 and EGFR are the key DEGs between ER-positive/HER2-negative BC and ER-negative/HER2-negative BC. Further studies are required to confirm the functions of the identified genes.

Dataset Information

Hierarchical paracrine interaction of breast cancer associated fibroblasts with cancer cells via hMAPK-microRNAs to drive ER-negative breast cancer phenotype.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets