Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

MicroRNA expression profiles of distinct human and mouse mammary epithelial cell types

ABSTRACT: This SuperSeries is composed of the SubSeries listed below. Refer to individual Series

ORGANISM(S): Mus musculus

SUBMITTER: Gordon Smyth

PROVIDER: E-GEOD-67056 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Integration of microRNA signatures of distinct mammary epithelial cell types with their gene expression and epigenetic portraits.

Pal Bhupinder B Chen Yunshun Y Bert Andrew A Hu Yifang Y Sheridan Julie M JM Beck Tamara T Shi Wei W Satterley Keith K Jamieson Paul P Goodall Gregory J GJ Lindeman Geoffrey J GJ Smyth Gordon K GK Visvader Jane E JE

Breast cancer research : BCR 20150618

<h4>Introduction</h4>MicroRNAs (miRNAs) have been implicated in governing lineage specification and differentiation in multiple organs; however, little is known about their specific roles in mammopoiesis. We have determined the global miRNA expression profiles of functionally distinct epithelial subpopulations in mouse and human mammary tissue, and compared these to their cognate transcriptomes and epigenomes. Finally, the human miRNA signatures were used to interrogate the different subtypes of ...[more]

PMID: 26080807

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Project description:Purpose: To identify tissue microRNAs predictive of sunitinib activity in patients with metastatic renal-cell carcinoma (MRCC) and to validate them in a cellular model. Selected microRNAs were studied in serum from MRCC patients and healthy individuals. Methods: We screened 673 microRNAs using TaqMan Low-density Arrays (TLDAs) in tumors from MRCC patients with extreme phenotypes of marked efficacy and resistance to sunitinib, selected from an identification cohort (n=41). Differentially expressed microRNAs were selected using bioinformatics-based target prediction analysis and quantified by qRT-PCR in tumors from patients presenting similar phenoytpes selected from an independent cohort (n=117). Results were validated in a cellular model of sunitinib resistance and studied in serum from healthy individuals and MRCC patients. Results: TLDAs identified 64 microRNAs differentially expressed in the identification cohort. Seven candidates were quantified by qRT-PCR in the independent series. MiR-942 was the most accurate predictor of sunitinib efficacy (p=0.0074). High expression of miR-942, miR-133a, miR484, and miR-628-5p was significantly associated with decreased time-to-progression and overall survival. These microRNAs were overexpressed in the sunitinib resistant cell line Caki-2 in comparison with the sensitive parental cell line. Serum levels of miR-942, miR-133a, miR-484, miR-146a-5p, miR-374a and miR-486-5p were significantly reduced in MRCC patients compared to healthy controls. Conclusions: Our strategy identified differentially expressed microRNAs in MRCC patients presenting marked sensitivity and resistance to sunitinib. Mir-942 was the best predictor of efficacy. Results were confirmed in a cellular model of sunitinib resistance. We also identified exosome derived serum microRNAs differentially expressed in MRCC patients and healthy individuals. Taqman Low Density Array for 6 FFPE tissues obtained from extreme phenotype MRCC patients, (n=3 marked resistance to sunitinib treatment patients and n=3 marked sensitivity to sunitinib treatment patients), was performanced to screen 667 microRNAs.

Dataset Information

MicroRNA expression profiles of distinct human and mouse mammary epithelial cell types

Publications

Integration of microRNA signatures of distinct mammary epithelial cell types with their gene expression and epigenetic portraits.

Similar Datasets

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