Dataset Information

Identification of tissue microRNAs predictive of sutinib activity in patients with metastatic renal cell carcinoma

ABSTRACT: 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.

ORGANISM(S): Homo sapiens

PROVIDER: GSE37766 | GEO | 2014/09/30

SECONDARY ACCESSION(S): PRJNA163247

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

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.

Project description:Accumulating evidence indicates that human circulating microRNAs (miRNAs) could serve as diagnostic and prognostic biomarkers in various cancers. We aimed to explore novel miRNA biomarkers in the blood of breast cancer patients based on miRNA profiling. A miRCURY™ LNA Array was used to identify differentially altered miRNAs in the whole blood of breast cancer patients (n = 6) and healthy controls (n = 6). Levels of candidate miRNAs were quantified by quantitative reverse transcription-polymerase chain reaction (qRT-PCR) in whole blood specimens of 15 breast cancer patients and 13 age-matched healthy control individuals. The miRWalk database was used to predict miRNA targets and the DAVID tool was used to identify significant enrichment pathways. A total of 171 differentially expressed miRNAs were identified by microarray, including 169 up-regulated and 2 down-regulated miRNAs in breast cancer. Five upregulated miRNAs (miR-30b-5p, miR-96-5p, miR-182-5p, miR-374b-5p, and miR-942-5p) were confirmed by qRT-PCR. The areas under the receiver operating characteristic curve of miR-30b-5p, miR-96-5p, miR-182-5p, miR-374b-5p, and miR-942-5p were 0.9333, 0.7692, 0.7590, 0.8256, and 0.8128, respectively. A total of 855 genes were predicted to be targeted by the five miRNAs, and the one cut domain family member 2 gene (ONECUT2) was a shared target of the five miRNAs. Analysis of publicly available data revealed that these dysregulated miRNAs and target genes were associated with the survival of breast cancer patients. Furthermore, the five miRNAs were significantly enriched in numerous cancer-related pathways, including “MicroRNAs in cancer”, “Pathways in cancer”, “FoxO signaling pathway”, “Ras signaling pathway”, “Rap1 signaling pathway”, “MAPK signaling pathway”, and “PI3K-Akt signaling pathway”. Our data support the potential of the five identified miRNAs as novel biomarkers for the detection of breast cancer, and indicate that they may be involved in breast cancer development and progression.

Dataset Information

Identification of tissue microRNAs predictive of sutinib activity in patients with metastatic renal cell carcinoma

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets