Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Detection of urinary microRNAs from epithelial ovarian cancer patients as biomarker

ABSTRACT: The Urine RNA from 5 ovarian cancer and 5 healthy control were analyzed with the Human miRNA Microarray and then validated with a quantitative reverse-transcription PCR assay with 29 individual samples, 20 benign gynecological disease and 26 age/sex-matched healthy control. This study determines the clinical value of urine miRNAs as biomarker for epithelial ovarian cancer. Results: The miRNA microarray results demonstrate that the original 38 were identified that were significantly differentially expression in epithelial ovarian cancer compared with healthy control (P<0.01). A total of 1 miRNA was up-regulated in ovarian cancer, while 37 miRNA were down-regulated.

ORGANISM(S): Homo sapiens

PROVIDER: GSE58517 | GEO | 2015/06/17

SECONDARY ACCESSION(S): PRJNA252815

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Detection of urinary microRNAs from epithelial ovarian cancer patients as biomarker

Project description:The Urine RNA from 5 ovarian cancer and 5 healthy control were analyzed with the Human miRNA Microarray and then validated with a quantitative reverse-transcription PCR assay with 29 individual samples, 20 benign gynecological disease and 26 age/sex-matched healthy control. This study determines the clinical value of urine miRNAs as biomarker for epithelial ovarian cancer. Results: The miRNA microarray results demonstrate that the original 38 were identified that were significantly differentially expression in epithelial ovarian cancer compared with healthy control (P<0.01). A total of 1 miRNA was up-regulated in ovarian cancer, while 37 miRNA were down-regulated. the urine RNA from 5 ovarian cancer and 5 healthy women were analysised with Human mirRNA microarry

2015-06-17 | E-GEOD-58517 | biostudies-arrayexpress

Urinary single cell profiling captures cellular diversity of the kidney

Project description:We performed single cell transcriptomic analysis on 17 urine samples obtained from five subjects at two different occasions using both spot and 24-hour urine collection. In addition, we used a combined spot urine samples of five healthy individuals as a control sample. We sequenced a total of 71,667 cells. After quality control and downstream analysis, we found that epithelial cells were the most common cell types in the urine. We were also able to identify most kidney cell types in the urine, such as podocyte, proximal, and collecting duct (CD), in addition to macrophages, monocytes and lymphocytes.

2021-02-02 | GSE157640 | GEO

Profiling of urinary microRNAs in children with atopic dermatitis

Project description:Atopic dermatitis (AD) is the most prevalent chronic inflammatory skin disease in children characterized by dermatitis and pruritus. MicroRNAs (miRNAs) have been shown as great potential biomarkers for disease fingerprints to predict prognostics. We aimed to identify miRNA signature from serum and urine for the prognosis of AD patient by genome-wide miRNA profiling analysis. Urine from 3 children with AD and 3 healthy children were collected

2014-10-17 | E-GEOD-62403 | biostudies-arrayexpress

Urinary Extracellular Vesicles RNA Profile in Clear Cell Renal Cell Carcinoma as Biomarker

Project description:Background: Renal cell carcinoma (RCC) accounts for about 2% of all cancers. Renal biopsy is the gold standard among the diagnostic tools, but it is invasive and not suitable for all patients. Therefore, new reliable and non-invasive biomarkers for ccRCC detection are required. Secretion of extracellular vesicles (EVs), containing RNA molecules that can be transferred between cells, seems to be a general characteristic of malignant transformation. Consistently, cancer-derived EVs are enriched in the blood, urine and various malignant effusions of cancer patients. Therefore, urinary samples can be a non-invasive approach for discovering diagnostic biomarkers. Methods: We enrolled 33 clear-cell RCC (ccRCC) patients and 22 healthy subjects (HS), age and sex-matched, for urine collection and extracellular vesicles isolation by differential centrifugation. Transcriptional profiles of urinary EVs from 12 patients with ccRCC and 11 HS were generated using the Illumina HumanHT-12 v4 BeadChip oligonucleotide arrays. Microarray analysis led to the identification of RNA that were then validated using RT-qPCR. Results: We showed for the first time that urinary exosomal shuttle RNA (esRNA) was significantly different in ccRCC patients compared to HS and we identified three EVs esRNA involved in the tumor biology that are potentially suitable as non-invasive biomarkers. GSTA1, CEBPA and PCBD1 RNA levels decreased in urinary EVs of patients compared to HS. After 1 month post-operation, the levels of RNA increased to reach the normal level. Conclusions: This study suggests, for the first time, the potential use of the RNA content of urinary EVs to provide a non-invasive first step to diagnose the ccRCC. Total RNA obtained from urinary extracellular vesicles isolated from ccRCC patients and healthy subjects.

2016-09-01 | E-GEOD-72922 | biostudies-arrayexpress

Genome-wide identification of urinary cell-free microRNAs for non-invasive detection of bladder cancer

Project description:Objective was to identify urine cell-free microRNAs enabling early non-invasive detection of bladder cancer. Total RNA enriched for fraction of short RNAs was isolated using Urine microRNA purification kit (Norgen corp.). miRNA profiles were determined using the Affymetrix GeneChip miRNA 3.0 array and analyzed to identify differentially deregulated miRNA in bladder cancer patients compared with helathy controls.

2017-10-29 | E-MTAB-6102 | biostudies-arrayexpress

Gene Expression Profiling Reveals Novel Candidate Markers of Ovarian Carcinoma Intraperitoneal Metastasis

Project description:Epithelial ovarian cancer (EOC) has the highest mortality among gynecological carcinomas. The lack of specific markers for prognostic determination of EOC progression hinders the search for novel effective therapies. The aim of the present study was (i) to explore differences in expressions of ATP-binding cassette and solute carrier transporter genes, genes associated with drug metabolism and cell cycle regulation between control ovarian tissues, primary epithelial ovarian carcinomas (EOC) and intraperitoneal EOC metastases; (ii) to investigate associations of gene expression level with prognosis of patients with intraperitoneal metastases.

2017-09-01 | GSE102180 | GEO

Expression data from OVCAR-3 cells overexpressing histone H1.3

Project description:Ovarian cancer is a deadly gynecological malignancy for which novel biomarkers and therapeutic targets are imperative for improving survival. To investigate the role of histone H1 in ovarian cancer cells, we overexpress a histone H1 variant, H1.3, in the OVCAR-3 epithelial ovarian cancer cell line. RNA was extracted from OV-3/H1.3(H) cells (OVCAR-3 with overexpression of H1.3) and control cells of OVCAR-3 transfected with vectors without H1.3. The microarray chip used was human Affymetrix ST1.0 array. Gene expression changes caused by overexpression of H1.3 in OVCAR-3 cells were identified.

2014-10-31 | GSE61692 | GEO

Epithelial ovarian cancer biomarker

Project description:Protein biomarkers of early stages of disease are critical for managing the disease and improving outcomes. Unfortunately, discovery of protein biomarkers is hampered by the limited availability of assays for sensitive and reproducible quantification of proteins in complex biological matrices such as blood plasma. In the recent years, Selected Reaction Monitoring (SRM) has emerged as a robust mass spectrometric method, capable of overcoming this limitation. Here, we present a comprehensive SRM-based strategy for developing protein biomarkers for epithelial ovarian cancer, and illustrate the extent to which SRM platform, combined with sound experimental design and statistical analysis, results in robust detection of predictive analytes. First, the biomarker development was guided by a discovery-driven proteomic effort to detect potential N-glycoprotein biomarker candidates in tissue of a genetically stable ovarian cancer mouse model. Next, 65 candidate markers were reproducibly quantified with SRM assays across a large cohort of over 200 plasma samples from ovarian cancer patients and healthy controls. Finally, these measurements were used to derive 5-protein signatures for distinguishing individuals with epithelial ovarian cancer from healthy controls. The sensitivity of the candidate biomarker signature in combination with CA125 ELISA-based measurements currently used in clinic, exceeded that of CA125 ELISA-based measurements alone. The SRM-based strategy in this study is broadly applicable. It can be used in any study that requires accurate and reproducible quantification of selected proteins in a high-throughput and multiplexed fashion.

2019-07-02 | MSV000084048 | MassIVE

Expression data from OVCAR-3 cells overexpressing histone H1.3

2014-10-31 | E-GEOD-61692 | biostudies-arrayexpress

Urinary exosomal small non-coding RNA expression of prostate cancer patients

Project description:Small non-coding RNA profiling of urine exosomal total RNA from patients with or without prostate cancer were performed using Affymetrix GeneChip miRNA 4.0 to identify small non-coding RNA profile that can be used for prostate cancer diagnosis.

2020-07-29 | GSE138740 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data