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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

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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

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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.

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

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2014-10-31 | GSE61692 | GEO

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

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

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.

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