Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Urine miRNA transcriptome of prostate cancer patients [Validation cohort]

ABSTRACT: Intra-individual stability of the urine miRNA transcriptome was examined by investigating longitudinal changes over time in a cohort of patients with localized prostate cancer. Using training and validation cohorts, urinary miRNA biomarkers are characterized and validated their utility to identify aggressive prostate cancer.

ORGANISM(S): Homo sapiens

PROVIDER: GSE104430 | GEO | 2019/10/24

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Similar Datasets

Urine miRNA transcriptome of prostate cancer patients [Discovery cohort]

Project description:Intra-individual stability of the urine miRNA transcriptome was examined by investigating longitudinal changes over time in a cohort of patients with localized prostate cancer. Using training and validation cohorts, urinary miRNA biomarkers are characterized and validated their utility to identify aggressive prostate cancer.

2019-10-24 | GSE104431 | GEO

Urine miRNA transcriptome of prostate cancer patients [Training cohort]

2019-10-24 | GSE104429 | GEO

Urine miRNA transcriptome of prostate cancer patients [Training cohort]

Project description:Urine miRNA transcriptome of prostate cancer patients [Training cohort]

| PRJNA412693 | ENA

Urine miRNA transcriptome of prostate cancer patients [Validation cohort]

Project description:Urine miRNA transcriptome of prostate cancer patients [Validation cohort]

| PRJNA412692 | ENA

Urine miRNA transcriptome of prostate cancer patients [Discovery cohort]

Project description:Urine miRNA transcriptome of prostate cancer patients [Discovery cohort]

| PRJNA412691 | ENA

Discovery and validation of urinary molecular signature of early sepsis (validation cohort)

Project description:Sepsis remains a diagnostic challenge with no gold-standard test. Urine provides a readily available, non-invasive biofluid with significant diagnostic potential. Urinary gene expression has been previously used for diagnosis and prognosis of urological malignancies and transplant allograft rejections, but remains unutilized for sepsis diagnosis. In this study, the authors use urinary gene expression profiles to both diagnose sepsis and characterize its pathophysiology. By using differential expression augmented with machine learning ensembles, the authors identify a collection of cellular mRNA from 239 genes in patient urine which show exceptional power in classifying septic patients from those with chronic systemic disease in both internal and independent external validation cohorts. Functional analysis indexes the disrupted biological pathways in early sepsis and additionally reveals key molecular networks driving its pathogenesis. This study serves a pioneering step towards expanding the clinical potential of urinary molecular profiles for application to systemic diseases.

2020-02-20 | GSE112098 | GEO

Urine miRNA transcriptome of prostate cancer patients

Project description:Urine miRNA transcriptome of prostate cancer patients

| PRJNA342023 | ENA

Genomic biomarkers from urine cells indicate a unique metabolomic difference between sepsis and sterile inflammation (validation cohort)

Project description:Sepsis is a time-sensitive condition associated with significant mortality, morbidity, and healthcare costs, especially when the diagnosis is delayed. Clinicians often fail to accurately differentiate between sepsis and a sterile systemic inflammatory response syndrome (SIRS) among patients who incur sterile tissue damage from major surgery. Sepsis is driven by a dysregulated host response to pathogens; SIRS is driven by tissue damage. Transcriptomic profiling of whole blood or of specific cellular components of blood have been utilized for discovering underlying etiological differences between sepsis and uninfected SIRS. Blood-based gene microarrays have demonstrated efficacy in differentiating sepsis from SIRS. Urine is often collected from critically ill patients as standard clinical care, but the diagnostic utility of urine sepsis biomarkers is unknown. In this study we used single-center prospective cohorts of SIRS and sepsis patients, we tested the hypothesis that machine learning feature selection from whole genome transcriptomic urinary RNA signatures can identify gene expression patterns that differentiate between sepsis and sterile SIRS within twelve hours of sepsis onset.

2021-07-12 | GSE168440 | GEO

Discovery and validation of urinary molecular signature of early sepsis (discovery cohort)

2020-02-20 | GSE112099 | GEO

Cell and microvesicle urine microRNA deep sequencing profiles from healthy individuals

Project description:Background: Urine is a potential source of biomarkers for diseases of the kidneys and urinary tract. RNA, including microRNA, is present in the urine enclosed in detached cells or in extracellular vesicles (EVs) or bound and protected by extracellular proteins. Detection of cell- and disease-specific microRNA in urine may aid early diagnosis of organ-specific pathology. In this study, we applied barcoded deep sequencing to profile microRNAs in urine of healthy volunteers, and characterized the effects of sex, urine fraction (cells vs. EVs) and repeated voids by the same individuals. Results: Compared to urine-cell-derived small RNA libraries, urine-EV-derived libraries were relatively enriched with miRNA, and accordingly had lesser content of other small RNA such as rRNA, tRNA and sn/snoRNA. Unsupervised clustering of specimens in relation to miRNA expression levels showed prominent bundling by specimen type (urine cells or EVs) and by sex, as well as a tendency of repeated (first and second void) samples to neighbor closely. Likewise, miRNA profile correlations between void repeats, as well as fraction counterparts (cells and EVs from the same specimen) were distinctly higher than correlations between miRNA profiles overall. Differential miRNA expression by sex was similar in cells and EVs. Conclusions: miRNA profiling of both urine EVs and sediment cells can convey biologically important differences between individuals. However, to be useful as urine biomarkers, careful consideration is needed for biofluid fractionation and sex-specific analysis, while the time of voiding appears to be less important.

2016-01-28 | GSE72183 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

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