Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

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

ABSTRACT: 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.

ORGANISM(S): Homo sapiens

PROVIDER: GSE112099 | GEO | 2020/02/20

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

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

Genomic biomarkers from urine cells indicate a unique metabolomic difference between sepsis and sterile inflammation (discovery 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 | GSE168442 | GEO

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

2021-07-12 | GSE168440 | GEO

Peptidomics analysis of urine from IC/BPS patients and healthy controls

Project description:Interstitial cystitis/bladder pain syndrome (IC/BPS) is a chronic and debilitating pain disorder of the bladder and urinary tract with poorly understood etiology. A definitive diagnosis of IC/BPS can be challenging because many symptoms of IC/BPS are shared with other urological disorders. An analysis of urine presents an attractive and non-invasive resource for monitoring and diagnosing IC/BPS. Here, a non-targeted LC-MS and LC-MS/MS-based peptidomics analysis of urine samples collected from IC/BPS patients were compared to urine samples from asymptomatic controls.

2022-06-06 | PXD031843 | Pride

Identification of urinary exosomal non-coding RNAs as novel biomarkers in chronic kidney disease

Project description:We aimed to identify urinary exosomal ncRNAs as novel biomarkers for diagnosis of Chronic Kidney Disease (CKD) for this, we examined 15 exosomal ncRNA profiles in urine samples from CKD patients from four different stages (I, II, III and IV) and compared them to 10 healthy controls. We identified a significant number of novel, differentially expressed ncRNAs in CKD patients compared to healthy, which might be employed as early diagnostic markers in CKD in the future.

2016-11-30 | E-MTAB-5152 | biostudies-arrayexpress

Urinary proteomics for discovery of gastric cancer biomarkers

Project description:Although blood-based protein biomarkers have been described for diagnosis of some cancers, detection of tumor-derived peptides/proteins in urine provides an attractive and non-invasive alternative for diagnostic or screening purposes. For instance, bladder tumor antigen (BTA) assay is FDA approved urine-based biomarker assay to detect the bladder cancer. In this study, we decided to investigate the possibility of discovering proteins as biomarkers in urine from individuals diagnosed with gastric cancer. We carried out comprehensive quantitative profiling of urine samples using tandem mass tags (TMT)-based multiplexed mass spectrometry approach. Of the 1,504 total number of proteins identified, 246 proteins were differentially expressed in a gastric cancer cohort. Ephrin A1 (EFNA1), pepsinogen A3 (PGA3) and vitronectin (VTN) were among the upregulated proteins which are known to play crucial role in the progression of gastric cancer. Notably, our analysis also revealed other overexpressed proteins including shisa family member 5 (SHISA5), mucin like 1 (MUCL1) and leukocyte cell derived chemotaxin 2 (LECT2), which had not previously been linked to gastric cancer. We decided to deploy a recently developed targeted method, SureQuant, for validating a subset of potential proteins discovered in this study. We confirmed the overexpression of EFNA1, VTN and SORT1 in an independent set of urine samples. Our study demonstrates the potential of urinary proteomics to identify promising diagnostic biomarkers of cancers in a non-invasive fashion.

2024-08-10 | PXD040956 | Pride

Genomic complexity of urothelial bladder cancer revealed in urinary cfDNA

Project description:Comparison of the genomic profiles of urinary cellular DNA and cell-free DNA (cfDNA) from the urine to matched diagnostic formalin-fixed paraffin embedded (FFPE) tumour DNAs for 23 well-characterised UBC patients.

2015-12-31 | E-MTAB-3841 | biostudies-arrayexpress

LC-MSMS analysis of human urine to identify early diagnostic biomarkers for prostate cancer

Project description:In order to detect proteins sensitive and specific enough to detect early stages of PCa, we performed a complex comparative proteomic study analyzing urine as a source for non-invasive biomarkers. We compared urine samples from patients with early PCa stages and Gleason score between 6 and 7 with samples from patients with BPH and other urological cancers, namely bladder and renal. The main goal of this study was to discover a diagnostic biomarker or set of biomarkers in urine, sufficiently sensitive to detect PCa in its early stages and specific enough to separate the disease from BPH or other urological cancers.

2018-01-02 | PXD008407 | Pride

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

Urinary exosomal long noncoding RNAs may be novel biomarkers for the diagnosis of clear cell renal cell carcinoma

Project description:Background: Renal cell carcinoma, which presents no significant clinical manifestations at early stages, is one of the few tumors with an increasing worldwide incidence. This malignancy cannot be directly detected by tumor markers in body fluid without information from imaging examinations. Approximately 30–40% of patients with kidney cancer exhibit distant metastasis at diagnosis, and their 5-year survival rate is much lower than that of patients with early-stage renal cell carcinoma. Thus, the early diagnosis of renal cell carcinoma is extremely important. The aim of this study was to investigate the utility of urinary exosomal long noncoding RNA (lncRNA) as a new potential diagnostic marker for renal cell carcinoma. Methods: Exosomes were isolated by ultracentrifugation from 50-ml urine samples from 10 patients with clear cell renal cell carcinoma and 10 matched healthy donors. Differentially expressed lncRNAs were analyzed by next-generation sequencing and further validated in kidney cancer cell lines, tissues and urinary exosomes. Then, we evaluated the sensitivity, specificity, clinical diagnostic value and stability of the selected lncRNAs. Results: The levels of lncRNAs NR_040448 and NR_033390 in urinary exosomes can likely indicate the presence of renal cell carcinoma. In addition, RNA protected by exosomes was stable enough to serve as a biomarker. Conclusion: Urinary exosomal lncRNA is a promising marker for the early diagnosis of renal cell carcinoma.

2019-01-23 | GSE125442 | 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