Discovery and validation of urinary molecular signature of early sepsis
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PROVIDER: PRJNA439291 | ENA |
REPOSITORIES: ENA
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Project description:Discovery and validation of urinary molecular signature of early sepsis (validation cohort)
| PRJNA439295 | ENA
Project description:Discovery and validation of urinary molecular signature of early sepsis (discovery cohort)
| PRJNA439296 | ENA
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
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 | GSE112099 | GEO
Project description:This SuperSeries is composed of the SubSeries listed below.
2020-02-20 | GSE112100 | GEO
Project description:Identify alterations in gene expression unique to systemic and kidney-specific pathophysiologic processes using whole-genome analyses of RNA isolated from the urinary cells of sepsis patients. Design:Prospective cohort study. Setting:Quaternary care academic hospital. Patients:A total of 266 sepsis and 82 control patients enrolled between January 2015 and February 2018. Interventions:Whole-genome transcriptomic analysis of messenger RNA isolated from the urinary cells of sepsis patients within 12 hours of sepsis onset and from control subjects. Measurements and Main Results:The differentially expressed probes that map to known genes were subjected to feature selection using multiple machine learning techniques to find the best subset of probes that differentiates sepsis from control subjects. Using differential expression augmented with machine learning ensembles, we identified a set of 239 genes in urine, which show excellent effectiveness in classifying septic patients from those with chronic systemic disease in both internal and independent external validation cohorts. Functional analysis indexes disrupted biological pathways in early sepsis and reveal key molecular networks driving its pathogenesis. Conclusions:We identified unique urinary gene expression profile in early sepsis. Future studies need to confirm whether this approach can complement blood transcriptomic approaches for sepsis diagnosis and prognostication.
| S-EPMC7523873 | biostudies-literature
Project description:We assayed leukocyte global gene expression for a prospective discovery cohort of 106 adult patients admitted to UK intensive care units with sepsis due to community acquired pneumonia or faecal peritonitis. We assigned all samples to sepsis response signature groups after performing unsupervised analysis of the transcriptomic data.
2017-05-01 | E-MTAB-5274 | biostudies-arrayexpress
Project description:To identify signature genes that help distinguish (1) sepsis from non-infectious causes of systemic inflammatory response syndrome, (2) between Gram-positive and Gram-negative sepsis. Experiment Overall Design: A total of 70 critically ill patients (46 sepsis and 24 control) were enrolled in a single-centre observational study. Gene-expression profiling was performed using Affymetrix microarray (U133plus2) with 54,675 transcript. Data was divided into a training set (n=35) and a validation set (n=35). A molecular signature was developed in the training set and was then validated in the validation set.
2009-04-12 | E-GEOD-9960 | biostudies-arrayexpress
Project description:We assayed leukocyte global gene expression for a prospective validation cohort of 221 adult patients admitted to UK intensive care units with sepsis due to community acquired pneumonia or faecal peritonitis. 10 samples from patients scheduled for elective cardiac surgery were also assayed as non-septic controls. We assigned all samples to sepsis response signature groups after performing unsupervised analysis of the transcriptomic data.
2017-05-01 | E-MTAB-5273 | biostudies-arrayexpress
Project description:Background: Sepsis involves aberrant immune responses to infection, but the exact nature of this immune dysfunction remains poorly defined. Bacterial endotoxins like lipopolysaccharide (LPS) are potent inducers of inflammation, which has been associated with the pathophysiology of sepsis, but repeated exposure can also induce a suppressive effect known as endotoxin tolerance or cellular reprogramming. It has been proposed that endotoxin tolerance might be associated with the immunosuppressive state that was primarily observed during late-stage sepsis. However, this relationship remains poorly characterised. Here we clarify the underlying mechanisms and timing of immune dysfunction in sepsis. Methods: We defined a gene expression signature characteristic of endotoxin tolerance. Gene-set test approaches were used to correlate this signature with early sepsis, both newly and retrospectively analysing microarrays from 593 patients in 11 cohorts. Then we recruited a unique cohort of possible sepsis patients at first clinical presentation in an independent blinded controlled observational study to determine whether this signature was associated with the development of confirmed sepsis and organ dysfunction. Findings: All sepsis patients presented an expression profile strongly associated with the endotoxin tolerance signature (p < 0.01; AUC 96.1%). Importantly, this signature further differentiated between suspected sepsis patients who did, or did not, go on to develop confirmed sepsis, and predicted the development of organ dysfunction. Interpretation: Our data support an updated model of sepsis pathogenesis in which endotoxin tolerance-mediated immune dysfunction (cellular reprogramming) is present throughout the clinical course of disease and related to disease severity. Thus endotoxin tolerance might offer new insights guiding the development of new therapies and diagnostics for early sepsis. For the RNA-Seq study reported here, 73 patients were recruited with deferred consent at the time of first examination in an emergency ward based on the opinion of physicians that there was a potential for the patient's condition to develop into sepsis. These were retrospectively divided into groups based on clinical features and compared to 11 non-urgent surgical controls.
2014-11-14 | E-GEOD-63311 | biostudies-arrayexpress