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Discovery and validation of urinary molecular signature of early sepsis

ABSTRACT: This SuperSeries is composed of the SubSeries listed below.

ORGANISM(S): Homo sapiens

PROVIDER: GSE112100 | GEO | 2020/02/20

REPOSITORIES: GEO

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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

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

2020-02-20 | GSE112099 | GEO

Discovery and Validation of Urinary Molecular Signature of Early Sepsis.

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

Discovery and validation of urinary molecular signature of early sepsis

Project description:Discovery and validation of urinary molecular signature of early sepsis

| PRJNA439291 | ENA

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

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

| PRJNA439295 | ENA

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

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

| PRJNA439296 | ENA

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