ArticleScience translational medicine2015
A comprehensive time-course-based multicohort analysis of sepsis and sterile inflammation reveals a robust diagnostic gene set.
Article in Science translational medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 5 registered trials, which are not on this map. Cited by 200 papers, 10 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Longitudinal Gene Expression Profiling in Adults After Traumatic Injury
Collection of Clinical Specimens From Volunteers for Assay Development
TriVerity in the Diagnosis and Prognosis of Emergency Department Patients With Suspected Infections and Suspected Sepsis
Validation of Molecular and Protein Biomarkers in Sepsis
Sepsis Prognosis and Diagnosis in the Emergency Department Using a Host Response Classifier (SPEED) UK - a Multi-Centre Observational Study
Who cites it
200 citing papers in PubMed, 10 syntheses or guidelines pooled it, 333 citations in OpenAlex.
- Pooled it
- Comprehensive multi-cohort transcriptional meta-analysis of muscle diseases identifies a signature of disease severity.Scientific reports · 2022Pooled it
- Multisystem inflammatory syndrome in children: a microcosm of challenges and opportunities for translational bioinformatics in pediatric research.Current opinion in pediatrics · 2021Pooled it
- Transcriptomic Profiling Identifies Neutrophil-Specific Upregulation of Cystatin F as a Marker of Acute Inflammation in Humans.Frontiers in immunology · 2021Pooled it
- Host-response-based gene signatures for tuberculosis diagnosis: A systematic comparison of 16 signatures.PLoS medicine · 2019Pooled it
- The immunosuppressive face of sepsis early on intensive care unit-A large-scale microarray meta-analysis.PloS one · 2018Pooled it
- Gene Expression Analysis to Assess the Relevance of Rodent Models to Human Lung Injury.American journal of respiratory cell and molecular biology · 2017Pooled it
- META-ANALYSIS OF CONTINUOUS PHENOTYPES IDENTIFIES A GENE SIGNATURE THAT CORRELATES WITH COPD DISEASE STATUS.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2017Pooled it
- Genome-wide expression for diagnosis of pulmonary tuberculosis: a multicohort analysis.The Lancet. Respiratory medicine · 2016Pooled it
- Integrated, Multi-cohort Analysis Identifies Conserved Transcriptional Signatures across Multiple Respiratory Viruses.Immunity · 2015Pooled it
- Relationship Between Phenotyping and Individualized Absolute Risk Differences in Sepsis: A Secondary Analysis of Two Approaches in Two Multicenter Trials.Critical care explorations · 2025Trial
- A 6-mRNA host response classifier in whole blood predicts outcomes in COVID-19 and other acute viral infections.Scientific reports · 2022Trial
- An NLRP3 inflammasome-anchored Astragalus mechanistic prior yields a mortality-associated transcriptomic signal in ICU sepsis: a secondary analysis with exploratory cross-cohort assessment.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2026Article
- Real-world clinical evaluation of the Inflammatix TriVerity test for sepsis at the emergency department: an Acutelines study.Internal and emergency medicine · 2026Article
- The molecular ICU: a primer on omics, informatics and the future of precision critical care.Critical care (London, England) · 2026Review
- Whole blood transcriptomics reveals sepsis mortality-associated changes in neutrophil degranulation.American journal of respiratory cell and molecular biology · 2026Observational
- Pulmonary inflammation in severe pneumonia is characterised by compartmentalised and mechanistically distinct sub-phenotypes.Nature communications · 2026Article
- Diagnostic accuracy of SeptiCyte® RAPID to discriminate sepsis from non-infectious critical illness in patients meeting sepsis criteria according to sepsis-3 definition at ICU admission.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2026Observational
- Community-Acquired Pneumonia: Disease Course Prediction With a 5-Gene Signature.CHEST pulmonary · 2026Article
- Perioperative inflammation, anesthesia modulation and cellular signaling in lung cancer: an integrated transcriptomic and single-cell analysis.Translational lung cancer research · 2026Article
140 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Although several dozen studies of gene expression in sepsis have been published, distinguishing sepsis from a sterile systemic inflammatory response syndrome (SIRS) is still largely up to clinical suspicion. We hypothesized that a multicohort analysis of the publicly available sepsis gene expression data sets would yield a robust set of genes for distinguishing patients with sepsis from patients with sterile inflammation. A comprehensive search for gene expression data sets in sepsis identified 27 data sets matching our inclusion criteria. Five data sets (n = 663 samples) compared patients with sterile inflammation (SIRS/trauma) to time-matched patients with infections. We applied our multicohort analysis framework that uses both effect sizes and P values in a leave-one-data set-out fashion to these data sets. We identified 11 genes that were differentially expressed (false discovery rate ≤1%, inter-data set heterogeneity P > 0.01, summary effect size >1.5-fold) across all discovery cohorts with excellent diagnostic power [mean area under the receiver operating characteristic curve (AUC), 0.87; range, 0.7 to 0.98]. We then validated these 11 genes in 15 independent cohorts comparing (i) time-matched infected versus noninfected trauma patients (4 cohorts), (ii) ICU/trauma patients with infections over the clinical time course (3 cohorts), and (iii) healthy subjects versus sepsis patients (8 cohorts). In the discovery Glue Grant cohort, SIRS plus the 11-gene set improved prediction of infection (compared to SIRS alone) with a continuous net reclassification index of 0.90. Overall, multicohort analysis of time-matched cohorts yielded 11 genes that robustly distinguish sterile inflammation from infectious inflammation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.