ArticleFrontiers in immunology2025
Identification and validation of core biomarkers for sepsis: a comprehensive analysis using bioinformatics and machine learning.
Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Artificial intelligence in biomarker discovery for diseases: diagnostic and therapeutic prospects.Signal transduction and targeted therapy · 2026Review
- Identification of MMP8, DDX24, RNASE2, and EMB as a novel diagnostic gene panel for sepsis: a transcriptome-based modeling study.Frontiers in immunology · 2026Article
- Thirty-five years of evolving inflammatory paradigms in sepsis (1991-2025): a comprehensive mapping of immune dysregulation research, anti-inflammatory targets, and translational barriers.Frontiers in immunology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sepsis, a life-threatening condition caused by the body's response to infection, requires timely and accurate diagnosis to improve patient outcomes. Despite advances in medical research, identifying reliable biomarkers for sepsis remains a challenge. This study aims to identify and validate key biomarkers for sepsis, addressing the limitations of current diagnostic methods like the SOFA score, PCT, and CRP, particularly in terms of specificity and early detection.
methodsWe recruited 23 sepsis patients and 10 healthy controls, collecting peripheral blood samples for mRNA sequencing. Public datasets (GSE134347, GSE167363, and GSE220189) were also utilized for differential gene expression analysis. The expression and functions of these biomarkers were systematically verified through GO/KEGG enrichment analysis, protein-protein interaction network construction, ROC curve analysis, AUC values of machine-learning models, survival analysis, and immune cell subset localization analysis.
resultsBioinformatics analysis identified four core biomarkers-CD27, KLRB1, RETN, and CD163-as significantly differentially expressed in sepsis patients. ROC curve and AUC analyses of machine-learning models showed AUC values exceeding 0.9 for these biomarkers across seven models, indicating superior diagnostic performance. Survival analysis revealed significant associations of KLRB1, RETN, and CD163 with sepsis prognosis. Specifically, higher expression levels of RETN and CD163 were linked to increased mortality risk, whereas higher KLRB1 levels were associated with decreased mortality risk. Immune cell-specific expression localization showed CD27 expression in T cells, KLRB1 in NK cells, RETN in monocytes and neutrophils, and CD163 in monocytes, indicating a cell-type-based immune regulatory network.
conclusionCD27, KLRB1, RETN, and CD163 form a dynamic immune network that reflects the pathological progression of sepsis from hyper-inflammatory to immunosuppressive phases. Monitoring the expression changes of these biomarkers can accurately assess patients' immune status and guide clinical interventions, such as anti-inflammatory or immunostimulatory therapies. This study offers new directions for early diagnosis and individualized treatment of sepsis.
Indexed as
Identifiers
What OpenQuestion holds
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.