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Updated: Jan 7, 2026
A Data-Driven Approach to Quantifying Immune States in Sepsis
Published on: February 7, 2025
Identification and validation of core biomarkers for sepsis: a comprehensive analysis using bioinformatics and
Haili Li1, Sishi Jiang1, Zhibin Chen1
1Department of Emergency Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China.
None:
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.
Methods:
We 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.
Results:
Bioinformatics 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.
Conclusion:
CD27, 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.
