ArticleFrontiers in immunology2024
The immune landscape of sepsis and using immune clusters for identifying sepsis endotypes.
Article in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Immune Endotype-Guided Precision Immunotherapy in Sepsis: From Transcriptomics to Bedside Application.Cureus · 2026Review
- Beyond antimicrobials: a host-directed precision pharmacotherapy framework for septic shock.Frontiers in medicine · 2026Article
- Bridging sepsis physiology and biosensor engineering: a clinical perspective on electrochemical cytokine sensor design.Frontiers in bioengineering and biotechnology · 2026Article
- A Correlation between Sequential Organ Failure Assessment Scores and Biomarkers of Inflammation and Infection in Patients with Sepsis.Current medicinal chemistry · 2026Article
- Systemic immune responses in patients with spine surgery related infections.Frontiers in immunology · 2026Article
- Molecular analysis of immune cell subsets and cytokine profiles in septic Vietnamese patients.Clinical and experimental medicine · 2025Article
- Challenges of monocyte HLA-DR targeted immunomodulation in sepsis-a prospective observational cohort study.Frontiers in immunology · 2025Observational
- Reassessing sepsis research: new clues for old players and new players for an old symptom to improve patient outcomes.EXCLI journal · 2025Review
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The dysregulated immune response to sepsis still remains unclear. Stratification of sepsis patients into endotypes based on immune indicators is important for the future development of personalized therapies. We aimed to evaluate the immune landscape of sepsis and the use of immune clusters for identifying sepsis endotypes. Methods: The indicators involved in innate, cellular, and humoral immune cells, inhibitory immune cells, and cytokines were simultaneously assessed in 90 sepsis patients and 40 healthy controls. Unsupervised k-means cluster analysis of immune indicator data were used to identify patient clusters, and a random forest approach was used to build a prediction model for classifying sepsis endotypes. Results: We depicted that the impairment of innate and adaptive immunity accompanying increased inflammation was the most prominent feature in patients with sepsis. However, using immune indicators for distinguishing sepsis from bacteremia was difficult, most likely due to the considerable heterogeneity in sepsis patients. Cluster analysis of sepsis patients identified three immune clusters with different survival rates. Cluster 1 (36.7%) could be distinguished from the other clusters as being an "effector-type" cluster, whereas cluster 2 (34.4%) was a "potential-type" cluster, and cluster 3 (28.9%) was a "dysregulation-type" cluster, which showed the lowest survival rate. In addition, we established a prediction model based on immune indicator data, which accurately classified sepsis patients into three immune endotypes. Conclusion: We depicted the immune landscape of patients with sepsis and identified three distinct immune endotypes with different survival rates. Cluster membership could be predicted with a model based on immune data.
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