A combined risk model for the multi-encompassing identification of heterogeneities of prognoses, biological pathway variations and immune states for sepsis patients

Comprehensive analysis of immune cell landscapes revealed that immune cell ratio eosinophil/B.cell.memory is predictive of survival in sepsis

BACKGROUND

Immune dysregulation is a feature of sepsis. However, a comprehensive analysis of the immune landscapes in septic patients has not been conducted.

OBJECTIVES

This study aims to explore the abundance ratios of immune cells in sepsis and investigate their clinical value.

METHODS

Sepsis transcriptome data sets were downloaded from the NCBI GEO database. The immunedeconv R package was employed to analyze the abundance of immune cells in sepsis patients and calculate the ratios of different immune cell types. Differential analysis of immune cell ratios was performed using the t test. The Spearman rank correlation coefficient was utilized to find the relationships between immune cell abundance and pathways. The prognostic significance of immune cell ratios for patient survival probability was assessed using the log-rank test. In addition, differential gene expression was performed using the limma package, and gene co-expression analysis was executed using the WGCNA package.

RESULTS

We found significant changes in immune cell ratios between sepsis patients and healthy controls. Some of these ratios were associated with 28-day survival. Certain pathways showed significant correlations with immune cell ratios. Notably, six immune cell ratios demonstrated discriminative ability for patients with systemic inflammatory response syndrome (SIRS), bacterial sepsis, and viral sepsis, with an Area Under the Curve (AUC) larger than 0.84. Patients with a high eosinophil/B.cell.memory ratio exhibited poor survival outcomes. A total of 774 differential genes were identified in sepsis patients with a high eosinophil/B.cell.memory ratio compared to those with a low ratio. These genes were organized into seven co-expression modules associated with relevant pathways, including interferon signaling, T-cell receptor signaling, and specific granule pathways.

CONCLUSIONS

Immune cell ratios eosinophil/B.cell.memory and NK.cell.activated/NK.cell.resting in sepsis patients can be utilized for disease subtyping, prognosis, and diagnosis. The proposed cell ratios may have higher prognostic values than the neutrophil-to-lymphocyte ratio (NLR).

Basophils absence predicts poor prognosis and indicates immunosuppression of patients in intensive care units

Immune cells and immunity are associated with the prognosis of patients with critical illness. Here, medical records retrospectively extracted from the Medical Information Mart for Intensive Care IV were used for screening an immune-related biomarker in intensive care units (ICU) patients and applied for validating the identified indicator in septic patients. In this work, the count of innate immune cells, basophils, harbored a superior role in predicting ICU patients' prognosis compared with those of other blood immune cells (OR 0.013, 95% CI 0.001, 0.118, P < 0.001). Importantly, basophils absence during ICU stay was positively correlated with the 28-day mortality of ICU patients and served as an independent predictor of ICU patients' prognosis (OR 3.425, 95% CI 3.717-3.165, P < 0.001). Moreover, the association between critical illness progression, poor outcome, and basophils absence was verified in septic patients. Subsequent investigations revealed the positive relationship between basophils absence and immunosuppression, and suggested the potential of basophils-mediated immunity in predicting the 28-day mortality of ICU patients. Collectively, we identify basophils absence during ICU stay as a novel and unfavorable indicator for evaluating the prognosis of ICU patients and recognizing a branch of ICU patients potentially suitable for intensified treatment and immunoenhancement therapy.

Integrated analysis of multi-omics data reveals T cell exhaustion in sepsis

Background

Sepsis is a heterogeneous disease, therefore the single-gene-based biomarker is not sufficient to fully understand the disease. Higher-level biomarkers need to be explored to identify important pathways related to sepsis and evaluate their clinical significance.

Methods

Gene Set Enrichment Analysis (GSEA) was used to analyze the sepsis transcriptome to obtain the pathway-level expression. Limma was used to identify differentially expressed pathways. Tumor IMmune Estimation Resource (TIMER) was applied to estimate immune cell abundance. The Spearman correlation coefficient was used to find the relationships between pathways and immune cell abundance. Methylation and single-cell transcriptome data were also employed to identify important pathway genes. Log-rank test was performed to test the prognostic significance of pathways for patient survival probability. DSigDB was used to mine candidate drugs based on pathways. PyMol was used for 3-D structure visualization. LigPlot was used to plot the 2-D pose view for receptor-ligand interaction.

Results

Eighty-four KEGG pathways were differentially expressed in sepsis patients compared to healthy controls. Of those, 10 pathways were associated with 28-day survival. Some pathways were significantly correlated with immune cell abundance and five pathways could be used to distinguish between systemic inflammatory response syndrome (SIRS), bacterial sepsis, and viral sepsis with Area Under the Curve (AUC) above 0.80. Seven related drugs were screened using survival-related pathways.

Conclusion

Sepsis-related pathways can be utilized for disease subtyping, diagnosis, prognosis, and drug screening.

Immunological risk factors for sepsis-associated delirium and mortality in ICU patients

Background

A major challenge in intervention of critical patients, especially sepsis-associated delirium (SAD) intervention, is the lack of predictive risk factors. As sepsis and SAD are heavily entangled with inflammatory and immunological processes, to identify the risk factors of SAD and mortality in the intensive care unit (ICU) and determine the underlying molecular mechanisms, the peripheral immune profiles of patients in the ICU were characterized.

Methods

This study contains a cohort of 52 critical patients who were admitted to the ICU of the First Affiliated Hospital of Jinan University. Comorbidity, including sepsis and SAD, of this cohort was diagnosed and recorded. Furthermore, peripheral blood samples were collected on days 1, 3, and 5 of admission for peripheral immune profiling with blood routine examination, flow cytometry, ELISA, RNA-seq, and qPCR.

Results

The patients with SAD had higher mortality during ICU admission and within 28 days of discharge. Compared with survivors, nonsurvivors had higher neutrophilic granulocyte percentage, higher CRP concentration, lower monocyte count, lower monocyte percentage, lower C3 complement level, higher CD14^loCD16⁺ monocytes percentage, and higher levels of IL-6 and TNFα. The CD14^hiCD16^- monocyte percentage manifested favorable prediction values for the occurrence of SAD. Differentially expressed genes between the nonsurvival and survival groups were mainly associated with immune response and metabolism process. The longitudinal expression pattern of SLC2A1 and STIMATE were different between nonsurvivors and survivors, which were validated by qPCR.

Conclusions

Nonsurvival critical patients have a distinct immune profile when compared with survival patients. CD14^hiCD16^- monocyte prevalence and expression levels of SLC2A1 and STIMATE may be predictors of SAD and 28-day mortality in ICU patients.