Abstract
Background: Sepsis is well-known to alter innate and adaptive immune responses for sustained periods after initiation by an invading pathogen. Identification of immune cell characteristics may shed light on the immune signature of patients with sepsis and further indicate the appropriate immune-modulatory therapy for distinct populations. Therefore, we aimed to establish an immune model to classify sepsis into different immune endotypes via transcriptomics data analysis of previously published cohort studies. Methods: Datasets from two observational cohort studies that included 585 consecutive sepsis patients admitted to two intensive care units were downloaded as a training cohort and an external validation cohort. We analyzed genome-wide gene expression profiles in blood from these patients by using machine learning and bioinformatics. Results: The training cohort and the validation cohort had 479 and 106 patients, respectively. Principal component analysis indicated that two immune subphenotypes associated with sepsis, designated the immunoparalysis endotype, and immunocompetent endotype, could be distinguished clearly. In the training cohort, a higher cumulative 28-day mortality was found in patients classified as having the immunoparalysis endotype, and the hazard ratio was 2.32 (95% CI: 1.53-3.46 vs. the immunocompetent endotype). External validation further demonstrated that the present model could categorize sepsis into the immunoparalysis and immunocompetent type precisely and efficiently. The percentages of 4 types of immune cells (M0 macrophages, M2 macrophages, naïve B cells, and naïve CD4 T cells) were significantly associated with 28-day cumulative mortality (P < 0.05). Conclusion: The present study developed a comprehensive tool to identify the immunoparalysis endotype and immunocompetent status in hospitalized patients with sepsis and provides novel clues for further targeting of therapeutic approaches.