ArticleFrontiers in immunology2023
An immune-related gene signature predicts the 28-day mortality in patients with sepsis.
Article in Frontiers in immunology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 12 citations in OpenAlex.
- Enterocutaneous Fistula-Associated Sepsis and Mortality: Development and Validation of a Multimodal Artificial Intelligence Prediction Model.JMIR medical informatics · 2026Article
- Identification of a prognostic signature consisting of three macrophage-related genes for glioblastoma based on bulk and single-cell transcriptomes analyses.Annals of medicine · 2025Article
- Integration of single-cell and RNA-seq analysis reveals sepsis heterogeneity and prognostic significance of FCGR3A+ Macrophage subtypes.Biochemistry and biophysics reports · 2025Article
- Fibulin2 is a good diagnostic and prognostic indicator for sepsis.BMC infectious diseases · 2025Article
- Identification and validation of an endotoxin tolerance-based prognostic model with therapeutic insights in sepsis.Frontiers in immunology · 2025Article
- AI-driven multi-omics profiling of sepsis immunity in the digestive system.Frontiers in immunology · 2025Review
- Time-Course Renal and Pulmonary Injury Analysis and Bioinformatics Screening of Core Pathogenic Genes and Immune Cell Infiltration Patterns in a Sepsis.Journal of inflammation research · 2025Article
- Article
- Development and validation of an interpretable machine learning for mortality prediction in patients with sepsis.Frontiers in artificial intelligence · 2024Article
- A hypoxia- and lactate metabolism-related gene signature to predict prognosis of sepsis: discovery and validation in independent cohorts.European journal of medical research · 2023Article
Corrections and comments
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Authors and funding
11 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Sepsis is the leading cause of death in intensive care units and is characterized by multiple organ failure, including dysfunction of the immune system. In the present study, we performed an integrative analysis on publicly available datasets to identify immune-related genes (IRGs) that may play vital role in the pathological process of sepsis, based on which a prognostic IRG signature for 28-day mortality prediction in patients with sepsis was developed and validated. Methods: Weighted gene co-expression network analysis (WGCNA), Cox regression analysis and least absolute shrinkage and selection operator (LASSO) estimation were used to identify functional IRGs and construct a model for predicting the 28-day mortality. The prognostic value of the model was validated in internal and external sepsis datasets. The correlations of the IRG signature with immunological characteristics, including immune cell infiltration and cytokine expression, were explored. We finally validated the expression of the three IRG signature genes in blood samples from 12 sepsis patients and 12 healthy controls using qPCR. Results: We established a prognostic IRG signature comprising three gene members ( Discussion: This study presents an innovative IRG signature for 28-day mortality prediction in sepsis patients, which may be used to facilitate stratification of risky sepsis patients and evaluate patients' immune state.
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