ArticleBMC genomics2023
A comprehensive analysis of immune features and construction of an immune gene diagnostic model for sepsis.
Article in BMC genomics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Exploring the mechanism of polymorphonuclear neutrophils against sepsis based on immune model.Annals of medicine · 2026Review
- Multi-omics integration and machine learning identify a novel gene signature consisting of CCDC141, CHI3L2, RIMKLB, and PDLIM7 in bronchopulmonary dysplasia associated with neutrophil-driven immune dysregulation.Clinics (Sao Paulo, Brazil) · 2026Article
- Combination of machine learning and protein‑protein interaction network established one ATM‑DPP4‑TXN ferroptotic diagnostic model with experimental validation.Molecular medicine reports · 2025Article
- The relationship between immune cell infiltration and necroptosis gene expression in sepsis: an analysis using single-cell transcriptomic data.Frontiers in cellular and infection microbiology · 2025Article
- Comprehensive analysis of sialylation-related genes and construct the prognostic model in sepsis.Scientific reports · 2024Article
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Authors and funding
7 authors.
Funding
Abstract
Sepsis is a life-threatening syndrome resulting from immune system dysfunction that is caused by infection. It is of great importance to analyze the immune characteristics of sepsis, identify the key immune system related genes, and construct diagnostic models for sepsis. In this study, the sepsis transcriptome and expression profiling data were merged into an integrated dataset containing 277 sepsis samples and 117 non-sepsis control samples. Single-sample gene set enrichment analysis (ssGSEA) was used to assess the immune cell infiltration. Two sepsis immune subtypes were identified based on the 22 differential immune cells between the sepsis and the healthy control groups. Weighted gene co-expression network analysis (WCGNA) was used to identify the key module genes. Then, 36 differentially expressed immune-related genes were identified, based on which a robust diagnostic model was constructed with 11 diagnostic genes. The expression of 11 diagnostic genes was finally assessed in the training and validation datasets respectively. In this study, we provide comprehensive insight into the immune features of sepsis and establish a robust diagnostic model for sepsis. These findings may provide new strategies for the early diagnosis of sepsis in the future.
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