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Identification of a 3-gene signature predicting 28-day mortality for sepsis
Yiqian Zeng1, Yutian Liao2, Yang Wang1
1Department of Trauma Intensive Care Unit, Zhuzhou Hospital Affiliated to Xiangya School of Medicine, Central South University, Zhuzhou, China.
Abstract:
Sepsis often leads to unpredictable consequences. The prognosis of sepsis has not been largely improved. We tried to construct a prognostic gene model related to the 28-day mortality of sepsis to identify the risk of mortality and improve the outcome early. We identified the modules associated with 28-day mortality by weighted gene co-expression network analysis from the microarray data of GSE65682. Protein-protein interaction network analysis and univariate Cox regression were conducted to identify hub genes for constructing a prognostic model. Finally, the model was evaluated for robustness. The correlation between the model and immune cells was investigated. The cyan module has a significant negative relationship with 28-day mortality. A risk model was developed to predict prognosis, utilizing macrophage expressed gene 1, CX3C chemokine receptor 1, and human leukocyte antigen-DRB1. The model's expression was found to be higher in the group with lower risk, while the group with higher risk had a higher 28-day mortality rate. These findings were validated using both the test and whole sets. Three genes were positively associated with monocyte expression. We constructed a septic prognostic model with 3 genes, including macrophage expressed gene 1, CX3C chemokine receptor 1, and human leukocyte antigen-DRB1. The expression of them had a significant negative relationship with the 28-day mortality and may influenced monocyte function.
Insights
Researchers developed a 3-gene sepsis prognostic model to predict 28-day mortality risk. This model, utilizing macrophage expressed gene 1, CX3C chemokine receptor 1, and human leukocyte antigen-DRB1, may improve early sepsis outcome prediction.
Area of Science:
- Genomics
- Bioinformatics
- Immunology
Background:
- Sepsis prognosis remains challenging, with limited improvements in outcomes.
- Early identification of mortality risk is crucial for timely intervention in sepsis patients.
Purpose of the Study:
- To construct a robust prognostic gene model for predicting 28-day sepsis mortality.
- To identify key genes associated with sepsis prognosis and improve patient outcomes.
Main Methods:
- Weighted gene co-expression network analysis (WGCNA) on GSE65682 microarray data.
- Protein-protein interaction network analysis and univariate Cox regression to identify hub genes.
- Development and validation of a 3-gene prognostic model.
Main Results:
- A cyan module significantly correlated with 28-day mortality was identified.
- A prognostic model using macrophage expressed gene 1, CX3C chemokine receptor 1, and human leukocyte antigen-DRB1 was developed.
- Higher model expression correlated with lower risk and mortality; validated across datasets. Three genes positively associated with monocyte expression.
Conclusions:
- A novel 3-gene prognostic model for sepsis 28-day mortality was successfully constructed and validated.
- The identified genes (macrophage expressed gene 1, CX3C chemokine receptor 1, HLA-DRB1) show potential for early risk stratification.
- The model's association with monocyte expression suggests a link between these genes and immune cell function in sepsis.
