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. 2024 Apr 22;25(8):4574.
doi: 10.3390/ijms25084574.

Free Radical-Associated Gene Signature Predicts Survival in Sepsis Patients

Anlin Feng  1   2 , Marissa D Pokharel  1   3 , Ying Liang  1   2 , Wenli Ma  1   2 , Saurabh Aggarwal  1   3 , Stephen M Black  1   2   3 , Ting Wang  1   2   3
Affiliations

Free Radical-Associated Gene Signature Predicts Survival in Sepsis Patients

Anlin Feng et al. Int J Mol Sci. .

Abstract

Sepsis continues to overwhelm hospital systems with its high mortality rate and prevalence. A strategy to reduce the strain of sepsis on hospital systems is to develop a diagnostic/prognostic measure that identifies patients who are more susceptible to septic death. Current biomarkers fail to achieve this outcome, as they only have moderate diagnostic power and limited prognostic capabilities. Sepsis disrupts a multitude of pathways in many different organ systems, making the identification of a single powerful biomarker difficult to achieve. However, a common feature of many of these perturbed pathways is the increased generation of reactive oxygen species (ROS), which can alter gene expression, changes in which may precede the clinical manifestation of severe sepsis. Therefore, the aim of this study was to evaluate whether ROS-related circulating molecular signature can be used as a tool to predict sepsis survival. Here we created a ROS-related gene signature and used two Gene Expression Omnibus datasets from whole blood samples of septic patients to generate a 37-gene molecular signature that can predict survival of sepsis patients. Our results indicate that peripheral blood gene expression data can be used to predict the survival of sepsis patients by assessing the gene expression pattern of free radical-associated -related genes in patients, warranting further exploration.

Keywords: ROS; molecular signature; prognosis; sepsis.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
ROS-related genes and Sepsis Survival genes reveal 37-gene signature. (A) KEGG analysis was used to identify pathways enriched with ROS-related genes. (B) Comparison of differentially expressed genes between sepsis survival group compared to ROS-related genes reveals 37 common genes. (C) KEGG analysis was used to identify pathways enriched with 37-gene signature.
Figure 2
Figure 2
Heatmap of 37-gene signature expression in the discovery cohort. Heatmap of 37-gene signature expression reveals expression patterns between low and high-risk sepsis groups.
Figure 3
Figure 3
The 37-gene signature-based sepsis risk score differentiates sepsis high-risk patients from low-risk patients in both the discovery and validation cohort. (A) Box plot of risk scores in low and high-risk sepsis patients in both the discovery and validation cohorts. (B) ROC curves of the gene signature in distinguishing low and high-risk sepsis patients.
Figure 4
Figure 4
PCA plot of 37-gene signature in both Discovery and Validation Cohorts. Principal component analysis (PCA) on our 37-gene expression model was performed to reduce dimensionality and assess the similarity between each individual sample. In both the Discovery (A) and Validation (B) cohorts, the PCA showed that the 37 gene signature can entirely or mostly differentiate the high-risk sepsis patients from the low-risk sepsis patients. This PCA analysis represents 46.6–52.5% of the variable expression data.
Figure 5
Figure 5
Immune Profiling of PBMCs in sepsis low and high-risk groups. KEGG analysis for (A) up-regulated or (B) down-regulated DEGs in low sepsis survival scores (C) Gene set variation analysis heatmap reveals the variations of pathways between high and low sepsis survival scores. (D) Using CIBERSORT, immune cell proportions in PBMCs were estimated. (E) In patients with high sepsis survival scores, neutrophils are predicted to be increased, while CD8+ T and NK cells are predicted to be decreased. * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001. (F,G) T and NK cell pathways were significantly decreased in patients with high sepsis survival scores. NES: normalized enrichment score.
Figure 6
Figure 6
Density distribution plot of random gene signatures from the whole genome genes and only sepsis survival-related genes. Random gene signatures (37 genes) were selected from whole genome or sepsis survival-related genes and the performance of survival prediction was calculated as AUC (sum of AUC in discovery and validation cohorts). Compared with 10,000 randomly selected gene signatures, the 37-ROS-gene signature (AUC = 1.88, black inverted triangle) performs better than 99% of the gene signatures in both groups.
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