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. 2022 Dec 21:13:1110602.
doi: 10.3389/fimmu.2022.1110602. eCollection 2022.

Predicting the prognosis in patients with sepsis by a pyroptosis-related gene signature

Shuang Liang  1 , Manyu Xing  1 , Xiang Chen  1 , Jingyi Peng  1 , Zongbin Song  1 , Wangyuan Zou  1   2
Affiliations

Predicting the prognosis in patients with sepsis by a pyroptosis-related gene signature

Shuang Liang et al. Front Immunol. .

Abstract

Background: Sepsis remains a life-threatening disease with a high mortality rate that causes millions of deaths worldwide every year. Many studies have suggested that pyroptosis plays an important role in the development and progression of sepsis. However, the potential prognostic and diagnostic value of pyroptosis-related genes in sepsis remains unknown.

Methods: The GSE65682 and GSE95233 datasets were obtained from Gene Expression Omnibus (GEO) database and pyroptosis-related genes were obtained from previous literature and Molecular Signature Database. Univariate cox analysis and least absolute shrinkage and selection operator (LASSO) cox regression analysis were used to select prognostic differentially expressed pyroptosis-related genes and constructed a prognostic risk score. Functional analysis and immune infiltration analysis were used to investigate the biological characteristics and immune cell enrichment in sepsis patients who were classified as low- or high-risk based on their risk score. Then the correlation between pyroptosis-related genes and immune cells was analyzed and the diagnostic value of the selected genes was assessed using the receiver operating characteristic curve.

Results: A total of 16 pyroptosis-related differentially expressed genes were identified between sepsis patients and healthy individuals. A six-gene-based (GZMB, CHMP7, NLRP1, MYD88, ELANE, and AIM2) prognostic risk score was developed. Based on the risk score, sepsis patients were divided into low- and high-risk groups, and patients in the low-risk group had a better prognosis. Functional enrichment analysis found that NOD-like receptor signaling pathway, hematopoietic cell lineage, and other immune-related pathways were enriched. Immune infiltration analysis showed that some innate and adaptive immune cells were significantly different between low- and high-risk groups, and correlation analysis revealed that all six genes were significantly correlated with neutrophils. Four out of six genes (GZMB, CHMP7, NLRP1, and AIM2) also have potential diagnostic value in sepsis diagnosis.

Conclusion: We developed and validated a novel prognostic predictive risk score for sepsis based on six pyroptosis-related genes. Four out of the six genes also have potential diagnostic value in sepsis diagnosis.

Keywords: diagnosis; gene; prediction; prognosis; pyroptosis; sepsis.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Identification of differentially expressed pyroptosis-related genes and interaction. (A) Volcano plot of DEGs in the GSE65682 dataset between sepsis patients and healthy individuals. (B) Venn plot of the DEGs and pyroptosis genes. (C) Heatmap of pyroptosis-related DEGs. (D) Protein-protein interaction (PPI) network analysis of proteins encoded by the pyroptosis-related DEGs.
Figure 2
Figure 2
Consensus clustering analysis based on pyroptosis-related DEGs. (A) Consensus matrix when k=2. (B) Heatmap of pyroptosis-related DEGs expression and clinical characteristics in the two clusters. (C) Kaplan-Meier curves analysis for the survival of patients between the two clusters. (D) Box plots of pyroptosis-related DEGs expression level in the two clusters. ns, not significant; *P< 0.05,***P< 0.001, ****P< 0.0001.
Figure 3
Figure 3
Construction of pyroptosis-related prognostic risk score and prediction of the prognosis in the discovery cohort. (A) Univariate cox regression analysis of survival for 16 pyroptosis-related DEGs, and 10 genes with a P< 0.2. (B) LASSO-Cox regression of the 10 candidate genes. (C) Cross-validation for tuning predictor selection. (D) Distribution of patients based on the risk score. (E) Survival time and status of patients. (F) Kaplan-Meier curves analysis for the survival of patients in low- and high-risk groups. (G) Time-dependent receiver operating characteristic curve for 7-, 14-, and 28-day survival of sepsis patients. (H) Univariate and multivariate cox regression.
Figure 4
Figure 4
Validation of the pyroptosis-related prognostic risk score in the validation cohort, the test cohort, and the GSE95233 cohort. (A, E) Distribution of patients based on the risk score in the validation cohort (A) and the test cohort (E). (B, F) Survival time and status of patients in the validation cohort (B) and the test cohort (F). (C, G) Kaplan-Meier curves analysis for the survival of patients in low- and high-risk groups in the validation cohort (C) and the test cohort (G). (D, H) Time-dependent receiver operating characteristic curve for 7-, 14-, and 28-day survival of patients in the validation cohort (D) and the test cohort (H). (I) The survival of patients in low- and high-risk groups in the GSE95233 cohort. (J) Receiver operating characteristic curve for 28 days survival of patients in the GSE95233 dataset.
Figure 5
Figure 5
Construction of nomogram based on the pyroptosis-related prognostic risk score and clinical characteristic. (A) Nomogram for predicting 7-, 14-, and 28-day survival for sepsis patients. (B) Time-dependent receiver operating characteristic curves for 7-, 14-, and 28-day survival of patients. (C-E) Calibration curve of the nomogram for predicting 7-, 14-, and 28-day survival of patients.
Figure 6
Figure 6
Functional enrichment analysis of the DEGs between low- and high-risk groups. (A) Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. (B) Gene Ontology (GO) enrichment analysis. BP, Biological process; CC, Cellular component; MF, Molecular function.
Figure 7
Figure 7
Immune infiltration analysis between low- and high-risk groups in the GSE65682 dataset. (A) Box plots of 22 types of immune cell composition between low- and high-risk groups. *P< 0.05; **P< 0.01; ***P< 0.001; ****P< 0.0001. (B) The correlation between the selected six pyroptosis-related genes and the immune cells. NS, Not significant, *P< 0.05; **P< 0.01; ***P< 0.001.
Figure 8
Figure 8
The performance of the selected six pyroptosis-related genes in the diagnosis of sepsis. (A, B) Receiver operating characteristic curves in the GSE65682 dataset (A) and GSE95233 dataset (B). AUC, Area under the curve. (C, D) Box plots of the expression levels of the six genes between sepsis patients and healthy individuals in the GSE65682 dataset (C) and GSE95233 dataset (D). ***P< 0.001, ****P< 0.0001.
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