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. 2022 Dec 2:13:994295.
doi: 10.3389/fimmu.2022.994295. eCollection 2022.

Construction and validation of a robust prognostic model based on immune features in sepsis

Yongxin Zheng  1   2 , Baiyun Liu  1   2 , Xiumei Deng  1   2 , Yubiao Chen  1   2 , Yongbo Huang  1   2 , Yu Zhang  1   2 , Yonghao Xu  1   2 , Ling Sang  1   2 , Xiaoqing Liu  1   2 , Yimin Li  1   2
Affiliations

Construction and validation of a robust prognostic model based on immune features in sepsis

Yongxin Zheng et al. Front Immunol. .

Erratum in

Abstract

Purpose: Sepsis, with life-threatening organ failure, is caused by the uncontrolled host response to infection. Immune response plays an important role in the pathophysiology of sepsis. Immune-related genes (IRGs) are promising novel biomarkers that have been used to construct the diagnostic and prognostic model. However, an IRG prognostic model used to predict the 28-day mortality in sepsis was still limited. Therefore, the study aimed to develop a prognostic model based on IRGs to identify patients with high risk and predict the 28-day mortality in sepsis. Then, we further explore the circulating immune cell and immunosuppression state in sepsis.

Materials and methods: The differentially expressed genes (DEGs), differentially expressed immune-related genes (DEIRGs), and differentially expressed transcription factors (DETFs) were obtained from the GEO, ImmPort, and Cistrome databases. Then, the TFs-DEIRGs regulatory network and prognostic prediction model were constructed by Cox regression analysis and Pearson correlation analysis. The external datasets also validated the reliability of the prognostic model. Based on the prognostic DEIRGs, we developed a nomogram and conducted an independent prognosis analysis to explore the relationship between DEIRGs in the prognostic model and clinical features in sepsis. Besides, we further evaluate the circulating immune cells state in sepsis.

Results: A total of seven datasets were included in our study. Among them, GSE65682 was identified as a discovery cohort. The results of GSEA showed that there is a significant correlation between sepsis and immune response. Then, based on a P value <0.01, 69 prognostic DEIRGs were obtained and the potential molecular mechanisms of DEIRGs were also clarified. According to multivariate Cox regression analysis, 22 DEIRGs were further identified to construct the prognostic model and identify patients with high risk. The Kaplan-Meier survival analysis showed that high-risk groups have higher 28-day mortality than low-risk groups (P=1.105e-13). The AUC value was 0.879 which symbolized that the prognostic model had a better accuracy to predict the 28-day mortality. The external datasets also prove that the prognostic model had an excellent prediction value. Furthermore, the results of correlation analysis showed that patients with Mars1 might have higher risk scores than Mars2-4 (P=0.002). According to the previous study, Mars1 endotype was characterized by immunoparalysis. Thus, the sepsis patients in high-risk groups might exist the immunosuppression. Between the high-risk and low-risk groups, circulating immune cells types were significantly different, and risk score was significantly negatively correlated with naive CD4+ T cells (P=0.019), activated NK cells (P=0.0045), monocytes (P=0.0134), and M1 macrophages (P=0.0002).

Conclusions: Our study provides a robust prognostic model based on 22 DEIRGs which can predict 28-day mortality and immunosuppression status in sepsis. The higher risk score was positively associated with 28-day mortality and the development of immunosuppression. IRGs are a promising biomarker that might facilitate personalized treatments for sepsis.

Keywords: 28-day mortality; immune; immunosuppression; prognostic model; 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
Flowchart of data analysis and validation.
Figure 2
Figure 2
Screening DEGs, DEIRGs and DETFs. (A) Volcano plot showing DEGs in GSE66890; (B) Venn diagram showed DEIRGs; (C) Venn diagram showed DETFs; (D) Volcano plot showing DEIRGs; (E) Volcano plot showing DETFs. Based on the |fold change|>0.5 and FDR<0.05, the red points represent upregulated genes and the green points represent downregulated genes. No significant differences are showed in black.
Figure 3
Figure 3
Exploring the difference of immune response between sepsis and healthy by using GSEA. (A) The enriched gene sets in GO collection; (B) The results of GO analysis from GSEA; (C) The enriched gene sets in KEGG collection; (D) The results of KEGG analysis from GSEA. The enrichment score of curve above 0 points indicates that the gene sets were activated in healthy. The curve below 0 points indicates that the gene sets were activated in sepsis. p.adjust, adjusted p-value; NES, normalized enrichment score.
Figure 4
Figure 4
Prognostic DEIRGs and regulatory network between DETFs and prognostic DEIRGs. (A) Forest plot for prognostic DEIRGs in sepsis. Red and green dots were recognized as high-risk and low-risk, respectively; (B) Regulatory network between prognostic DEIRGs and DETFs. The red and green circles indicate high-risk DEIRGs and low-risk DEIRGs, respectively. The yellow triangles were applied to symbolize the DETFs. Moreover, the red and green lines were used to indicate a positive and negative correlation between prognostic DEIRGs and DETFs.
Figure 5
Figure 5
Construction of prognostic model based on 22 DEGs. (A) Kaplan–Meier survival analysis of 28-day mortality between high-risk groups (red) and low-risk groups (blue). The color of each survival line indicated the 95% CI of probability of survival at each time point. (B) The ROC curve showed the AUC value of prognostic model. (C) The risk score analysis between high-risk and low-risk groups. (D) The survival status analysis between high-risk and low-risk groups. (E) The differentially expression analysis of 22 DEIRGs in prognostic model from 479 sepsis patients.
Figure 6
Figure 6
The prognostic efficacy of IRGs prognostic model. (A) The ROC curve of E-MTAB-4451 dataset. (B) The ROC curve of E-MTAB-5273 dataset. (C) The ROC curve of E-MTAB-5274 dataset. (D) The ROC curve of GSE95233 dataset. (E) The ROC curve of GSE106878 dataset. (F) The ROC curve of GSE63042 dataset.
Figure 7
Figure 7
The results of univariate independent prognostic analysis and multivariate independent prognostic analysis. (A) Univariate independent prognostic analysis. (B) Multivariate independent prognostic analysis. The red dots and green dots in the forest map indicated that the clinical feature was a high-risk factor and low-risk factor, respectively. ICUA, ICU acquired infection.
Figure 8
Figure 8
Relationships between clinical features and DEIRGs in prognostic model. (A) Different expression of CD1D between the ICUA/NICUA in sepsis. (B–R) Different expression of DEIRGs in prognostic model between Mars1 and Mars2-4 in sepsis. ICUA, ICU acquired infection; NICUA, No ICU acquired infection; Mars, molecular diagnosis and risk stratification of sepsis.
Figure 9
Figure 9
A constructed nomogram for 28-day mortality prediction of a patients with sepsis. ICUA, ICU acquired infection; Mars, molecular diagnosis and risk stratification of sepsis.
Figure 10
Figure 10
The functional enrichment analysis for DEIRGs in prognostic model. (A) Biological process. (B) Cell component. (C) Molecular function. (D) KEGG pathway enrichment analysis.
Figure 11
Figure 11
Comparison and correlation of circulating immune cells between low-risk and high-risk groups. (A) Comparison of circulating immune cells between low-risk and high-risk groups via CIBERSORTx. (B–J) Correlation between risk scores and circulating immune cells via Spearman correlation analysis. *p < 0.05.
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