Integrated Analysis of Gene Co-Expression Network and Prediction Model Indicates Immune-Related Roles of the Identified Biomarkers in Sepsis and Sepsis-Induced Acute Respiratory Distress Syndrome

Abstract

Sepsis is a series of clinical syndromes caused by immunological response to severe infection. As the most important and common complication of sepsis, acute respiratory distress syndrome (ARDS) is associated with poor outcomes and high medical expenses. However, well-described studies of analysis-based researches, especially related bioinformatics analysis on revealing specific targets and underlying molecular mechanisms of sepsis and sepsis-induced ARDS (sepsis/se-ARDS), still remain limited and delayed despite the era of data-driven medicine. In this report, weight gene co-expression network based on data from a public database was constructed to identify the key modules and screen the hub genes. Functional annotation by enrichment analysis of the modular genes also demonstrated the key biological processes and signaling pathway; among which, extensive immune-involved enrichment was remarkably associated with sepsis/se-ARDS. Based on the differential expression analysis, least absolute shrink and selection operator, and multivariable logistic regression analysis of the screened hub genes, SIGLEC9, TSPO, CKS1B and PTTG3P were identified as the candidate biomarkers for the further analysis. Accordingly, a four-gene-based model for diagnostic prediction assessment was established and then developed by sepsis/se-ARDS risk nomogram, whose efficiency was verified by calibration curves and decision curve analyses. In addition, various machine learning algorithms were also applied to develop extra models based on the four genes. Receiver operating characteristic curve analysis proved the great diagnostic and predictive performance of these models, and the multivariable logistic regression of the model was still found to be the best as further verified again by the internal test, training, and external validation cohorts. During the development of sepsis/se-ARDS, the expressions of the identified biomarkers including SIGLEC9, TSPO, CKS1B and PTTG3P were all regulated remarkably and generally exhibited notable correlations with the stages of sepsis/se-ARDS. Moreover, the expression levels of these four genes were substantially correlated during sepsis/se-ARDS. Analysis of immune infiltration showed that multiple immune cells, neutrophils and monocytes in particular, might be closely involved in the process of sepsis/se-ARDS. Besides, SIGLEC9, TSPO, CKS1B and PTTG3P were considerably correlated with the infiltration of various immune cells including neutrophils and monocytes during sepsis/se-ARDS. The discovery of relevant gene co-expression network and immune signatures might provide novel insights into the pathophysiology of sepsis/se-ARDS.

Keywords: ARDS; diagnostic biomarker; gene co-expression network analysis; immune cell infiltration; prediction model; sepsis.

Figure 1

Co-expression network and module–trait relations of sepsis/se-ARDS. (A) Sample dendrogram and trait heatmap of sepsis/se-ARDS. (B) Cluster dendrogram of all genes based on key modules. (C) Co-expression network heatmap based on dissTOM. (D) Relationships of modules with clinical traits.

Figure 2

Identification of hub genes associated with sepsis/se-ARDS. Correlation between module membership (MM) and gene significance (GS) for the (A, B) control, (C, D) sepsis, and (E, F) se-ARDS groups in the key magenta (left) and midnight blue (right) modules.

Figure 3

Functional and pathway enrichment analyses of the key modules of sepsis/se-ARDS. (A) GSEA of innate immune response (left) and cell activation involved in immune response (right). (B) GSVA-based analysis of biological function enrichment by bubble plot. (C) GSVA-based analysis of KEGG pathway enrichment by bar-plot.

Figure 4

Construction of diagnostic prediction model for sepsis/se-ARDS. (A) Thirty-six hub genes differentially expressed between sepsis/se-ARDS samples and controls. (B) Different coefficients values and (C) binomial deviance values within the range of lambda. (D) Nomogram model for sepsis/se-ARDS prediction based on the identified biomarkers, SIGLEC9, TSPO, CKS1B, and PTTG3P. (E) Decision curve analysis and (F) calibration curve analysis of the sepsis/se-ARDS risk nomogram model.

Figure 5

Comparisons of ROC curves and AUC performances. The ROC curve analysis of sepsis/se-ARDS diagnostic efficacy of SIGLEC9, TSPO, CKS1B, and PTTG3P in (A) GSE32707, (B) GSE28750 and (C) GSE57065. (D) The ROC curve analysis of sepsis/se-ARDS prediction efficacy of the four-gene model in sepsis, se-ARDS or sepsis/se-ARDS cohorts(left) and (E) in training or test cohorts (right) of GSE32707. (F) The ROC curve analysis of sepsis/se-ARDS prediction efficacy of the four-gene models in validation cohorts of GSE28750 and GSE57065.

Figure 6

Expression of marker genes during sepsis/se-ARDS development. (A) Expression regulation of SIGLEC9, TSPO, CKS1B, and PTTG3P during the course of sepsis/se-ARDS. (B) Expression variation trends of SIGLEC9, TSPO, CKS1B, and PTTG3P among the control, sepsis day 0 and 7 groups. (C) Expression variation trends of CKS1B, PTTG3P, SIGLEC9, and TSPO among the control, se-ARDS day 0 and 7 groups. (D) Correlation analysis of the four biomarkers during sepsis/se-ARDS development. Correlation analysis between (E) SIGLEC9 and TSPO and between (F) CKS1B and PTTG3P. *P < 0.05; **P < 0.01; ***P < 0.001.

Figure 7

Analysis of immune cell infiltration during sepsis/se-ARDS development. (A) Hierarchical clustering heatmap of the subpopulations of 22 types of infiltrating immune cells among all samples. (B) Violin plot of the significantly dysregulated immune cells in sepsis/se-ARDS. (C) Correlation analysis of the 22 types of infiltrating immune cells. (D) Correlation analysis of the four biomarkers with the levels of immune cell infiltration. *P < 0.05; **P < 0.01; ***P < 0.001.