Identification of Immune-Related Genes as Potential Biomarkers in Early Septic Shock

Abstract

Introduction: Septic shock, a severe manifestation of infection-induced systemic immune response, poses a critical threat resulting in life-threatening multi-organ failure. Early diagnosis and intervention are imperative due to the potential for irreversible organ damage. However, specific and sensitive detection tools for the diagnosis of septic shock are still lacking.

Methods: Gene expression files of early septic shock were obtained from the Gene Expression Omnibus (GEO) database. CIBERSORT analysis was used to evaluate immune cell infiltration. Genes related to immunity and disease progression were identified using weighted gene co-expression network analysis (WGCNA), followed by enrichment analysis. CytoHubba was then employed to identify hub genes, and their relationships with immune cells were explored through correlation analysis. Blood samples from healthy controls and patients with early septic shock were collected to validate the expression of hub genes, and an external dataset was used to validate their diagnostic efficacy.

Results: Twelve immune cells showed significant infiltration differences in early septic shock compared to control, such as neutrophils, M0 macrophages, and natural killer cells. The identified immune and disease-related genes were mainly enriched in immune, cell signaling, and metabolism pathways. In addition, six hub genes were identified (PECAM1, F11R, ITGAL, ICAM3, HK3, and MCEMP1), all significantly associated with M0 macrophages and exhibiting an area under curve of over 0.7. These genes exhibited abnormal expression in patients with early septic shock. External datasets and real-time qPCR validation supported the robustness of these findings.

Conclusion: Six immune-related hub genes may be potential biomarkers for early septic shock.

Introduction: Septic shock, a severe manifestation of infection-induced systemic immune response, poses a critical threat resulting in life-threatening multi-organ failure. Early diagnosis and intervention are imperative due to the potential for irreversible organ damage. However, specific and sensitive detection tools for the diagnosis of septic shock are still lacking.

Methods: Gene expression files of early septic shock were obtained from the Gene Expression Omnibus (GEO) database. CIBERSORT analysis was used to evaluate immune cell infiltration. Genes related to immunity and disease progression were identified using weighted gene co-expression network analysis (WGCNA), followed by enrichment analysis. CytoHubba was then employed to identify hub genes, and their relationships with immune cells were explored through correlation analysis. Blood samples from healthy controls and patients with early septic shock were collected to validate the expression of hub genes, and an external dataset was used to validate their diagnostic efficacy.

Results: Twelve immune cells showed significant infiltration differences in early septic shock compared to control, such as neutrophils, M0 macrophages, and natural killer cells. The identified immune and disease-related genes were mainly enriched in immune, cell signaling, and metabolism pathways. In addition, six hub genes were identified (PECAM1, F11R, ITGAL, ICAM3, HK3, and MCEMP1), all significantly associated with M0 macrophages and exhibiting an area under curve of over 0.7. These genes exhibited abnormal expression in patients with early septic shock. External datasets and real-time qPCR validation supported the robustness of these findings.

Conclusion: Six immune-related hub genes may be potential biomarkers for early septic shock.

Keywords: Biomarkers; Immune infiltration; Septic shock; Weighted gene co-expression network analysis.

Fig. 1.

Immune infiltration analysis. The proportion of immune cell composition (a) and infiltration status of immune cells (b) were evaluated in early septic shock and control samples using the CIBERSORT algorithm. c ESTIMATE, immune, and stromal scores. H0/H24/H48: blood samples from ICU patients enrolled at the onset of septic shock were collected at three points: 30 min, 24 h, and 48 h.

Fig. 2.

WGCNA analysis. a Cluster analysis of the top 25% genes ranked by variance in early septic shock samples was performed using the average linkage method and presented as dendrogram and trait heatmap. b Scale-free fit indices and mean connectivity for different soft-threshold powers (β = 9). c, d Module eigengenes were clustered through the average chained hierarchical clustering method, and the cutting tree height of 0.3 was selected for dynamic merging to obtain candidate modules. e Correlation analysis of different modules with time, SAPS II, ESTIMATE, immune, and stromal scores.

Fig. 3.

Identification of core genes. a Correlation analysis of genes in black module with time and immune score. b Correlation analysis of genes in blue module with time and immune score. c The time-related genes and immune score-related genes were overlapped to obtain core genes using Venn diagram. GO enrichment (d) and KEGG enrichment (e) analysis of core genes.

Fig. 4.

Identification of hub genes. a PPI network of core genes. b Identification of hub genes using CytoHubba.

Fig. 5.

Expression levels of hub genes in early septic shock samples and controls. a In dataset GSE57065. b Using 13 blood samples from healthy controls (n = 13) and 33 blood samples from patients with septic shock (n = 11), collected at 30 min (0 h), 24 h, and 48 h after the onset of septic shock.

Fig. 6.

Hub genes and immune cells. a Correlation analysis of hub genes with immune cells. b Correlation analysis of hub genes with M0 macrophage.

Fig. 7.

Expression of hub gene in different immune cell types in dataset GSE115736.

Fig. 8.

ROC analysis of hub genes. a Using 28 samples of septic shock (H0) and 25 controls in dataset GSE57065. b Using 19 samples of septic shock and 40 controls in dataset GSE154918.

Fig. 9.

Drug prediction. Drug-gene interactions (genes on the left, potential drugs on the right).