Identification of Potential Early Diagnostic Biomarkers of Sepsis

Abstract

Objective: The goal of this article was to identify potential biomarkers for early diagnosis of sepsis in order to improve their survival.

Methods: We analyzed differential gene expression between adult sepsis patients and controls in the GSE54514 dataset. Coexpression analysis was used to cluster coexpression modules, and enrichment analysis was performed on module genes. We also analyzed differential gene expression between neonatal sepsis patients and controls in the GSE25504 dataset, and we identified the subset of differentially expressed genes (DEGs) common to neonates and adults. All samples in the GSE54514 dataset were randomly divided into training and validation sets, and diagnostic signatures were constructed using least absolute shrink and selection operator (LASSO) regression. The key gene signature was screened for diagnostic value based on area under the receiver operating characteristic curve (AUC). STEM software identified dysregulated genes associated with sepsis-associated mortality. The ssGSEA method was used to quantify differences in immune cell infiltration between sepsis and control samples.

Results: A total of 6316 DEGs in GSE54514 were obtained spanning 10 modules. Module genes were mainly enriched in immune and metabolic responses. Screening 51 genes from among common genes based on AUC > 0.7 led to a LASSO model for the training set. We obtained a 25-gene signature, which we validated in the validation set and in the GSE25504 dataset. Among the signature genes, SLC2A6, C1ORF55, DUSP5 and RHOB were recognized as key genes (AUC > 0.75) in both the GSE54514 and GSE25504 datasets. SLC2A6 was identified by STEM as associated with sepsis-associated mortality and showed the strongest positive correlation with infiltration levels of Th1 cells.

Conclusion: In summary, our four key genes may have important implications for the early diagnosis of sepsis patients. In particular, SLC2A6 may be a critical biomarker for predicting survival in sepsis.

Keywords: LASSO model; SLC2A6; WGCNA; diagnostic biomarker; early diagnosis; sepsis.

Figure 1

Study flowchart. Sequencing data from sepsis patients and controls in GSE54514 and GSE25504 datasets were analyzed by bioinformatics in order to identify early potential biomarkers of sepsis.

Figure 2

Coexpression network of differentially expressed genes. (A) Genes differentially expressed between sepsis patients and controls in the GSE54514 dataset. Green nodes represent down-regulation in sepsis; red nodes, up-regulation; and grey nodes, no significant difference from controls. The five genes most significantly up- or down-regulated, based on log₂(fold change), are marked using symbols. (B) Genes differentially expressed between sepsis patients and controls in the GSE25504 dataset. Green nodes represent down-regulation in sepsis; red nodes, up-regulation; and grey nodes, no significant difference from controls. The five genes most significantly up- or down-regulated, based on log₂(fold change), are marked using symbols. (C) Intersection of differentially expressed genes (DEGs) in the GSE54514 and GSE25504 datasets. The count on the left refers to DEGs unique to GSE54514; the count in the middle, DEGs common to both datasets; and the count on the right, DEGs unique to GSE25504. (D) Correlation between soft threshold power and scale-free topology model. (E) Cluster tree of coexpression modules of significantly different gene expression. Different colors represent different modules. (F) Crosstalk between modules. The more crosstalk between module genes and other genes, the greater the proportion of the ring is occupied by that module. Different colors represent different modules.

Figure 3

Biological functions and KEGG pathways enriched for module genes. (A) Significant up- or down-regulated biological processes in module genes of sepsis patients relative to controls, as quantified by gene set variation analysis (GSVA). FC, fold change. (B) Significant up- or down-regulated KEGG signaling pathways in module genes of sepsis patients relative to controls, as quantified by GSVA. (C) Up- or down-regulated KEGG pathways of gene set enrichment results in sepsis patients relative to controls. A P value < 0.05 was considered statistically significant.

Figure 4

Potential key genes for the diagnosis of sepsis. (A) The gene signature selection of optimal parameter (lambda) in LASSO model. (B) LASSO coefficient profiles of the 25 differentially expressed genes selected by the optimal lambda. (C) The receiver operating characteristic (ROC) curves of the gene signature in the training set of GSE54514. (D) The ROC curves of the gene signature in the validation set of GSE54514. (E) The ROC curves of the gene signature in GSE25504. (F) The genes in GSE54514 and GSE25504 with an area under the ROC curve (AUC) greater than 0.75 are indicated together with their mean AUCs. (G) Differential expression of key genes between sepsis patients and controls in GSE54514. ***P < 0.001.

Figure 5

Persistently dysregulated gene expression during sepsis development. (A) The expression of SCL2A6 was persistently elevated during sepsis development. (B) Pearson correlation of immune infiltrating cells with the key genes. Red nodes indicate positive correlation, and blue nodes indicate negative correlation. *P < 0.05, **P < 0.01. (C) Heatmap of gene sets showing persistent up- or down-regulation that increased in the trend: healthy controls < sepsis survivors < sepsis patients who died. Gene sets were arranged based on cluster assignment to generate simplified expression profiles. We graphically depict only 4 modules with >40 genes. (D) The box plots of STEM genes in 4 clusters. Line plots and box plots were used to display, respectively, fold changes (log₂FC) or absolute expression levels based on fragments per kilobase per million reads (log₂ fragments per kilobase million). Representative genes were highlighted using red lines. The key genes were located on the right side of the box map. *P < 0.05, **P < 0.01. (E) Signaling pathways persistently up- or down-regulated as sepsis develops. Red nodes in the heatmap represent up-regulated signaling pathways, while blue nodes represent down-regulated signaling pathways.