Abstract
The goal of this study was to identify potential transcriptomic markers in pediatric septic shock prognosis by an integrative analysis of multiple public microarray datasets. Using the R software and bioconductor packages, we performed a statistical analysis to identify differentially expressed (DE) genes in pediatric septic shock non-survivors, and further performed functional interpretation (enrichment analysis and co-expression network construction) and classification quality evaluation of the DE genes identified. Four microarray datasets (3 training datasets and 1 testing dataset, 252 pediatric patients with septic shock in total) were collected for the integrative analysis. A total of 32 DE genes (18 upregulated genes; 14 downregulated genes) were identified in pediatric septic shock non-survivors. Enrichment analysis revealed that those DE genes were strongly associated with acute inflammatory response to antigenic stimulus, response to yeast, and defense response to bacterium. A support vector machine classifier (non-survivors vs survivors) was also trained based on DE genes. In conclusion, the DE genes identified in this study are suggested as candidate transcriptomic markers for pediatric septic shock prognosis and provide novel insights into the progression of pediatric septic shock.
Highlights
Sepsis is the world’s leading cause of death of children, and it represents a complex disease with dysregulated inflammatory responses and a high mortality rate [1]
membrane metalloendopeptidase (MME) is widely present in organs and tissues, contributing to the inactivation of many biological peptides [24,25]
Decreased MME expression capacity was detected in the neutrophils from patients with septic shock [25]
Summary
Sepsis is the world’s leading cause of death of children, and it represents a complex disease with dysregulated inflammatory responses and a high mortality rate [1]. An immune response initiated through an invading pathogen cannot be controlled to restore homeostasis in sepsis, which generates a pathological syndrome with sustained excessive inflammation as well as immune suppression [2]. High-throughput transcriptomic data grow rapidly, enabling gene expression profiling and identification of prognostic targets in disease. Several studies have explored the prognosis of pediatric septic shock by transcriptional profiling using microarrays [5,6,7,8]. In this study, the gene expression patterns of pediatric septic shock patients (of survivors and non-survivors) were investigated using public microarray datasets. The differently expressed (DE) genes identified were further interpreted by enrichment analysis, construction of co-expression networks, and receiver operating characteristic (ROC) curve analysis
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