Identification of potential biomarkers and immune infiltration in pediatric sepsis via multiple-microarray analysis

Abstract

Immune adjustment has become a sepsis occurring in the development of an important mechanism that cannot be ignored. This article from the perspective of immune infiltration of pediatric sepsis screening markers, and promote the understanding of disease mechanisms. Bioinformatics integrated six data sets of pediatric sepsis by using the surrogate variable analysis package and then analyzed differentially expressed genes (DEGs), immune infiltration and weighted gene co-expression network analysis of characteristics (WGCNA) of immune infiltration between pediatric sepsis and the control. Common genes of WGCNA and DEGs were used to functional annotation, pathway enrichment analysis and protein-protein interaction network. Support vector machine (SVM), least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression were used to confirm the key genes for the diagnosis of pediatric sepsis. Receiver operating characteristic (ROC) curve, C index, principal component analysis (PCA) and GiViTi calibration band were used to evaluate the diagnostic performance of key genes. Decision curve analysis (DCA) was used to evaluate the clinical application value of key genes. Lastly, the correlation between key genes and immune cells was analyze. NK cells Resting and NK cell activated in pediatric sepsis during immune infiltration were significantly lower than those in the control group, while M1 Macrophages were higher than those in the control group. ROC, C-index, PCA, GiViTi calibration band and DCA indicated that MCEMP1, CD177, MMP8 and OLFM4 had high diagnostic performance for pediatric sepsis. There is a negative correlation between 4 key genes and NK cells resting, NK cells activated. Except for MCEMP1, the other 3 genes were positively correlated with M1 Macrophages. This study revealed differences in immune responses in pediatric sepsis and identified four key genes as potential biomarkers. Pediatric sepsis in pathology maybe understood better by learning about how it develops.