Abstract
BACKGROUND: Pediatric sepsis is a complex and heterogeneous condition resulting from a dysregulated immune response to infection. Pyroptosis, a newly recognized form of programmed cell death, has been implicated in the progression of various inflammatory diseases. However, the role of pyroptosis-related genes in pediatric sepsis remains unclear. METHODS: Based on the GSE13904 dataset, we explored the pyroptosis-related differentially expressed genes (DEGs) in pediatric sepsis. We analyzed the molecular clusters based on pyroptosis-related DEGs. The WGCNA algorithm was performed to identify cluster-specific DEGs. The optimal machine model was identified by multiple machine learning methods (RF, SVM, GLM, XGB). The diagnostic value of hub genes in pediatric sepsis was verified in the training (GSE13904) and validation set (GSE26440) through ROC. qRT-PCR was used to verify the expression levels of 5 hub genes in whole blood between the pediatric sepsis and the control. RESULTS: The dysregulated pyroptosis-related DEGs were identified in pediatric sepsis. Three pyroptosis-related molecular clusters were determined in pediatric sepsis. SVM presented the best discriminative performance with relatively lower residual and root mean square error. The nomogram, calibration curve, and decision curve analysis indicated the accuracy of SVM model to predict pediatric sepsis. 5 hub genes based on SVM presented satisfactory performance in the training and validation sets. These hub genes expression levels in pediatric sepsis were significantly higher than those in healthy controls in clinical samples. CONCLUSION: Our study systematically analyzed the relationship between pyroptosis and pediatric sepsis, and constructed a promising predictive model to evaluate the risk of pediatric sepsis.