Revealing the novel pyroptosis-related therapeutic targets for sepsis based on the machine learning

Abstract

Abstract Background Sepsis is one of the most lethal diseases worldwide. Pyroptosis as a unique form of cell death and the mechanism of interaction with sepsis is not yet clear. The aim of this study is to uncover pyroptosis genes associated with sepsis and to provide early therapeutic targets for the treatments of sepsis. Methods Based on the GSE134347 dataset, sepsis-related genes were mined by differential expression analysis and WGCNA. Subsequently, the sepsis-related genes were analyzed for enrichment and a protein-protein interaction (PPI) network was constructed. We performed unsupervised consensus clustering of sepsis patients based on 33 pyroptosis related genes (PRGs) provided by prior reviews. We finally obtained the PRGs mostly associated with sepsis by machine learning prediction models combined with the GeneCards database and prior reviews. The GSE32707 dataset served as an external validation dataset to validate the model and PRGs via receiver operating characteristic (ROC) curves. NetworkAnalyst online tool was utilized to create a ceRNA network of lncRNAs and miRNAs around PRGs mostly associated with sepsis. Results A total of 170 genes associated with sepsis and 13 hub genes were acquired by WGCNA and PPI network. The results of the enrichment analysis implied that these genes were mainly involved in the regulation of the inflammatory response and the positive regulation of bacterial and fungal defense responses. Prolactin signaling pathway and IL-17 signaling pathway were the primary enrichment pathways. Thirty-three PRGs can effectively classify septic patients into two subtypes, implying that there is a reciprocal relationship between sepsis and pyroptosis. Eventually, NLRC4 was considered as the PRG most strongly associated with sepsis. The validation results of the prediction model and NLRC4 based on ROC curves were 0.74 and 0.67, respectively, both of which showed better predictive values. Meanwhile, the ceRNA network consisting of 6 lncRNAs and 2 miRNAs was constructed around NLRC4. Conclusion NLRC4 as the PRG mostly associated with sepsis could be considered as a potential target for treatment. The 6 lncRNAs and 2 miRNAs centered on NLRC4 could serve as a further research direction to uncover the deeper pathogenesis of sepsis.