Department of Critical Care Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China.
Department of Respiratory Medicine, The Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China.
BMC Med Genomics. 2023 Feb 10;16(1):23. doi: 10.1186/s12920-023-01453-7.
Sepsis is one of the most lethal diseases worldwide. Pyroptosis is a unique form of cell death, and the mechanism of interaction with sepsis is not yet clear. The aim of this study was to uncover pyroptosis genes associated with sepsis and to provide early therapeutic targets for the treatment of sepsis.
Based on the GSE134347 dataset, sepsis-related genes were mined by differential expression analysis and weighted gene coexpression network analysis (WGCNA). Subsequently, the sepsis-related genes were analysed 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 prior reviews. The GSE32707 dataset served as an external validation dataset to validate the model and PRGs via receiver operating characteristic (ROC) curves. The NetworkAnalyst online tool was utilized to create a ceRNA network of lncRNAs and miRNAs around PRGs mostly associated with sepsis.
A total of 170 genes associated with sepsis and 13 hub genes were acquired by WGCNA and PPI network analysis. 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 defence responses. The prolactin signalling pathway and IL-17 signalling 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 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.
NLRC4, as the PRG mostly associated with sepsis, could be considered a potential target for treatment. The 6 lncRNAs and 2 miRNAs centred on NLRC4 could serve as a further research direction to uncover the deeper pathogenesis of sepsis.
脓毒症是全球最致命的疾病之一。细胞焦亡是一种独特的细胞死亡形式，其与脓毒症的相互作用机制尚不清楚。本研究旨在发现与脓毒症相关的细胞焦亡基因，为脓毒症的治疗提供早期治疗靶点。
基于 GSE134347 数据集，通过差异表达分析和加权基因共表达网络分析（WGCNA）挖掘与脓毒症相关的基因。随后，对与脓毒症相关的基因进行富集分析，并构建蛋白质-蛋白质相互作用（PPI）网络。基于先前综述提供的 33 个细胞焦亡相关基因（PRGs）对脓毒症患者进行无监督共识聚类。最后，通过机器学习预测模型结合先前综述获得与脓毒症最相关的 PRGs。GSE32707 数据集被用作外部验证数据集，通过接收者操作特征（ROC）曲线验证模型和 PRGs。利用在线工具 NetworkAnalyst 构建与脓毒症最相关的 PRGs 周围的 lncRNA 和 miRNA 的 ceRNA 网络。
通过 WGCNA 和 PPI 网络分析获得了与脓毒症相关的 170 个基因和 13 个枢纽基因。富集分析结果表明，这些基因主要参与炎症反应的调节和细菌及真菌防御反应的正调控。主要富集途径有催乳素信号通路和 IL-17 信号通路。33 个 PRGs 可以有效地将脓毒症患者分为两种亚型，这表明脓毒症与细胞焦亡之间存在相互关系。最终，NLRC4 被认为是与脓毒症最相关的 PRG。基于 ROC 曲线的预测模型和 NLRC4 的验证结果分别为 0.74 和 0.67，均显示出更好的预测值。同时，构建了以 NLRC4 为中心的包含 6 个 lncRNA 和 2 个 miRNA 的 ceRNA 网络。
作为与脓毒症最相关的 PRG，NLRC4 可以被认为是一种潜在的治疗靶点。以 NLRC4 为中心的 6 个 lncRNA 和 2 个 miRNA 可以作为进一步研究的方向，以揭示脓毒症更深层次的发病机制。
