Zhang Liuzhao, Chu Quanwang, Jiang Shuyue, Shao Bo
Department of Critical Care Medicine, Anhui Jing'an Medicine Hospital, Hefei, 230032, China.
Department of Pathology, Anhui Provincial Children's Hospital, 39 Wangjiang East Road, Hefei, Anhui, 230051, China.
BMC Pediatr. 2025 Jan 27;25(1):66. doi: 10.1186/s12887-025-05424-y.
This study aims to explore the genetic characteristics of pediatric sepsis through a combined analysis of multiple methods, including Mendelian Randomization (MR), differential gene expression analysis, and immune cell infiltration assessment. It explores their potential as biomarkers for sepsis risk and their involvement in immune-related pathways.
Differential expression analysis was performed using public datasets to identify genes with significant expression changes between pediatric sepsis patients and healthy controls. MR analysis utilized genome-wide significant SNPs as instrumental variables to assess causal relationships between gene expression and sepsis risk. Bi-directional MR was conducted to assess both forward and reverse causality. FDR correction was applied to adjust for multiple comparisons in MR results. Immune cell infiltration analysis was performed to investigate the genes' roles in immune responses, and findings were validated with independent datasets. ROC curves were constructed to assess predictive performance.
Differential expression analysis identified significant changes in RGL4,ATP9A,MAP3K7CL, and DDX11L2. MR analysis revealed causal associations between these genes and sepsis risk, with RGL4 and ATP9A upregulated (inflammatory roles), and MAP3K7CL and DDX11L2 downregulated (protective roles). Bi-directional MR found no significant reverse causality. Immune cell analysis showed associations with key immune cell types, and ROC analysis demonstrated strong predictive potential.
RGL4,ATP9A,MAP3K7CL, and DDX11L2 play important roles in pediatric sepsis risk and immune response regulation, offering insights into genetic and immune mechanisms that may inform future sepsis research and treatment.
本研究旨在通过孟德尔随机化（MR）、差异基因表达分析和免疫细胞浸润评估等多种方法的联合分析，探索儿童脓毒症的遗传特征。研究这些基因作为脓毒症风险生物标志物的潜力及其在免疫相关途径中的作用。
使用公共数据集进行差异表达分析，以鉴定儿童脓毒症患者与健康对照之间有显著表达变化的基因。MR分析利用全基因组显著的单核苷酸多态性（SNPs）作为工具变量，评估基因表达与脓毒症风险之间的因果关系。进行双向MR以评估正向和反向因果关系。应用错误发现率（FDR）校正来调整MR结果中的多重比较。进行免疫细胞浸润分析以研究这些基因在免疫反应中的作用，并用独立数据集验证结果。构建ROC曲线以评估预测性能。
差异表达分析确定了RGL4、ATP9A、MAP3K7CL和DDX11L2的显著变化。MR分析揭示了这些基因与脓毒症风险之间的因果关联，其中RGL4和ATP9A上调（具有炎症作用），而MAP3K7CL和DDX11L2下调（具有保护作用）。双向MR未发现显著的反向因果关系。免疫细胞分析显示与关键免疫细胞类型有关联，ROC分析显示出强大的预测潜力。
RGL4、ATP9A、MAP3K7CL和DDX11L2在儿童脓毒症风险和免疫反应调节中起重要作用，为可能为未来脓毒症研究和治疗提供信息的遗传和免疫机制提供了见解。
