Bai Xue, Liu RuXing, Tang Yujiao, Yang LiTing, Niu Zesu, Hu Yi, Zhang Ling, Chen MengFei
Department of Emergency, People's Hospital of Ningxia Hui Autonomous Region, Yinchuan, People's Republic of China.
Department of Emergency, The Third Clinical Medical College of Ningxia Medical University, Yinchuan, People's Republic of China.
J Inflamm Res. 2025 Apr 4;18:4709-4724. doi: 10.2147/JIR.S489077. eCollection 2025.
Sepsis is the 10th leading cause of death globally and the most common cause of death in patients with infections. Ubiquitination plays a key role in regulating immune responses during sepsis. This study combined bioinformatics and Mendelian randomization (MR) analyses to identify ubiquitin-related genes (UbRGs) with unique roles in sepsis.
Relevant genes were obtained from the GSE28750 dataset and GSE95233, weighted gene co-expression network analyses were performed to identify gene modules, and differentially expressed UBRGs (DE-UBRGs) were generated by differentially expressed genes (DEGs) crossover with key modular genes and UBRGs in sepsis and normal samples. Causal relationships between sepsis and UbRGs were analysed using MR, performance diagnostics were performed using subject work characteristics (ROC) curves, and an artificial neural network (ANN) model was developed. On this basis, immune infiltration was performed and the expression of key genes was verified in animal models.
3022 DEGs were found between sepsis and normal. A total of 2620 genes were obtained as key modular genes. Crossing DEGs, key modular genes and UBRGs yielded 93 DE-UBRGs. MR results showed WDR26 as a risk factor for sepsis (OR>1) and UBE2D1 as a protective factor for sepsis (OR<1), which was reinforced by scatterplot and forest plot. ROC curves showed that WDR26 and UBE2D1 could accurately differentiate between sepsis and normal samples. Confusion matrix and ROC curve results indicate that the artificial neural network model has strong diagnostic ability. The results of immune infiltration showed that.WDR26 was negatively correlated with plasma cells, while UBE2D1 was positively correlated with CD4 naïve T cells. Significant differences between sepsis and normal were obtained between UBE2D1 and WDR26 in the animal model.
There appeared to be a causal relationship between sepsis, WDR26 and UBE2D1. The insights were of value for effective clinical diagnosis and treatment in sepsis.
脓毒症是全球第十大死因，也是感染患者最常见的死因。泛素化在脓毒症期间调节免疫反应中起关键作用。本研究结合生物信息学和孟德尔随机化（MR）分析，以鉴定在脓毒症中具有独特作用的泛素相关基因（UbRGs）。
从GSE28750数据集和GSE95233中获取相关基因，进行加权基因共表达网络分析以鉴定基因模块，并通过脓毒症和正常样本中的差异表达基因（DEGs）与关键模块基因和UbRGs交叉生成差异表达的泛素相关基因（DE-UBRGs）。使用MR分析脓毒症与UbRGs之间的因果关系，使用受试者工作特征（ROC）曲线进行性能诊断，并开发人工神经网络（ANN）模型。在此基础上，进行免疫浸润分析，并在动物模型中验证关键基因的表达。
脓毒症与正常样本之间发现3022个DEGs。共获得2620个基因作为关键模块基因。DEGs、关键模块基因和UbRGs交叉产生93个DE-UBRGs。MR结果显示WDR26是脓毒症的危险因素（OR>1），UBE2D1是脓毒症的保护因素（OR<1），散点图和森林图进一步证实了这一点。ROC曲线显示WDR26和UBE2D1可以准确区分脓毒症和正常样本。混淆矩阵和ROC曲线结果表明人工神经网络模型具有很强的诊断能力。免疫浸润结果显示，WDR26与浆细胞呈负相关，而UBE2D1与初始CD4 T细胞呈正相关。在动物模型中，UBE2D1和WDR26在脓毒症与正常样本之间存在显著差异。
脓毒症、WDR26和UBE2D1之间似乎存在因果关系。这些见解对脓毒症的有效临床诊断和治疗具有重要价值。
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