Pan Xingyu, Luo Jin, Zhu Rong, Peng Jinpu, Jin Yuhan, Zhang Li, Pei Jun
Department of Nursing, Affiliated Hospital of Zunyi Medical University, Zunyi 563100, China; Nursing School of Zunyi Medical University, Zunyi 563100, China.
Department of Pediatric Surgery, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361005, China.
Transpl Immunol. 2025 May;90:102224. doi: 10.1016/j.trim.2025.102224. Epub 2025 Mar 25.
Ischemia-reperfusion injury (IRI) in kidney transplantation can delay graft function recovery and increase the risk of rejection. Mast cell activation releases various bioactive mediators that exacerbate renal IRI. Assessing mast cell activation may be crucial for managing IRI after kidney transplantation.
We analyzed the dataset GSE43974 from the Gene Expression Omnibus (GEO) to evaluate immune cell infiltration during the IRI phase of kidney transplantation using the CIBERSORT algorithm. Weighted gene co-expression network analysis (WGCNA) was performed to identify genes most strongly correlated with mast cell activation. Hub genes were identified using protein-protein interaction (PPI) network analysis and machine learning algorithms. Model accuracy for identifying hub genes was assessed using receiver operating characteristic (ROC) curve calibration. Clinical utility was evaluated through decision curve analysis (DCA). Correlation analysis was conducted to explore associations between the selected hub genes and immune cell infiltration. Additionally, a hub gene-miRNA regulatory network was constructed.
Mast cell activation exhibited the most significant variation among graft-infiltrating immune cells during IRI. WGCNA identified 115 genes closely associated with mast cell activation, from which three hub genes-JUN, MYC, and ALDH2-were selected using a PPI network and machine learning approach. A diagnostic model based on these three genes demonstrated high accuracy, as validated by the Hosmer-Lemeshow test (P = 0.980) and an area under the ROC curve (AUC) of 1. DCA indicated that these hub genes had strong clinical decision-making relevance, while correlation analysis confirmed their associations with multiple immune cell types. Finally, a hub gene-miRNA network provided a theoretical framework for the regulatory mechanisms of the three genes.
JUN, MYC, and ALDH2 may serve as biomarkers of mast cell activation during IRI in kidney transplantation. Further studies are warranted to explore their potential in mitigating IRI.
肾移植中的缺血再灌注损伤（IRI）会延迟移植肾功能恢复并增加排斥反应风险。肥大细胞激活会释放多种生物活性介质，加剧肾脏IRI。评估肥大细胞激活对于肾移植后IRI的管理可能至关重要。
我们分析了来自基因表达综合数据库（GEO）的数据集GSE43974，使用CIBERSORT算法评估肾移植IRI阶段的免疫细胞浸润情况。进行加权基因共表达网络分析（WGCNA）以识别与肥大细胞激活最密切相关的基因。使用蛋白质-蛋白质相互作用（PPI）网络分析和机器学习算法确定枢纽基因。使用受试者工作特征（ROC）曲线校准评估识别枢纽基因的模型准确性。通过决策曲线分析（DCA）评估临床实用性。进行相关性分析以探索所选枢纽基因与免疫细胞浸润之间的关联。此外，构建了枢纽基因- miRNA调控网络。
在IRI期间，肥大细胞激活在移植浸润免疫细胞中表现出最显著的变化。WGCNA识别出115个与肥大细胞激活密切相关的基因，使用PPI网络和机器学习方法从中选择了三个枢纽基因——JUN、MYC和ALDH2。基于这三个基因的诊断模型显示出高准确性，经Hosmer-Lemeshow检验（P = 0.980）和ROC曲线下面积（AUC）为1验证。DCA表明这些枢纽基因具有很强的临床决策相关性，而相关性分析证实了它们与多种免疫细胞类型的关联。最后，枢纽基因-miRNA网络为这三个基因的调控机制提供了理论框架。
JUN、MYC和ALDH2可能作为肾移植IRI期间肥大细胞激活的生物标志物。有必要进一步研究以探索它们在减轻IRI方面的潜力。
