Yu Bin, Liang Han, Zhou Shujun, Ye Qifa, Wang Yanfeng
Zhongnan Hospital of Wuhan University, Institute of Hepatobiliary Diseases of Wuhan University, Transplant Center of Wuhan University, Hubei Key Laboratory of Medical Technology on Transplantation, Wuhan, China.
The 3rd Xiangya Hospital of Central South University, Research Center of National Health Ministry on Transplantation Medicine Engineering and Technology, Changsha, China.
Transl Androl Urol. 2021 Apr;10(4):1637-1646. doi: 10.21037/tau-20-1533.
The high incidence of delayed graft function (DGF) following kidney transplantation with donation after cardiac death allografts (DCD-KT) poses great challenges to transplant clinicians. This study aimed to explore the DGF-related biomarkers and establish a genomic model for DGF prediction specific to DCD KT.
By data mining a public dataset (GSE43974), the key DGF-related genes in DCD kidney biopsies taken after short-time reperfusion (45-60 min) were identified by differential expression analysis and a LASSO-penalized logistic regression model. Their coefficients for modeling were calculated by multivariate logistic regression. Receiver operating characteristic curves and a nomogram were generated to evaluate its predictive ability for DGF occurrence. Gene set enrichment analysis (GSEA) was performed to explore biological pathways underlying DGF in DCD KT.
Five key DGF-related genes (CHST3, GOLPH3, ZBED5, AKR1C4, and ERRFI1) were first identified, all of which displayed good discrimination for DGF occurrence after DCD KT (all P<0.05). A five-mRNA-based risk score was further established and showed excellent predictive ability (AUC =0.9708, P<0.0001), which was obviously higher than that of the five genes alone. Eight DGF-related biological pathways in DCD kidneys, such as "arachidonic acid metabolism", "lysosome", "proximal tubule bicarbonate reclamation", "glutathione metabolism", were identified by GSEA (all P<0.05). Moreover, a convenient and visual nomogram based on the genomic risk score was also constructed and displayed high accuracy for DGF prediction specific to DCD KT.
The novel genomic model may effectively predict the likelihood of DGF immediately after DCD KT or even prior to transplantation in the context of normothermic machine perfusion in the future.
心脏死亡后器官捐献肾移植（DCD-KT）后移植肾功能延迟（DGF）的高发生率给移植临床医生带来了巨大挑战。本研究旨在探索与DGF相关的生物标志物，并建立一个针对DCD-KT的DGF预测基因组模型。
通过挖掘公共数据集（GSE43974），采用差异表达分析和LASSO惩罚逻辑回归模型，确定了短时间再灌注（45-60分钟）后DCD肾活检中与DGF相关的关键基因。通过多变量逻辑回归计算其建模系数。生成受试者工作特征曲线和列线图，以评估其对DGF发生的预测能力。进行基因集富集分析（GSEA）以探索DCD-KT中DGF的潜在生物学途径。
首次鉴定出五个与DGF相关的关键基因（CHST3、GOLPH3、ZBED5、AKR1C4和ERRFI1），所有这些基因对DCD-KT后DGF的发生均具有良好的区分能力（所有P<0.05）。进一步建立了基于五个mRNA的风险评分，显示出优异的预测能力（AUC =0.9708，P<0.0001），明显高于单独的五个基因。通过GSEA鉴定出DCD肾中八个与DGF相关的生物学途径，如“花生四烯酸代谢”、“溶酶体”、“近端小管碳酸氢盐回收”、“谷胱甘肽代谢”（所有P<0.05）。此外，还构建了基于基因组风险评分的便捷直观列线图，对DCD-KT的DGF预测显示出高准确性。
新的基因组模型可能有效地预测DCD-KT后立即甚至在未来常温机器灌注情况下移植前DGF的发生可能性。
