A novel genomic model for predicting the likelihood of delayed graft function in DCD kidney transplantation

Background: 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. Methods: 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. Results: 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. Conclusions: 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.