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.