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

ABSTRACT:

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. Gen

SUBMITTER: Yu B 

PROVIDER: S-EPMC8100846 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature