Predicting kidney graft survival with a machine learning model based on for-cause biopsy transcriptomics.

ABSTRACT: Kidney transplantation significantly improves outcomes in end-stage renal disease, however, long-term graft survival remains suboptimal. Traditional prognostic models often yield limited predictive power. Given the routine use of for-cause biopsies in clinical practice, we aimed to develop and compare ML models to predict graft survival using gene expression profiles from indication biopsies. Data from for-cause renal biopsies were collected from six cohorts in the Gene Expression Omnibus: GSE21374 (n = 282), GSE48581 (n = 306), GSE36059 (n = 411), GSE50058 (n = 101), GSE72925 (n = 168) and GSE25902 (n = 24). Differential expression and Cox regression analysis were applied to identify prognostic genes. These were used to train and validate 117 machine learning models. A total of 11 genes a

SUBMITTER: Filho VOC 

PROVIDER: S-EPMC12905312 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature