ArticleScientific reports2026
Predicting kidney graft survival with a machine learning model based on for-cause biopsy transcriptomics.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Artificial Intelligence in Kidney Transplant Pathology: Current Evidence, Limitations, and Relevance to the Banff Framework.Kidney international reports · 2026Review
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
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 associated with graft loss were identified and used to train ML models. The Gradient Boosting Machine (GBM) achieved the highest C-index (> 0.85) and accurately stratified patients by graft survival. External validation in diagnostic tasks across four independent cohorts confirmed the model's value of overall rejection (AUCs 0.760-0.826). Patients classified as High-risk showed reduced graft survival, enrichment of pro-inflammatory immune pathways and marked differences in immune cell composition. We developed and validated a transcriptome-based GBM model that accurately predicts graft survival using for-cause biopsy samples. The model demonstrated strong prognostic and diagnostic performance across multiple independent cohorts and reflects key immunological mechanisms of rejection. Its compact gene set offers practicality, supporting its potential integration into routine transplant care.
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