ArticleScientific reports2018
Validation of systems biology derived molecular markers of renal donor organ status associated with long term allograft function.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 5 citations in OpenAlex.
- Diagnostic Biomarkers and Targeted Drug Prediction for Acute Kidney Injury: A Computational ApproachEndocrine, metabolic & immune disorders drug targets · 2026Article
- Multi-omics Approach in Kidney Transplant: Lessons Learned from COVID-19 Pandemic.Current transplantation reports · 2023Article
- Population Characteristics and Clinical Outcomes from the Renal Transplant Outcome Prediction Validation Study (TOPVAS).Journal of clinical medicine · 2022Article
- Urine recirculation prolongs normothermic kidney perfusion via more optimal metabolic homeostasis-a proteomics study.American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons · 2021Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Donor organ quality affects long term outcome after renal transplantation. A variety of prognostic molecular markers is available, yet their validity often remains undetermined. A network-based molecular model reflecting donor kidney status based on transcriptomics data and molecular features reported in scientific literature to be associated with chronic allograft nephropathy was created. Significantly enriched biological processes were identified and representative markers were selected. An independent kidney pre-implantation transcriptomics dataset of 76 organs was used to predict estimated glomerular filtration rate (eGFR) values twelve months after transplantation using available clinical data and marker expression values. The best-performing regression model solely based on the clinical parameters donor age, donor gender, and recipient gender explained 17% of variance in post-transplant eGFR values. The five molecular markers EGF, CD2BP2, RALBP1, SF3B1, and DDX19B representing key molecular processes of the constructed renal donor organ status molecular model in addition to the clinical parameters significantly improved model performance (p-value = 0.0007) explaining around 33% of the variability of eGFR values twelve months after transplantation. Collectively, molecular markers reflecting donor organ status significantly add to prediction of post-transplant renal function when added to the clinical parameters donor age and gender.
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