ArticleJournal of the American Society of Nephrology : JASN2014
Urinary cell mRNA profiles and differential diagnosis of acute kidney graft dysfunction.
Article in Journal of the American Society of Nephrology : JASN, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02776111 (The Use of Microfluidics and Transcriptomics to Implement Precision Medicine in Post Solid Organ Transplant Patients), which is not on this map. Cited by 27 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
The Use of Microfluidics and Transcriptomics to Implement Precision Medicine in Post Solid Organ Transplant Patients
Who cites it
27 citing papers in PubMed, 47 citations in OpenAlex.
- Exfoliated kidney cells from urine for non-invasive kidney transplant monitoring: A potential opportunity?Clinical and experimental nephrology · 2026Review
- Development of a Flexible Multiplex Urine RNA Assay for Detection and Differentiation of Kidney Allograft Injury.medRxiv : the preprint server for health sciences · 2025Article
- Genomic and Biomarker Innovations in Predicting Kidney Transplant Rejection.Journal of clinical medicine · 2025Review
- Genetic imputation of kidney transcriptome, proteome and multi-omics illuminates new blood pressure and hypertension targets.Nature communications · 2024Article
- Rejection markers in kidney transplantation: do new technologies help children?Pediatric nephrology (Berlin, Germany) · 2023Review
- Extracellular Vesicles: The Future of Diagnosis in Solid Organ Transplantation?International journal of molecular sciences · 2023Review
- Longitudinal monitoring of mRNA levels of regulatory T cell biomarkers by using non-invasive strategies to predict outcome in renal transplantation.BMC nephrology · 2022Article
- Progress in kidney transplantation: The role for systems immunology.Frontiers in medicine · 2022Review
- Non-Invasive Diagnosis for Acute Rejection Using Urinary mRNA Signature Reflecting Allograft Status in Kidney Transplantation.Frontiers in immunology · 2021Article
- Discovery and Validation of Urinary Molecular Signature of Early Sepsis.Critical care explorations · 2020Article
- Urinary Cell Transcriptome Profiling and Identification of ITM2A, SLAMF6, and IKZF3 as Biomarkers of Acute Rejection in Human Kidney Allografts.Transplantation direct · 2020Article
- Article
- Development and validation of a peripheral blood mRNA assay for the assessment of antibody-mediated kidney allograft rejection: A multicentre, prospective study.EBioMedicine · 2019Article
- Assessing the Clinical Impact of Risk Models for Opting Out of Treatment.Medical decision making : an international journal of the Society for Medical Decision Making · 2019Article
- Assessment of 19 Genes and Validation of CRM Gene Panel for Quantitative Transcriptional Analysis of Molecular Rejection and Inflammation in Archival Kidney Transplant Biopsies.Frontiers in medicine · 2019Article
- Extracellular vesicles in renal disease.Nature reviews. Nephrology · 2017Review
- Urinary MicroRNA as Biomarker in Renal Transplantation.American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons · 2017Review
- Moving Biomarkers toward Clinical Implementation in Kidney Transplantation.Journal of the American Society of Nephrology : JASN · 2017Review
- Non-invasive approaches in the diagnosis of acute rejection in kidney transplant recipients, part II: omics analyses of urine and blood samples.Clinical kidney journal · 2017Review
- Potential urine proteomics biomarkers for primary nephrotic syndrome.Clinical proteomics · 2017Article
Corrections and comments
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Authors and funding
16 authors at 4 institutions in 3 countries.
Funding
Abstract
Noninvasive tests to differentiate the basis for acute dysfunction of the kidney allograft are preferable to invasive allograft biopsies. We measured absolute levels of 26 prespecified mRNAs in urine samples collected from kidney graft recipients at the time of for-cause biopsy for acute allograft dysfunction and investigated whether differential diagnosis of acute graft dysfunction is feasible using urinary cell mRNA profiles. We profiled 52 urine samples from 52 patients with biopsy specimens indicating acute rejection (26 acute T cell-mediated rejection and 26 acute antibody-mediated rejection) and 32 urine samples from 32 patients with acute tubular injury without acute rejection. A stepwise quadratic discriminant analysis of mRNA measures identified a linear combination of mRNAs for CD3ε, CD105, TLR4, CD14, complement factor B, and vimentin that distinguishes acute rejection from acute tubular injury; 10-fold cross-validation of the six-gene signature yielded an estimate of the area under the curve of 0.92 (95% confidence interval, 0.86 to 0.98). In a decision analysis, the six-gene signature yielded the highest net benefit across a range of reasonable threshold probabilities for biopsy. Next, among patients diagnosed with acute rejection, a similar statistical approach identified a linear combination of mRNAs for CD3ε, CD105, CD14, CD46, and 18S rRNA that distinguishes T cell-mediated rejection from antibody-mediated rejection, with a cross-validated estimate of the area under the curve of 0.81 (95% confidence interval, 0.68 to 0.93). Incorporation of these urinary cell mRNA signatures in clinical decisions may reduce the number of biopsies in patients with acute dysfunction of the kidney allograft.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.