Background: Non-invasive biomarkers may predict adverse events such as acute rejection after kidney transplantation and may be preferable to existing methods because of superior accuracy and convenience. It is uncertain how these biomarkers, often derived from a single study, perform across different cohorts of recipients. Methods: Using a cross-validation framework that evaluates the performance of biomarkers, the aim of this study was to devise an integrated gene signature set that predicts acute rejection in kidney transplant recipients. Inclusion criteria were publicly available datasets of gene signatures that reported acute rejection episodes after kidney transplantation. We tested the predictive probability for acute rejection using gene signatures within individual datasets and validated the set using other datasets. Eight eligible studies of 1454 participants, with a total of 512 acute rejections episodes were included. Results: All sets of gene signatures had good positive and negative predictive values (79-96%) for acute rejection within their own cohorts, but the predictability reduced to less than 50% when tested in other independent datasets. By integrating signatures sets with high specificity scores across all studies, a set of 150 genes (included CXCL6, CXCL11, OLFM4 and PSG9) which are known to be associated with immune responses, had reasonable predictive values (varied between 69-90%). Conclusions: A set of gene signatures for acute rejection derived from a specific cohort of kidney transplant recipients do not appear to provide adequate prediction in an independent cohort of transplant recipients. However, integration of gene signatures sets with high specificity scores may improve the prediction performance of these markers. # Equal contributions Funding: The authors disclosed receipt of the following financial support for this article. Australian Research Council Discovery Project Grant and Australia NHMRC Career Developmental Fellowship to JY. NHMRC Ideas Grant, Career Development Fellowships, Medical Research Future Fund and Trials and Cohort, and Investigator Grant to GW. Research Training Program Tuition Fee Offset and University of Sydney Postgraduate Award to YC. Disclosure: The authors declare no conflicts of interest. Corresponding Author: Jean Yang School of Mathematics and Statistics, Faculty of Science, Carslaw Building F07, NSW 2006, Australia jean.yang@sydney.edu.au Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.
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