期刊
TRANSPLANTATION
卷 106, 期 4, 页码 806-820出版社
LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1097/TP.0000000000003815
关键词
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资金
- National Institutes of Health [UO1AI63589, R37AI051652, NIH UH2/UH3 TR000933]
- NIDDK [K08-DK087824, R03-DK105270]
- NIH
- National Center for Advancing Translational Sciences, National Institutes of Health [UL1 TR 000002]
This study explored noninvasive diagnostic methods for intragraft pathologies and identified miRNAs associated with nonimmune, nonviral native liver diseases. Absolute levels of miRNAs, quantified using customized RT-qPCR assays, were found to be capable of rapidly and noninvasively diagnosing acute rejection in liver allografts and distinguishing it from intragraft inflammation or fibrosis caused by recurrent hepatitis C virus.
Background. Acute rejection (AR) and recurrent hepatitis C virus (R-HCV) are significant complications in liver allograft recipients. Noninvasive diagnosis of intragraft pathologies may improve their management. Methods. We performed small RNA sequencing and microRNA (miRNA) microarray profiling of RNA from sera matched to liver allograft biopsies from patients with nonimmune, nonviral (NINV) native liver disease. Absolute levels of informative miRNAs in 91 sera matched to 91 liver allograft biopsies were quantified using customized real-time quantitative PCR (RT-qPCR) assays: 30 biopsy-matched sera from 26 unique NINV patients and 61 biopsy-matched sera from 41 unique R-HCV patients. The association between biopsy diagnosis and miRNA abundance was analyzed by logistic regression and calculating the area under the receiver operating characteristic curve. Results. Nine miRNAs-miR-22, miR-34a, miR-122, miR-148a, miR-192, miR-193b, miR-194, miR-210, and miR-885-5p-were identified by both sRNA-seq and TLDA to be associated with NINV-AR. Logistic regression analysis of absolute levels of miRNAs and goodness-of-fit of predictors identified a linear combination of miR-34a + miR-210 (P < 0.0001) as the best statistical model and miR-122 + miR-210 (P < 0.0001) as the best model that included miR-122. A different linear combination of miR-34a + miR-210 (P < 0.0001) was the best model for discriminating NINV-AR from R-HCV with intragraft inflammation, and miR-34a + miR-122 (P < 0.0001) was the best model for discriminating NINV-AR from R-HCV with intragraft fibrosis. Conclusions. Circulating levels of miRNAs, quantified using customized RT-qPCR assays, may offer a rapid and noninvasive means of diagnosing AR in human liver allografts and for discriminating AR from intragraft inflammation or fibrosis due to R-HCV.
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