Transplantation · 2012
Identification of biomarkers to assess organ quality and predict posttransplantation outcomes.
СтатьяПоддержка NIH: внешняя программаНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Scian Mariano J, Maluf Daniel G, Archer Kellie J, Turner Stephen D, Suh Jihee L, David Krystle G, King Anne L, Posner Marc P, Brayman Kenneth L, Mas Valeria R
Аннотация
UNLABELLED: The increased disparity between organ supply and need has led to the use of extended criteria donors and donation after cardiac death donors with other comorbidities.
METHODS: We have examined the preimplantation transcriptome of 112 kidney transplant recipient samples from 100 deceased-donor kidneys by microarray profiling. Subject groups were segregated based on estimated glomerular filtration rate (eGFR) at 1 month after transplantation: the GFR-high group (n=74) included patients with eGFR 45 mL/min per 1.73 m(2), whereas the GFR-low group (n=35) included patients with eGFR 45 mL/min or less per 1.73 m(2).
RESULTS: Gene expression profiling identified higher expression of 160 probe sets (140 genes) in the GFR-low group, whereas expression of 37 probe sets (33 genes) was higher in the GFR-high group (P<0.01, false discovery rate <0.2). Four genes (CCL5, CXCR4, ITGB2, and EGF) were selected based on fold change and P value and further validated using an independent set of samples. A random forest analysis identified three of these genes (CCL5, CXCR4, and ITGB2) as important predictors of graft function after transplantation.
CONCLUSIONS: Inclusion of pretransplantation molecular gene expression profiles in donor quality assessment systems may provide the necessary information for better donor organ selection and function prediction. These biomarkers would further allow a more objective and complete assessment of procured renal allografts at pretransplantation time.
METHODS: We have examined the preimplantation transcriptome of 112 kidney transplant recipient samples from 100 deceased-donor kidneys by microarray profiling. Subject groups were segregated based on estimated glomerular filtration rate (eGFR) at 1 month after transplantation: the GFR-high group (n=74) included patients with eGFR 45 mL/min per 1.73 m(2), whereas the GFR-low group (n=35) included patients with eGFR 45 mL/min or less per 1.73 m(2).
RESULTS: Gene expression profiling identified higher expression of 160 probe sets (140 genes) in the GFR-low group, whereas expression of 37 probe sets (33 genes) was higher in the GFR-high group (P<0.01, false discovery rate <0.2). Four genes (CCL5, CXCR4, ITGB2, and EGF) were selected based on fold change and P value and further validated using an independent set of samples. A random forest analysis identified three of these genes (CCL5, CXCR4, and ITGB2) as important predictors of graft function after transplantation.
CONCLUSIONS: Inclusion of pretransplantation molecular gene expression profiles in donor quality assessment systems may provide the necessary information for better donor organ selection and function prediction. These biomarkers would further allow a more objective and complete assessment of procured renal allografts at pretransplantation time.