Biopsy transcriptome expression profiling to identify kidney transplants at risk of chronic injury: a multicentre, prospective study.
Biopsy transcriptome expression profiling to identify kidney transplants at risk of chronic injury: a multicentre, prospective study.
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DOI：
10.1016/s0140-6736(16)30826-1
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发表时间：
2016-09-03
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影响因子：
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通讯作者：
Murphy B
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作者：
O'Connell PJ;Zhang W;Menon MC;Yi Z;Schröppel B;Gallon L;Luan Y;Rosales IA;Ge Y;Losic B;Xi C;Woytovich C;Keung KL;Wei C;Greene I;Overbey J;Bagiella E;Najafian N;Samaniego M;Djamali A;Alexander SI;Nankivell BJ;Chapman JR;Smith RN;Colvin R;Murphy B
肾移植中的慢性损伤仍然是同种异体移植物丢失的主要原因。这项研究的目的是确定一个能够预测由于纤维化导致进展性损伤风险的移植肾的基因集。慢性移植排斥反应基因组学(GOCAR)研究是一项前瞻性的多中心研究。我们前瞻性地收集了移植后3个月肾功能稳定的同种异体肾移植受者(n=204)的活检标本。我们使用微阵列分析来研究其中159个组织样本中的基因表达。我们的目标是确定在12个月时与慢性移植物损伤指数(CADI)评分相关的基因，但在活检时与纤维化无关。我们应用惩罚性回归模型和基于排列的方法来得出预测同种异体移植肝纤维化的最佳基因集。Gocar研究在ClinicalTrials.gov注册，编号NCT00611702。我们确定了一组13个独立预测1年后纤维化发展的基因(即，CADI-12≥2)。该基因集具有较高的预测能力(曲线下面积[AUC]0·967)，优于基线临床变量(AUC 0·706)和临床病理变量(AUC 0·806)。此外，常规病理变量不能确定哪些组织学正常的同种异体移植物将进展为纤维化(AUC 0.754)，而预测基因集准确地区分进展风险高和低的移植物(AUC 0.916)。这13个基因也能准确预测移植物早期丢失(2年时AUC为0.842，3年时为0.844)。我们在Gocar研究的一个独立队列(n=45，AUC 0·866)和两个独立的公共可用表达数据集(n=282，AUC 0·831和n=24，AUC 0·972)中验证了该基因集的预测价值。我们的结果表明，这组13个基因可以用来在发生不可逆转的损害之前识别肾移植受者有同种异体移植物丢失的风险，从而允许修改治疗以防止进展为纤维化。美国国立卫生研究院。
Chronic injury in kidney transplants remains a major cause of allograft loss. The aim of this study was to identify a gene set capable of predicting renal allografts at risk of progressive injury due to fibrosis. This Genomics of Chronic Allograft Rejection (GoCAR) study is a prospective, multicentre study. We prospectively collected biopsies from renal allograft recipients (n=204) with stable renal function 3 months after transplantation. We used microarray analysis to investigate gene expression in 159 of these tissue samples. We aimed to identify genes that correlated with the Chronic Allograft Damage Index (CADI) score at 12 months, but not fibrosis at the time of the biopsy. We applied a penalised regression model in combination with permutation-based approach to derive an optimal gene set to predict allograft fibrosis. The GoCAR study is registered with ClinicalTrials.gov, number NCT00611702. We identified a set of 13 genes that was independently predictive for the development of fibrosis at 1 year (ie, CADI-12 ≥2). The gene set had high predictive capacity (area under the curve [AUC] 0·967), which was superior to that of baseline clinical variables (AUC 0·706) and clinical and pathological variables (AUC 0·806). Furthermore routine pathological variables were unable to identify which histologically normal allografts would progress to fibrosis (AUC 0·754), whereas the predictive gene set accurately discriminated between transplants at high and low risk of progression (AUC 0·916). The 13 genes also accurately predicted early allograft loss (AUC 0·842 at 2 years and 0·844 at 3 years). We validated the predictive value of this gene set in an independent cohort from the GoCAR study (n=45, AUC 0·866) and two independent, publically available expression datasets (n=282, AUC 0·831 and n=24, AUC 0·972). Our results suggest that this set of 13 genes could be used to identify kidney transplant recipients at risk of allograft loss before the development of irreversible damage, thus allowing therapy to be modified to prevent progression to fibrosis. National Institutes of Health.