A comprehensive time-course-based multicohort analysis of sepsis and sterile inflammation reveals a robust diagnostic gene set.
A comprehensive time-course-based multicohort analysis of sepsis and sterile inflammation reveals a robust diagnostic gene set.
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DOI：
10.1126/scitranslmed.aaa5993
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发表时间：
2015-05-13
影响因子：
17.1
通讯作者：
Khatri P
中科院分区：
文献类型：
--
作者：
Sweeney TE;Shidham A;Wong HR;Khatri P
尽管已经发表了几十项关于脓毒症基因表达的研究，但脓毒症与无菌全身炎症反应综合征(SIRS)的区别在很大程度上仍有待临床怀疑。我们假设，对公开可用的脓毒症基因表达数据集进行多队列分析将产生一组强大的基因，用于区分脓毒症患者和无菌炎症患者。对脓毒症的基因表达数据集的全面搜索发现了27个符合我们纳入标准的数据集。五个数据集(n=663个样本)比较了无菌炎症(SIRS/创伤)患者和时间匹配的感染患者。我们将我们的多队列分析框架应用于这些数据集，该框架以留下一个数据集的方式同时使用效果大小和P值。我们确定了11个差异表达的基因(假发现率≤为1%，数据集内异质性P>；0.01，汇总效应大小为1.5倍)，这些基因在所有具有出色诊断能力的发现队列中[受试者工作特征曲线下的平均面积(AUC)，0.87；范围0.70至0.98]。然后，我们在15个独立的队列中验证了这11个基因，比较了(I)时间匹配的感染和非感染创伤患者(4个队列)，(Ii)在临床过程中有感染的ICU/创伤患者(3个队列)，以及(Iii)健康受试者和脓毒症患者(8个队列)。在发现的Glue Grant队列中，SIRS加上11个基因集改善了感染预测(与单独使用SIRS相比)，连续净重新分类指数为0.90。总体而言，对时间匹配的队列进行的多队列分析得出了11个基因，它们有力地区分了无菌炎症和感染性炎症。
Although several dozen studies of gene expression in sepsis have been published, distinguishing sepsis from a sterile systemic inflammatory response syndrome (SIRS) is still largely up to clinical suspicion. We hypothesized that a multicohort analysis of the publicly available sepsis gene expression data sets would yield a robust set of genes for distinguishing patients with sepsis from patients with sterile inflammation. A comprehensive search for gene expression data sets in sepsis identified 27 data sets matching our inclusion criteria. Five data sets (n = 663 samples) compared patients with sterile inflammation (SIRS/trauma) to time-matched patients with infections. We applied our multicohort analysis framework that uses both effect sizes and P values in a leave-one-data set-out fashion to these data sets. We identified 11 genes that were differentially expressed (false discovery rate ≤1%, inter–data set heterogeneity P > 0.01, summary effect size >1.5-fold) across all discovery cohorts with excellent diagnostic power [mean area under the receiver operating characteristic curve (AUC), 0.87; range, 0.7 to 0.98]. We then validated these 11 genes in 15 independent cohorts comparing (i) time-matched infected versus noninfected trauma patients (4 cohorts), (ii) ICU/trauma patients with infections over the clinical time course (3 cohorts), and (iii) healthy subjects versus sepsis patients (8 cohorts). In the discovery Glue Grant cohort, SIRS plus the 11-gene set improved prediction of infection (compared to SIRS alone) with a continuous net reclassification index of 0.90. Overall, multicohort analysis of time-matched cohorts yielded 11 genes that robustly distinguish sterile inflammation from infectious inflammation.