Consensus Transcriptional Landscape of Human End-Stage Heart Failure.
Consensus Transcriptional Landscape of Human End-Stage Heart Failure.
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DOI：
10.1161/jaha.120.019667
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发表时间：
2021-04-06
影响因子：
5.4
通讯作者：
Saez-Rodriguez J
中科院分区：
文献类型：
--
作者：
Ramirez Flores RO;Lanzer JD;Holland CH;Leuschner F;Most P;Schultz JH;Levinson RT;Saez-Rodriguez J
Transcriptomic studies have contributed to fundamental knowledge of myocardial remodeling in human heart failure (HF).然而，研究之间报告的关键心力衰竭基因往往不一致，并且缺乏整合来自多个患者队列的证据的系统性努力。在这里，我们的目标是提供一个框架，对公开可用的数据集进行全面比较和分析，从而得出人类终末期心力衰竭的无偏见共识转录特征。 We curated and uniformly processed 16 public transcriptomic studies of left ventricular samples from 263 healthy and 653 failing human hearts. First, we evaluated the degree of consistency between studies by using linear classifiers and overrepresentation analysis. Then, we meta‐analyzed the deregulation of 14 041 genes to extract a consensus signature of HF.最后，为了从功能上表征这一特征，我们评估了 343 个转录因子、14 个信号通路和 182 个 micro RNA 的活性，以及 5998 个生物过程的富集。 Machine learning approaches revealed conserved disease patterns across all studies independent of technical differences. These consistent molecular changes were prioritized with a meta‐analysis, functionally characterized and validated on external data.我们通过免费公共资源 (https://saezlab.shinyapps.io/reheat/) 提供所有结果，并通过破译胎儿基因重编程和追踪心力衰竭患者血浆蛋白质组标记物的潜在心肌起源来举例说明用法。尽管技术和采样变异性混淆了个别研究中差异表达基因的识别，但我们证明了心力衰竭末期的协调分子反应是保守的。 The presented resource is crucial to complement findings in independent studies and decipher fundamental changes in failing myocardium.
Transcriptomic studies have contributed to fundamental knowledge of myocardial remodeling in human heart failure (HF). However, the key HF genes reported are often inconsistent between studies, and systematic efforts to integrate evidence from multiple patient cohorts are lacking. Here, we aimed to provide a framework for comprehensive comparison and analysis of publicly available data sets resulting in an unbiased consensus transcriptional signature of human end‐stage HF. We curated and uniformly processed 16 public transcriptomic studies of left ventricular samples from 263 healthy and 653 failing human hearts. First, we evaluated the degree of consistency between studies by using linear classifiers and overrepresentation analysis. Then, we meta‐analyzed the deregulation of 14 041 genes to extract a consensus signature of HF. Finally, to functionally characterize this signature, we estimated the activities of 343 transcription factors, 14 signaling pathways, and 182 micro RNAs, as well as the enrichment of 5998 biological processes. Machine learning approaches revealed conserved disease patterns across all studies independent of technical differences. These consistent molecular changes were prioritized with a meta‐analysis, functionally characterized and validated on external data. We provide all results in a free public resource (https://saezlab.shinyapps.io/reheat/) and exemplified usage by deciphering fetal gene reprogramming and tracing the potential myocardial origin of the plasma proteome markers in patients with HF. Even though technical and sampling variability confound the identification of differentially expressed genes in individual studies, we demonstrated that coordinated molecular responses during end‐stage HF are conserved. The presented resource is crucial to complement findings in independent studies and decipher fundamental changes in failing myocardium.
来自多个微阵列实验的基因表达数据的荟萃分析的潜在变量方法。
影响因子：
3
作者：
Choi, Hyungwon;Shen, Ronglai;Chinnaiyan, Arul M;Ghosh, Debashis
通讯作者：
Ghosh, Debashis