Identifying miRNA-mRNA Networks Associated With COPD Phenotypes.
Identifying miRNA-mRNA Networks Associated With COPD Phenotypes.
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DOI：
10.3389/fgene.2021.748356
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发表时间：
2021
影响因子：
3.7
通讯作者：
Kechris K
中科院分区：
文献类型：
--
作者：
Zhuang Y;Hobbs BD;Hersh CP;Kechris K
慢性阻塞性肺疾病(COPD)的特征是呼气气流受限和呼吸急促等症状。虽然许多研究表明，在COPD的发病机制中，microRNA(MiRNA)和基因(MRNAs)的表达异常，但miRNAs和mRNAs如何系统地相互作用并促进COPD的发展仍不清楚。为了更深入地了解COPD发病机制背后的基因调控网络，我们使用稀疏多重规范相关网络(SmCCNet)整合了COPD基因研究中404名参与者的全血miRNA和RNA测序数据，以确定与COPD相关表型(包括肺功能和肺气肿)相关的新的miRNA-mRNA网络。我们假设来自SmCCNet的表型导向的可解释的miRNA-mRNA网络将有助于发现传统的单一生物标记发现方法(如差异表达)可能无法发现的新的生物标记。此外，我们调查了在整合前调整协变量的组学和临床表型数据是否会增加网络识别的统计能力。我们的研究表明，与没有协变量调整相比，对年龄、性别、种族和CT扫描仪模型进行部分协变量调整(在定量肺气肿网络中)可以改善网络识别。然而，对当前吸烟状况和相对白细胞比例的进一步调整有时会削弱识别肺功能和肺气肿网络的能力，这可能是由于吸烟状况和白细胞计数与COPD相关表型的相关性。通过部分协变量调整，我们发现了6个与COPD相关表型相关的miRNA-mRNA网络。一个网络由2个miRNA和28个mRNA组成，与S预测的1%用力呼气量有0.33的相关性(p=5.40E-12)。我们还发现了一个由5个miRNAs和81个mRNAs组成的网络，它们与肺气肿的百分比有0.45的相关性(p=8.80E-22)。与COPD特征相关的miRNA-mRNA网络提供了COPD发病机制的系统视图，并与单个miRNA或mRNA表达数据补充了生物标记物的识别。
Chronic obstructive pulmonary disease (COPD) is characterized by expiratory airflow limitation and symptoms such as shortness of breath. Although many studies have demonstrated dysregulated microRNA (miRNA) and gene (mRNA) expression in the pathogenesis of COPD, how miRNAs and mRNAs systematically interact and contribute to COPD development is still not clear. To gain a deeper understanding of the gene regulatory network underlying COPD pathogenesis, we used Sparse Multiple Canonical Correlation Network (SmCCNet) to integrate whole blood miRNA and RNA-sequencing data from 404 participants in the COPDGene study to identify novel miRNA–mRNA networks associated with COPD-related phenotypes including lung function and emphysema. We hypothesized that phenotype-directed interpretable miRNA–mRNA networks from SmCCNet would assist in the discovery of novel biomarkers that traditional single biomarker discovery methods (such as differential expression) might fail to discover. Additionally, we investigated whether adjusting -omics and clinical phenotypes data for covariates prior to integration would increase the statistical power for network identification. Our study demonstrated that partial covariate adjustment for age, sex, race, and CT scanner model (in the quantitative emphysema networks) improved network identification when compared with no covariate adjustment. However, further adjustment for current smoking status and relative white blood cell (WBC) proportions sometimes weakened the power for identifying lung function and emphysema networks, a phenomenon which may be due to the correlation of smoking status and WBC counts with the COPD-related phenotypes. With partial covariate adjustment, we found six miRNA–mRNA networks associated with COPD-related phenotypes. One network consists of 2 miRNAs and 28 mRNAs which had a 0.33 correlation (p = 5.40E-12) to forced expiratory volume in 1 s (FEV1) percent predicted. We also found a network of 5 miRNAs and 81 mRNAs that had a 0.45 correlation (p = 8.80E-22) to percent emphysema. The miRNA–mRNA networks associated with COPD traits provide a systems view of COPD pathogenesis and complements biomarker identification with individual miRNA or mRNA expression data.
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影响因子：
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