Chronic obstructive pulmonary disease (COPD) is a disease in which airflow obstruction in the lung makes it difficult for patients to breathe. Although COPD occurs predominantly in smokers, there are still deficits in our understanding of the additional risk factors in smokers. To gain a deeper understanding of the COPD molecular signatures, we used Sparse Multiple Canonical Correlation Network (SmCCNet), a recently developed tool that uses sparse multiple canonical correlation analysis, to integrate proteomic and metabolomic data from the blood of 1008 participants of the COPDGene study to identify novel protein–metabolite networks associated with lung function and emphysema. Our aim was to integrate -omic data through SmCCNet to build interpretable networks that could assist in the discovery of novel biomarkers that may have been overlooked in alternative biomarker discovery methods. We found a protein–metabolite network consisting of 13 proteins and 7 metabolites which had a −0.34 correlation (p-value = 2.5 × 10⁻²⁸) to lung function. We also found a network of 13 proteins and 10 metabolites that had a −0.27 correlation (p-value = 2.6 × 10⁻¹⁷) to percent emphysema. Protein–metabolite networks can provide additional information on the progression of COPD that complements single biomarker or single -omic analyses.

中文翻译：

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识别与COPD表型相关的蛋白质代谢网络。

慢性阻塞性肺疾病（COPD）是一种肺部气流阻塞使患者呼吸困难的疾病。尽管COPD主要发生在吸烟者中，但我们对吸烟者其他危险因素的理解仍存在不足。为了更深入地了解COPD分子标记，我们使用了稀疏多规范相关网络（SmCCNet），这是一种最近开发的工具，使用稀疏多规范相关分析来整合来自1008名COPDGene研究参与者血液中的蛋白质组学和代谢组学数据以确定与肺功能和肺气肿有关的新型蛋白质代谢网络。我们的目标是通过SmCCNet整合组学数据，以建立可解释的网络，以帮助发现可能在其他生物标记物发现方法中被忽略的新型生物标记物。我们发现了由13种蛋白质和7种代谢物组成的蛋白质代谢网络，它们具有-0.34的相关性（p值= 2.5×10 ^-28）的肺功能。我们还发现了由13种蛋白质和10种代谢物组成的网络，与肺气肿百分率具有-0.27的相关性（p值= 2.6×10 ^-17）。蛋白质代谢物网络可以提供有关COPD进展的其他信息，可补充单个生物标志物或单个组学分析。