网络引导的惩罚回归及其在蛋白质组学数据中的应用
A Network-Guided Penalized Regression with Application to Proteomics Data
AI总结：
针对高维数据中核心蛋白质预后作用研究不足的问题，提出一种网络引导的惩罚回归方法，结合Gaussian graphical model与adaptive Lasso进行变量选择，并在CPTAC数据上验证了其识别疾病预后生物标志物的有效性。
摘要：
网络理论在揭示复杂蛋白质相互作用方面已被证明具有不可估量的价值。以往的研究采用了基于网络理论的统计方法，包括Gaussian graphical model（高斯图模型），来推断蛋白质间的网络，并基于度中心性等网络关键结构特性识别hub proteins（核心蛋白质）。然而，在高维数据背景下，在调整临床协变量的同时，探讨hub proteins对预后的作用的研究十分有限。为填补这一空白，我们提出了一种网络引导的惩罚回归方法。首先，我们利用Gaussian graphical model构建网络以识别hub proteins。接着，我们保留这些已识别的hub proteins及临床相关因素，同时对非hub proteins应用adaptive Lasso进行变量选择。我们的网络引导估计量被证明具有变量选择一致性和渐近正态性。模拟结果表明，与现有方法相比，我们的方法产生了更好的结果，并显示出在推进蛋白质组学研究中生物标志物识别的潜力。最后，我们将该方法应用于Clinical Proteomic Tumor Analysis Consortium (CPTAC)数据，识别出可能作为多种疾病预后生物标志物的hub proteins，包括罕见遗传疾病和癌症免疫治疗的immune checkpoint（免疫检查点）。
英文摘要：
Network theory has proven invaluable in unraveling complex protein interactions. Previous studies have employed statistical methods rooted in network theory, including the Gaussian graphical model, to infer networks among proteins, identifying hub proteins based on key structural properties of networks such as degree centrality. However, there has been limited research examining a prognostic role of hub proteins on outcomes, while adjusting for clinical covariates in the context of high-dimensional data. To address this gap, we propose a network-guided penalized regression method. First, we construct a network using the Gaussian graphical model to identify hub proteins. Next, we preserve these identified hub proteins along with clinically relevant factors, while applying adaptive Lasso to non-hub proteins for variable selection. Our network-guided estimators are shown to have variable selection consistency and asymptotic normality. Simulation results suggest that our method produces better results compared to existing methods and demonstrates promise for advancing biomarker identification in proteomics research. Lastly, we apply our method to the Clinical Proteomic Tumor Analysis Consortium (CPTAC) data and identified hub proteins that may serve as prognostic biomarkers for various diseases, including rare genetic disorders and immune checkpoint for cancer immunotherapy.
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