M&D Data Science Center, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, Japan.
Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokane-dai, Minato-ku, Tokyo, Japan.
BMC Bioinformatics. 2022 Aug 16;23(1):342. doi: 10.1186/s12859-022-04871-z.
Gene regulatory networks have garnered a large amount of attention to understand disease mechanisms caused by complex molecular network interactions. These networks have been applied to predict specific clinical characteristics, e.g., cancer, pathogenicity, and anti-cancer drug sensitivity. However, in most previous studies using network-based prediction, the gene networks were estimated first, and predicted clinical characteristics based on pre-estimated networks. Thus, the estimated networks cannot describe clinical characteristic-specific gene regulatory systems. Furthermore, existing computational methods were developed from algorithmic and mathematics viewpoints, without considering network biology.
To effectively predict clinical characteristics and estimate gene networks that provide critical insights into understanding the biological mechanisms involved in a clinical characteristic, we propose a novel strategy for predictive gene network estimation. The proposed strategy simultaneously performs gene network estimation and prediction of the clinical characteristic. In this strategy, the gene network is estimated with minimal network estimation and prediction errors. We incorporate network biology by assuming that neighboring genes in a network have similar biological functions, while hub genes play key roles in biological processes. Thus, the proposed method provides interpretable prediction results and enables us to uncover biologically reliable marker identification. Monte Carlo simulations shows the effectiveness of our method for feature selection in gene estimation and prediction with excellent prediction accuracy. We applied the proposed strategy to construct gastric cancer drug-responsive networks.
We identified gastric drug response predictive markers and drug sensitivity/resistance-specific markers, AKR1B10, AKR1C3, ANXA10, and ZNF165, based on GDSC data analysis. Our results for identifying drug sensitive and resistant specific molecular interplay are strongly supported by previous studies. We expect that the proposed strategy will be a useful tool for uncovering crucial molecular interactions involved a specific biological mechanism, such as cancer progression or acquired drug resistance.
基因调控网络在理解复杂分子网络相互作用引起的疾病机制方面受到了广泛关注。这些网络已被应用于预测特定的临床特征，例如癌症、致病性和抗癌药物敏感性。然而，在大多数以前使用基于网络的预测的研究中，首先估计基因网络，然后根据预先估计的网络预测临床特征。因此，估计的网络不能描述特定临床特征的基因调控系统。此外，现有的计算方法是从算法和数学的角度开发的，而没有考虑网络生物学。
为了有效地预测临床特征并估计能够深入了解涉及临床特征的生物学机制的基因网络，我们提出了一种用于预测基因网络估计的新策略。该策略同时进行基因网络估计和临床特征预测。在该策略中，通过最小化网络估计和预测误差来估计基因网络。我们通过假设网络中的相邻基因具有相似的生物学功能，而枢纽基因在生物过程中发挥关键作用，从而将网络生物学纳入其中。因此，该方法提供了可解释的预测结果，并使我们能够发现具有生物学可靠性的标记识别。蒙特卡罗模拟表明，该方法在基因估计和预测中的特征选择具有有效性，并且具有出色的预测准确性。我们应用该策略构建了胃癌药物反应网络。
我们基于 GDSC 数据分析鉴定了胃癌药物反应的预测标志物和药物敏感性/耐药性特异性标志物，AKR1B10、AKR1C3、ANXA10 和 ZNF165。我们识别药物敏感和耐药特异性分子相互作用的结果得到了先前研究的有力支持。我们期望该策略将成为揭示特定生物学机制（如癌症进展或获得性耐药）中关键分子相互作用的有用工具。
