Gene Regulatory Network-Classifier: Gene Regulatory Network-Based Classifier and Its Applications to Gastric Cancer Drug (5-Fluorouracil) Marker Identification
Gene Regulatory Network-Classifier: Gene Regulatory Network-Based Classifier and Its Applications to Gastric Cancer Drug (5-Fluorouracil) Marker Identification
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DOI：
10.1089/cmb.2022.0181
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发表时间：
2022-11-25
影响因子：
1.7
通讯作者：
Miyano，Satoru
中科院分区：
文献类型：
--
作者：
Park，Heewon;Imoto，Seiya;Miyano，Satoru
疾病的复杂机制涉及分子网络的干扰，而不是单个基因的紊乱，这意味着基于单个基因的分析不足以理解这些机制。基因调控网络（GRNs）已经引起了人们的极大兴趣，并且已经开发了各种方法用于其统计推断和基于基因网络的分析。虽然已经开发了各种计算方法，但相对较少注意将生物学知识纳入计算方法。此外，基于网络的分析的现有研究基于预构建的GRNs执行细胞系状态的预测/分类，这意味着我们不能提取预测/分类特定的基因网络，导致难以解释与癌细胞系状态相关的生物学机制和标记识别。我们开发了一种新的策略来构建基于GRN的分类器，称为GRN-classifier。建议的GRN分类器估计GRN和分类细胞系的同时，基因网络估计，以尽量减少错误的基因网络估计和负对数似然分类细胞系。因此，我们可以识别生物状态特异性基因调控系统，使我们能够实现生物学上可靠的分类解释。我们还提出了一种算法来实现基于坐标下降更新的GRN分类器。通过蒙特卡罗模拟来检验GRN分类器的性能。结果：我们的策略在分类模型中的特征选择和基因网络估计中的边缘选择方面提供了有效的结果。GRN分类器也表现出出色的分类精度。我们应用GRN分类器将癌细胞系分类为抗癌药物相关状态，即5-氟尿嘧啶（5-FU）敏感/耐药和5-FU靶/非靶癌细胞系。然后，我们根据5-FU相关状态分类特异性基因网络确定了5-FU标记。通过文献调查验证了所确定的标志物的机制。我们的研究结果表明，MYOF和AHNAK 2之间的分子相互作用可能在耐药中起着至关重要的作用，并可以提供有关5-FU化疗效果的信息。抑制5-FU标记物MYOF/AHNAK 2和AKR 1C 1/AKR 1C 3可能提高肿瘤细胞对5-FU的耐药性。
The complex mechanisms of diseases involve the disturbance of the molecular network, rather than disorder in a single gene, implying that single gene-based analysis is insufficient to understand these mechanisms. Gene regulatory networks (GRNs) have attracted a lot of interest and various approaches have been developed for their statistical inference and gene network-based analysis. Although various computational methods have been developed, relatively little attention has been paid to incorporation of biological knowledge into the computational approaches. Furthermore, existing studies on network-based analysis perform prediction/classification of status of cell lines based on preconstructed GRNs, implying that we cannot extract prediction/classification-specific gene networks, leading to difficulty in interpretation of biological mechanisms and marker identification related to the status of cancer cell lines. We developed a novel strategy to build a GRN-based classifier, called a GRN-classifier. The proposed GRN-classifier estimates GRNs and classifies cell lines simultaneously, where the gene network is estimated to minimize error in gene network estimation and the negative log-likelihood for classifying cell lines. Thus, we can identify biological status-specific gene regulatory systems, enabling us to achieve biologically reliable interpretation of the classification. We also propose an algorithm to implement the GRN-classifier based on coordinate descent update. Monte Carlo simulations were conducted to examine performance of the GRN-classifier.Results: Our strategy provides effective results in feature selection in the classification model and edge selection in gene network estimation. The GRN-classifier also shows outstanding classification accuracy. We apply the GRN-classifier to classify cancer cell lines into anticancer drug-related status, that is, 5-fluorouracil (5-FU)-sensitive/resistant and 5-FU target/nontarget cancer cell lines. We then identified 5-FU markers based on 5-FU-related status classification-specific gene networks. The mechanisms of the identified markers were verified through literature survey. Our results suggest that the molecular interplay betweenMYOFandAHNAK2may play a crucial role in drug resistance and can provide information on the chemotherapy efficiency of 5-FU. It is also suggested that suppression of the identified 5-FU markers, includingMYOF/AHNAK2andAKR1C1/AKR1C3may improve 5-FU resistance of cancer cell lines.