School of Mathematics, Statistics and Data Science, Sungshin Women's University, Seoul, Korea.
Human Genome Center, The Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo, Japan.
PLoS One. 2023 Aug 23;18(8):e0286044. doi: 10.1371/journal.pone.0286044. eCollection 2023.
Biological condition-responsive gene network analysis has attracted considerable research attention because of its ability to identify pathways or gene modules involved in the underlying mechanisms of diseases. Although many condition-specific gene network identification methods have been developed, they are based on partial or incomplete gene regulatory network information, with most studies only considering the differential expression levels or correlations among genes. However, a single gene-based analysis cannot effectively identify the molecular interactions involved in the mechanisms underlying diseases, which reflect perturbations in specific molecular network functions rather than disorders of a single gene. To comprehensively identify differentially regulated gene networks, we propose a novel computational strategy called comprehensive analysis of differential gene regulatory networks (CIdrgn). Our strategy incorporates comprehensive information on the networks between genes, including the expression levels, edge structures and regulatory effects, to measure the dissimilarity among networks. We extended the proposed CIdrgn to cell line characteristic-specific gene network analysis. Monte Carlo simulations showed the effectiveness of CIdrgn for identifying differentially regulated gene networks with different network structures and scales. Moreover, condition-responsive network identification in cell line characteristic-specific gene network analyses was verified. We applied CIdrgn to identify gastric cancer and itsf chemotherapy (capecitabine and oxaliplatin) -responsive network based on the Cancer Dependency Map. The CXC family of chemokines and cadherin gene family networks were identified as gastric cancer-specific gene regulatory networks, which was verified through a literature survey. The networks of the olfactory receptor family with the ASCL1/FOS family were identified as capecitabine- and oxaliplatin sensitive -specific gene networks. We expect that the proposed CIdrgn method will be a useful tool for identifying crucial molecular interactions involved in the specific biological conditions of cancer cell lines, such as the cancer stage or acquired anticancer drug resistance.
生物状态响应基因网络分析因其能够识别与疾病潜在机制相关的途径或基因模块而引起了相当多的研究关注。虽然已经开发出许多特定于状态的基因网络识别方法，但它们基于部分或不完整的基因调控网络信息，大多数研究仅考虑基因之间的差异表达水平或相关性。然而，基于单个基因的分析不能有效地识别疾病潜在机制中涉及的分子相互作用，这些作用反映了特定分子网络功能的扰动，而不是单个基因的紊乱。为了全面识别差异调控的基因网络，我们提出了一种称为综合差异基因调控网络分析（CIdrgn）的新计算策略。我们的策略整合了基因之间网络的综合信息，包括表达水平、边缘结构和调控效应，以衡量网络之间的差异。我们将提出的 CIdrgn 扩展到细胞系特征特异性基因网络分析。蒙特卡罗模拟表明，CIdrgn 有效地识别具有不同网络结构和规模的差异调控基因网络。此外，还验证了细胞系特征特异性基因网络分析中的条件响应网络识别。我们应用 CIdrgn 基于癌症依赖图谱识别胃癌及其化疗（卡培他滨和奥沙利铂）反应网络。CXC 趋化因子家族和钙粘蛋白基因家族网络被鉴定为胃癌特异性基因调控网络，这通过文献调查得到了验证。嗅觉受体家族与 ASCL1/FOS 家族的网络被鉴定为卡培他滨和奥沙利铂敏感特异性基因网络。我们期望所提出的 CIdrgn 方法将成为识别与癌症细胞系特定生物学条件（如癌症阶段或获得性抗癌药物耐药性）相关的关键分子相互作用的有用工具。
