Molecular Biotechnology Center, Department of Molecular Biotechnology and Health Sciences, University of Turin, Via Nizza 52, 10126 Turin, Italy.
Department of Neurosciences "Rita Levi Montalcini", University of Turin, Corso Massimo D'Ázeglio 52, 10126 Turin, Italy.
Int J Mol Sci. 2020 Dec 12;21(24):9461. doi: 10.3390/ijms21249461.
Biological systems respond to perturbations through the rewiring of molecular interactions, organised in gene regulatory networks (GRNs). Among these, the increasingly high availability of transcriptomic data makes gene co-expression networks the most exploited ones. co-expression networks are useful tools to identify changes in response to an external perturbation, such as mutations predisposing to cancer development, and leading to changes in the activity of gene expression regulators or signalling. They can help explain the robustness of cancer cells to perturbations and identify promising candidates for targeted therapy, moreover providing higher specificity with respect to standard co-expression methods. Here, we comprehensively review the literature about the methods developed to assess differential co-expression and their applications to cancer biology. Via the comparison of normal and diseased conditions and of different tumour stages, studies based on these methods led to the definition of pathways involved in gene network reorganisation upon oncogenes' mutations and tumour progression, often converging on immune system signalling. A relevant implementation still lagging behind is the integration of different data types, which would greatly improve network interpretability. Most importantly, performance and predictivity evaluation of the large variety of mathematical models proposed would urgently require experimental validations and systematic comparisons. We believe that future work on differential gene co-expression networks, complemented with additional omics data and experimentally tested, will considerably improve our insights into the biology of tumours.
生物系统通过分子相互作用的重连来响应扰动，这些相互作用组织在基因调控网络（GRNs）中。在这些相互作用中，转录组数据的可用性越来越高，使得基因共表达网络成为最常用的网络之一。共表达网络是识别对外部扰动响应变化的有用工具，例如导致癌症发展的易感性突变，并导致基因表达调节剂或信号转导活性的变化。它们可以帮助解释癌细胞对扰动的鲁棒性，并识别有希望的靶向治疗候选物，此外，与标准共表达方法相比，它们提供了更高的特异性。在这里，我们全面回顾了关于评估差异共表达的方法及其在癌症生物学中的应用的文献。通过比较正常和患病条件以及不同的肿瘤阶段，基于这些方法的研究导致了定义基因网络在致癌基因突变和肿瘤进展时重新组织的途径，这些途径通常集中在免疫系统信号上。一个相关的实施仍然落后的是不同数据类型的整合，这将大大提高网络的可解释性。最重要的是，需要对所提出的大量数学模型进行性能和预测性评估，并进行实验验证和系统比较。我们相信，补充额外的组学数据并经过实验验证的差异基因共表达网络的未来工作将极大地提高我们对肿瘤生物学的理解。
