School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, China.
Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University Ministry of Industry and Information Technology, Xi'an, China.
BMC Bioinformatics. 2019 May 1;20(Suppl 7):194. doi: 10.1186/s12859-019-2740-6.
The mechanism of many complex diseases has not been detected accurately in terms of their stage evolution. Previous studies mainly focus on the identification of associations between genes and individual diseases, but less is known about their associations with specific disease stages. Exploring biological modules through different disease stages could provide valuable knowledge to genomic and clinical research.
In this study, we proposed a powerful and versatile framework to identify stage-specific cancer related genes and their dynamic modules by integrating multiple datasets. The discovered modules and their specific-signature genes were significantly enriched in many relevant known pathways. To further illustrate the dynamic evolution of these clinical-stages, a pathway network was built by taking individual pathways as vertices and the overlapping relationship between their annotated genes as edges.
The identified pathway network not only help us to understand the functional evolution of complex diseases, but also useful for clinical management to select the optimum treatment regimens and the appropriate drugs for patients.
许多复杂疾病的机制在其阶段演变方面尚未得到准确检测。以前的研究主要集中在鉴定基因与个体疾病之间的关联，但对它们与特定疾病阶段的关联知之甚少。通过不同的疾病阶段探索生物模块可以为基因组学和临床研究提供有价值的知识。
在这项研究中，我们提出了一个强大而通用的框架，通过整合多个数据集来识别特定阶段的癌症相关基因及其动态模块。发现的模块及其特定特征基因在许多相关的已知途径中显著富集。为了进一步说明这些临床阶段的动态演变，通过将单个途径作为顶点，将注释基因之间的重叠关系作为边，构建了一个途径网络。
所鉴定的途径网络不仅有助于我们理解复杂疾病的功能演变，而且对于临床管理也很有用，可以为患者选择最佳治疗方案和合适的药物。
