A pan-cancer modular regulatory network analysis to identify common and cancer-specific network components.
A pan-cancer modular regulatory network analysis to identify common and cancer-specific network components.
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作者：
Knaack SA;Siahpirani AF;Roy S
包括癌症在内的许多人类疾病都是控制基因特定环境表达的转录调控网络受到干扰的结果。跨多种癌症类型的比较方法是阐明这一疾病家族的共同和特定网络特征的有力方法。癌症基因组图谱（TCGA）最近的努力已经为多种类型的癌症产生了大量的功能基因组数据集。一个新出现的挑战是设计计算方法，系统地比较这些基因组数据集在不同的癌症类型，确定共同的和癌症特异性的网络组件。我们提出了一个模块和网络为基础的表征的转录模式在六种不同的癌症正在研究TCGA：乳腺癌，结肠癌，直肠癌，肾癌，卵巢癌和子宫内膜癌。我们的方法使用了最近开发的调控网络重建算法，模块化调控网络学习与每个基因的信息（MERLIN），在稳定性选择框架内预测调节个别基因和基因模块。我们基于模块的分析确定了每个癌症研究中免疫系统过程的共同主题，其中模块在统计学上富集了免疫应答过程以及干扰素调节因子（IRF）和信号转导和转录激活因子（STAT）家族的关键免疫应答调节因子的靶标。通过比较从每种癌症类型中推断出的调控网络，确定了一个核心调控网络，其中包括参与染色质重塑、细胞周期和免疫反应的基因。调控网络中心包括在特定癌症类型中具有已知作用的基因以及在不同癌症类型中具有潜在新作用的基因。总的来说，我们的综合模块和网络分析概括了癌症生物学中的已知主题，并揭示了新的调控中心，这些中心表明多种癌症中免疫反应，细胞周期和染色质重塑的复杂相互作用。
Many human diseases including cancer are the result of perturbations to transcriptional regulatory networks that control context-specific expression of genes. A comparative approach across multiple cancer types is a powerful approach to illuminate the common and specific network features of this family of diseases. Recent efforts from The Cancer Genome Atlas (TCGA) have generated large collections of functional genomic data sets for multiple types of cancers. An emerging challenge is to devise computational approaches that systematically compare these genomic data sets across different cancer types that identify common and cancer-specific network components. We present a module- and network-based characterization of transcriptional patterns in six different cancers being studied in TCGA: breast, colon, rectal, kidney, ovarian, and endometrial. Our approach uses a recently developed regulatory network reconstruction algorithm, modular regulatory network learning with per gene information (MERLIN), within a stability selection framework to predict regulators for individual genes and gene modules. Our module-based analysis identifies a common theme of immune system processes in each cancer study, with modules statistically enriched for immune response processes as well as targets of key immune response regulators from the interferon regulatory factor (IRF) and signal transducer and activator of transcription (STAT) families. Comparison of the inferred regulatory networks from each cancer type identified a core regulatory network that included genes involved in chromatin remodeling, cell cycle, and immune response. Regulatory network hubs included genes with known roles in specific cancer types as well as genes with potentially novel roles in different cancer types. Overall, our integrated module and network analysis recapitulated known themes in cancer biology and additionally revealed novel regulatory hubs that suggest a complex interplay of immune response, cell cycle, and chromatin remodeling across multiple cancers.