Background: Cancer is a complex disease with a lucid etiology and in understanding the causation, we need to appreciate this complexity. Objective: Here we are aiming to gain insights into the genetic associations of prostate cancer through a network-based systems approach using the BC3Net algorithm. Methods: Specifically, we infer a prostate cancer Gene Regulatory Network (GRN) from a large-scale gene expression data set of 333 patient RNA-seq profiles obtained from The Cancer Genome Atlas (TCGA) database. Results: We analyze the functional components of the inferred network by extracting subnetworks based on biological process information and interpret the role of known cancer genes within each process. Fur-thermore, we investigate the local landscape of prostate cancer genes and discuss pathological associa-tions that may be relevant in the development of new targeted cancer therapies. Conclusion: Our network-based analysis provides a practical systems biology approach to reveal the collective gene-interactions of prostate cancer. This allows a close interpretation of biological activity in terms of the hallmarks of cancer.

中文翻译：

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从RNA-Seq数据推断前列腺癌基因调控网络

背景：癌症是一种复杂的疾病，具有明确的病因，在理解其因果关系时，我们需要认识到这种复杂性。目的：我们的目标是通过使用 BC3Net 算法的基于网络的系统方法来深入了解前列腺癌的遗传关联。方法：具体而言，我们从癌症基因组图谱 (TCGA) 数据库中获得的 333 名患者 RNA 序列图谱的大规模基因表达数据集中推断出前列腺癌基因调控网络 (GRN)。结果：我们通过基于生物过程信息提取子网络来分析推断网络的功能组件，并解释已知癌症基因在每个过程中的作用。此外，我们研究了前列腺癌基因的局部情况，并讨论了可能与新的靶向癌症疗法的开发相关的病理关联。结论：我们基于网络的分析提供了一种实用的系统生物学方法来揭示前列腺癌的集体基因相互作用。这使得可以根据癌症的特征来仔细解释生物活性。