Systematic identification of gene regulatory modules can provide invaluable knowledge towards understanding aberrant transcriptional/post-transcriptional collaborative regulatory (co-regulatory) effects in cancer. Transcription factor (TF) and microRNA (miRNA) are known as two classes of prominent regulators that play crucial roles in gene regulation. Existing studies on gene regulatory modules identification mainly focused on the miRNA-mediated regulatory network, and few considered these two regulators in a co-occurring network. In this current study, we developed a computational method called BiModule for systematically identifying TF-miRNA co-regulatory modules. BiModule operates in two main stages: it first constructs a cancerspecific regulator-mRNA network and then identifies modules based on maximal bicliques by employing biclique modularity strategy, which is a novel flexible method for bipartite graph mining. We applied our model to a cervical cancer dataset. The results showed that the TF-miRNA co-regulatory modules identified by BiModule exhibit denser connections and stronger expression correlations than another existing related method. Moreover, the BiModule-modules exhibit high biological functional enrichment. In addition, based on Kaplan-Meier survival analysis, we found a number of modules with significant prognostic associations.

中文翻译：

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BiModule：用于识别转录因子和microRNA共同调控模块的双峰模块化策略。

基因调控模块的系统鉴定可以提供宝贵的知识，以了解癌症中异常的转录/转录后协同调控（共同调控）作用。转录因子（TF）和微RNA（miRNA）被称为两类突出的调节子，它们在基因调节中起关键作用。现有的关于基因调控模块鉴定的研究主要集中在miRNA介导的调控网络上，很少有人在共同存在的网络中考虑这两个调控基因。在本研究中，我们开发了一种称为BiModule的计算方法，用于系统地识别TF-miRNA协同调节模块。BiModule的运行分为两个主要阶段：它首先构建了一个癌症特异性调节子-mRNA网络，然后通过采用双斜度模块化策略基于最大双斜度来识别模块，这是一种新颖的灵活的二方图挖掘方法。我们将模型应用于宫颈癌数据集。结果表明，BiModule鉴定的TF-miRNA共同调控模块比其他现有相关方法具有更紧密的连接和更强的表达相关性。此外，BiModule模块具有很高的生物学功能。此外，基于Kaplan-Meier生存分析，我们发现了许多与预后相关的模块。结果表明，BiModule鉴定的TF-miRNA共同调控模块比其他现有相关方法具有更紧密的连接和更强的表达相关性。此外，BiModule模块具有很高的生物学功能。此外，基于Kaplan-Meier生存分析，我们发现了许多与预后相关的模块。结果表明，BiModule鉴定的TF-miRNA共同调控模块比其他现有相关方法具有更紧密的连接和更强的表达相关性。此外，BiModule模块具有很高的生物学功能。此外，基于Kaplan-Meier生存分析，我们发现了许多与预后相关的模块。