小细胞肺癌（SCLC）是一种侵袭性疾病，由于其转录亚型和亚型转变的混合，治疗起来具有挑战性。转录因子 (TF) 网络一直是通过系统方法识别 SCLC 亚型调节因子的研究焦点。然而，它们的结构可以提供有关亚型驱动因素和转变的线索，但几乎没有被研究过。在这里，我们使用图论概念分析 SCLC TF 网络的结构，并确定其负责复杂信号处理的结构上重要的组件（称为集线器）。我们首先分析无偏网络结构，然后将 RNA-seq 数据整合为分配给每个相互作用的权重，证明网络的中心是不同 SCLC 亚型的调节者。数据驱动的分析强调 MYC 作为中心，这与最近的报告一致。此外，我们假设连接功能不同的中心的路径可能控制亚型转换，并通过对候选路径的网络模拟来测试该假设并观察亚型转换。总体而言，复杂网络的结构分析可以识别其功能上重要的组件和驱动网络动态的路径。此类分析可以是生成假设的第一步，并可以指导发现目标路径，其扰动可能会改变网络动态表型。© 2023。作者。

Small cell lung cancer (SCLC) is an aggressive disease and challenging to treat due to its mixture of transcriptional subtypes and subtype transitions. Transcription factor (TF) networks have been the focus of studies to identify SCLC subtype regulators via systems approaches. Yet, their structures, which can provide clues on subtype drivers and transitions, are barely investigated. Here, we analyze the structure of an SCLC TF network by using graph theory concepts and identify its structurally important components responsible for complex signal processing, called hubs. We show that the hubs of the network are regulators of different SCLC subtypes by analyzing first the unbiased network structure and then integrating RNA-seq data as weights assigned to each interaction. Data-driven analysis emphasizes MYC as a hub, consistent with recent reports. Furthermore, we hypothesize that the pathways connecting functionally distinct hubs may control subtype transitions and test this hypothesis via network simulations on a candidate pathway and observe subtype transition. Overall, structural analyses of complex networks can identify their functionally important components and pathways driving the network dynamics. Such analyses can be an initial step for generating hypotheses and can guide the discovery of target pathways whose perturbation may change the network dynamics phenotypically.© 2023. The Author(s).