癌症具有分子异质性，看似相似的患者具有不同的分子结构，因此临床行为也不同。在最近的研究中，基因表达网络已被证明比一些更简单的测量方法对于癌症异质性分析更有效/信息更丰富。基因互连可以分为“直接”和“间接”，后者可能是由共享的基因组调节因子（例如转录因子、microRNA 和其他调节分子）和其他机制引起的。有人建议，将基因表达的调节因子纳入网络分析并关注直接互连可以导致对更重要的基因互连的更深入理解。这种分析可能会受到大量参数（由网络分析、监管机构的纳入和异质性共同引起）和通常较弱信号的严重挑战。为了有效解决这个问题，我们建议纳入已发表文献中包含的先前信息。一个关键的挑战是，此类先验信息可能是部分的，甚至是错误的。我们开发了一个两步程序，可以灵活地适应不同级别的先验信息质量。仿真证明了所提出方法的有效性及其相对于相关竞争对手的优越性。在对乳腺癌数据集的分析中，得出了与替代方案不同的结果，并且确定的样本亚组具有重要的临床差异。© 作者 2024。由牛津大学出版社出版。版权所有。 [br]如需权限，请发送电子邮件至：journals.permissions@oup.com。

Cancer is molecularly heterogeneous, with seemingly similar patients having different molecular landscapes and accordingly different clinical behaviors. In recent studies, gene expression networks have been shown as more effective/informative for cancer heterogeneity analysis than some simpler measures. Gene interconnections can be classified as "direct" and "indirect," where the latter can be caused by shared genomic regulators (such as transcription factors, microRNAs, and other regulatory molecules) and other mechanisms. It has been suggested that incorporating the regulators of gene expressions in network analysis and focusing on the direct interconnections can lead to a deeper understanding of the more essential gene interconnections. Such analysis can be seriously challenged by the large number of parameters (jointly caused by network analysis, incorporation of regulators, and heterogeneity) and often weak signals. To effectively tackle this problem, we propose incorporating prior information contained in the published literature. A key challenge is that such prior information can be partial or even wrong. We develop a two-step procedure that can flexibly accommodate different levels of prior information quality. Simulation demonstrates the effectiveness of the proposed approach and its superiority over relevant competitors. In the analysis of a breast cancer dataset, findings different from the alternatives are made, and the identified sample subgroups have important clinical differences.© The Author(s) 2024. Published by Oxford University Press. All rights reserved. [br]For permissions, please e-mail: journals.permissions@oup.com.