癌症是全球死亡的主要原因之一。尽管在预防和治疗方面取得了重大进展，但对许多癌症类型来说，死亡率仍然很高。因此，需要使用分子数据来对患者进行分层和识别生物标志物的创新方法。有前途的生物标志物还可以从竞争性内源性RNA（ceRNA）网络中推断出，这些网络捕捉基因-miRNA基因调节景观。迄今为止，这些生物标志物的作用只能进行全球性研究，而不能以样本特定的方式进行研究。为了减轻这种情况，我们介绍了一种新方法，称为spongEffects，它从ceRNA网络中推断亚网络（或模块），并计算与其调节活性相关的患者或样本特异性得分。我们展示了spongEffects如何用于下游的解释和机器学习任务，例如肿瘤分类和识别子类型特定的调节相互作用。在乳腺癌亚型分类的具体示例中，我们优先考虑影响不同亚型的生物学的模块。总之，spongEffects将ceRNA模块作为生物标志物进行优先考虑，并提供miRNA调节景观的洞见。值得注意的是，这些模块得分仅可从基因表达数据中推断出，因此可应用于缺乏miRNA表达信息的队列中。https://bioconductor.org/packages/devel/bioc/html/SPONGE.html在Bioinformatics在线上提供。©作者2023年出版牛津大学出版社。

Cancer is one of the leading causes of death worldwide. Despite significant improvements in prevention and treatment, mortality remains high for many cancer types. Hence, innovative methods that use molecular data to stratify patients and identify biomarkers are needed. Promising biomarkers can also be inferred from competing endogenous RNA (ceRNA) networks that capture the gene-miRNA gene regulatory landscape. Thus far, the role of these biomarkers could only be studied globally but not in a sample-specific manner. To mitigate this, we introduce spongEffects, a novel method that infers subnetworks (or modules) from ceRNA networks and calculates patient- or sample-specific scores related to their regulatory activity.We show how spongEffects can be used for downstream interpretation and machine learning tasks such as tumor classification and for identifying subtype-specific regulatory interactions. In a concrete example of breast cancer subtype classification, we prioritize modules impacting the biology of the different subtypes. In summary, spongEffects prioritizes ceRNA modules as biomarkers and offers insights into the miRNA regulatory landscape. Notably, these module scores can be inferred from gene expression data alone and can thus be applied to cohorts where miRNA expression information is lacking.https://bioconductor.org/packages/devel/bioc/html/SPONGE.html.are available at Bioinformatics online.© The Author(s) 2023. Published by Oxford University Press.