There are numerous ways of portraying cancer complexity based on combining multiple types of data. A common approach involves developing signatures from gene expression profiles to highlight a few key reproducible features that provide insight into cancer risk, progression, or recurrence. Normally, a selection of such features is made through relevance or significance, given a reference context. In the case of highly metastatic cancers, numerous gene signatures have been published with varying levels of validation. Then, integrating the signatures could potentially lead to a more comprehensive view of the connection between cancer and its phenotypes by covering annotations not fully explored in individual studies. This broader understanding of disease phenotypes would improve the predictive accuracy of statistical models used to identify meaningful associations. We present an example of this approach by reconciling a great number of published signatures into meta-signatures relevant to Osteosarcoma (OS) metastasis. We generate a well-annotated and interpretable interactome network from integrated OS gene expression signatures and identify key nodes that regulate essential aspects of metastasis. While the connected signatures link diverse prognostic measurements for OS, the proposed approach is applicable to any type of cancer.
基于整合多种数据类型，有多种方法可以描绘癌症的复杂性。一种常见方法是从基因表达谱中开发特征标记，以突出少数关键且可重复的特征，这些特征有助于深入理解癌症风险、进展或复发。通常，在给定参考背景下，这些特征的选择基于相关性或显著性。对于高转移性癌症，已发表了大量经过不同程度验证的基因特征标记。通过整合这些特征标记，可以覆盖单个研究中未充分探索的注释，从而更全面地揭示癌症与其表型之间的联系。这种对疾病表型更广泛的理解将提高统计模型的预测准确性，这些模型用于识别有意义的关联。我们通过整合大量已发表的特征标记，构建了与骨肉瘤转移相关的元特征标记，以此为例展示了该方法。我们从整合的骨肉瘤基因表达特征标记中，生成了一个注释详尽且可解释的相互作用网络，并识别出调控转移关键环节的核心节点。虽然所连接的特征标记关联了骨肉瘤的多种预后指标，但所提出的方法适用于任何类型的癌症。
Prioritizing Context-Dependent Cancer Gene Signatures in Networks
肿瘤(癌症)患者之家