An important goal in cancer research is the survival prognosis of a patient based on a minimal panel of genomic and molecular markers such as genes or proteins. Purely data-driven models without any biological knowledge can produce non-interpretable results. We propose a penalized semiparametric Bayesian Cox model with graph-structured selection priors for sparse identification of multi-omics features by making use of a biologically meaningful graph via a Markov random field (MRF) prior to capturing known relationships between multi-omics features. Since the fixed graph in the MRF prior is for the prior probability distribution, it is not a hard constraint to determine variable selection, so the proposed model can verify known information and has the potential to identify new and novel biomarkers for drawing new biological knowledge. Our simulation results show that the proposed Bayesian Cox model with graph-based prior knowledge results in more trustable and stable variable selection and non-inferior survival prediction, compared to methods modeling the covariates independently without any prior knowledge. The results also indicate that the performance of the proposed model is robust to a partially correct graph in the MRF prior, meaning that in a real setting where not all the true network information between covariates is known, the graph can still be useful. The proposed model is applied to the primary invasive breast cancer patients data in The Cancer Genome Atlas project.

翻译：癌症研究的一个重要目标是基于最小化的基因组和分子标志物（如基因或蛋白质）面板对患者的生存预后进行预测。缺乏生物学知识的纯数据驱动模型可能产生难以解释的结果。我们提出了一种带有图结构选择先验的惩罚半参数贝叶斯Cox模型，通过利用具有生物学意义的图，借助马尔可夫随机场（MRF）先验来捕获多组学特征之间的已知关系，从而实现多组学特征的稀疏识别。由于MRF先验中的固定图是针对先验概率分布的，它并非决定变量选择的硬性约束，因此所提出的模型能够验证已知信息，并具有识别新的生物标志物以获取新生物学知识的潜力。我们的模拟结果表明，与那些对协变量进行独立建模且无任何先验知识的方法相比，所提出的基于图先验知识的贝叶斯Cox模型能够产生更可靠、更稳定的变量选择结果，且生存预测性能不逊色。结果还表明，所提出模型的性能对MRF先验中部分正确的图具有鲁棒性，这意味着在实际场景中，即使并非所有协变量之间的真实网络信息都已知，该图仍然可以发挥作用。所提出的模型已应用于癌症基因组图谱项目中的原发性浸润性乳腺癌患者数据。