Gene expression profiles have been extensively discussed as an aid to guide the therapy by predicting disease outcome for the patients suffering from complex diseases, such as cancer. However, prediction models built upon single-gene (SG) features show poor stability and performance on independent datasets. Attempts to mitigate these drawbacks have led to the development of network-based approaches that integrate pathway information to produce meta-gene (MG) features. Also, MG approaches have only dealt with the two-class problem of good versus poor outcome prediction. Stratifying patients based on their molecular subtypes can provide a detailed view of the disease and lead to more personalized therapies. We propose and discuss a novel MG approach based on de novo pathways, which for the first time have been used as features in a multi-class setting to predict cancer subtypes. Comprehensive evaluation in a large cohort of breast cancer samples from The Cancer Genome Atlas (TCGA) revealed that MGs are considerably more stable than SG models, while also providing valuable insight into the cancer hallmarks that drive them. In addition, when tested on an independent benchmark non-TCGA dataset, MG features consistently outperformed SG models. We provide an easy-to-use web service at http://pathclass.compbio.sdu.dk where users can upload their own gene expression datasets from breast cancer studies and obtain the subtype predictions from all the classifiers.

中文翻译：

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基于从头途径的生物标志物鉴定

基因表达谱已被广泛讨论，作为通过预测患有诸如癌症的复杂疾病的患者的疾病结果来指导治疗的辅助手段。但是，基于单基因（SG）功能构建的预测模型在独立数据集上显示出较差的稳定性和性能。减轻这些缺点的尝试导致了基于网络的方法的发展，该方法集成了路径信息以产生元基因（MG）功能。同样，MG方法仅处理好与坏结果预测的两类问题。根据他们的分子亚型对患者进行分层可以提供对该疾病的详细了解，并导致更加个性化的治疗。我们提出并讨论了一种基于de novo的新颖的MG方法通路，首次在多类别设置中用作预测癌症亚型的特征。对来自癌症基因组图谱（TCGA）的大量乳腺癌样本的综合评估表明，MGs比SG模型更稳定，同时也为推动其发展的癌症标志提供了宝贵的见识。此外，当在独立的基准非TCGA数据集上进行测试时，MG的性能始终优于SG模型。我们在http://pathclass.compbio.sdu.dk上提供了一个易于使用的Web服务，用户可以在其中上载来自乳腺癌研究的自己的基因表达数据集，并从所有分类器中获得亚型预测。