With the high prevalence of breast cancer, it is urgent to find out the intrinsic difference between various subtypes, so as to infer the underlying mechanisms. Given the available multi-omics data, their proper integration can improve the accuracy of breast cancer subtype recognition. In this study, DeepMO, a model using deep neural networks based on multi-omics data, was employed for classifying breast cancer subtypes. Three types of omics data including mRNA data, DNA methylation data, and copy number variation (CNV) data were collected from The Cancer Genome Atlas (TCGA). After data preprocessing and feature selection, each type of omics data was input into the deep neural network, which consists of an encoding subnetwork and a classification subnetwork. The results of DeepMO based on multi-omics on binary classification are better than other methods in terms of accuracy and area under the curve (AUC). Moreover, compared with other methods using single omics data and multi-omics data, DeepMO also had a higher prediction accuracy on multi-classification. We also validated the effect of feature selection on DeepMO. Finally, we analyzed the enrichment gene ontology (GO) terms and biological pathways of these significant genes, which were discovered during the feature selection process. We believe that the proposed model is useful for multi-omics data analysis.

中文翻译：

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使用基于多组学数据的深度神经网络对乳腺癌亚型进行分类

随着乳腺癌的高患病率，迫切需要找出不同亚型之间的内在差异，以推断其潜在机制。鉴于可用的多组学数据，它们的适当整合可以提高乳腺癌亚型识别的准确性。在本研究中，DeepMO（一种基于多组学数据的深度神经网络模型）被用于对乳腺癌亚型进行分类。从癌症基因组图谱（TCGA）中收集了三种类型的组学数据，包括 mRNA 数据、DNA 甲基化数据和拷贝数变异（CNV）数据。经过数据预处理和特征选择后，每种类型的组学数据被输入到深度神经网络中，该网络由编码子网络和分类子网络组成。基于多组学的DeepMO在二元分类上的结果在准确率和曲线下面积（AUC）方面优于其他方法。此外，与其他使用单组学数据和多组学数据的方法相比，DeepMO 在多分类上也具有更高的预测精度。我们还验证了特征选择对 DeepMO 的影响。最后，我们分析了在特征选择过程中发现的这些重要基因的富集基因本体（GO）术语和生物学途径。我们相信所提出的模型对于多组学数据分析很有用。