A multi-source fusion method to identify biomarkers for breast cancer prognosis based on dual-layer heterogeneous network

The prognosis of breast cancer is challenging, which is an urgent problem to be solved. The prognostic biomarkers for breast cancer can help us predict the clinical outcomes of patients, and network-based methods are widely introduced to find prognostic biomarkers. According to the difference of input biological data, existing network-based biomarker prediction methods are mainly classified into two types: integrating single-source network or multi-source networks. However, the interactome of single-source network remains incomplete, and biological networks are noisy, which will hamper the network-based identification accuracy of biomarkers. In this study, we introduce a multi-source fusion method, DualMarker, which integrates multiple biological information sources and constructs a dual-layer heterogeneous network by fast network embedding. Next, we introduce a network enhancement method to denoise the constructed dual-layer heterogeneous network, and we implement network propagation algorithm on the constructed dual-layer heterogeneous network to rank the features. After comparing with competitive methods, we find that DualMarker substantially outperforms these methods. In addition, we verify that the biomarkers identified by DualMarker are closely related to the prognosis of breast cancer patients.