In clinical trials, prognostic biomarkers have become essential for guiding treatment decisions after breast cancer surgery. Network-based methods have gained notable attention to reveal marker genes, but many existing methods only focus on a single network, which inevitably neglects the incompleteness of interaction relationships within the network. Even when based upon the multiplex network, most of methods directly integrate the multiplex network into an aggregated network and do not take into account the inherent noise in the biological networks, which can not preserve the topological structure of each original network very well. In this study, we propose a novel method, WEmarker, for breast cancer-specific prognostic analysis. WEmarker reduces the noise level of biological networks and quantifies the probability of interactions between genes, and represents the nodes in the weighted multiplex network as vectors while efficiently retaining the structure information of these networks for identifying prognostic biomarkers. The results show that WEmarker outperforms comparative methods and the case study also demonstrates that biomarkers identified by WEmarker have reliable biological interpretability for breast cancer prognosis.

Breast cancer is a complex disease that arises from multiple factors, including genetics, age, and environmental factors. Prognosis prediction for breast cancer is a challenging task that urgently needs to be addressed. Prognostic biomarkers can aid in predicting clinical outcomes for breast cancer patients, and network-based approaches are frequently employed to identify such biomarkers. However, the accuracy of these approaches based on single source biological network is poor due to incomplete interactions of single biological network. Some network-based approaches that integrate multiple biological networks have not considered network denoising, which may lead to the accuracy of these approaches to be improved. We propose a multi-source fusion identification method named DualMarker for prognostic biomarkers of breast cancer. This method constructs a dual-layer heterogeneous network by integrating multiple biological sources. To decrease the negative effects of incomplete interactions in biological networks, we denoise the constructed network. The ranking of features is obtained by the network propagation algorithm and the initial scoring strategy. Compared with six other network-based methods, DualMarker shows the best performance in six breast cancer datasets. Moreover, we have also demonstrated that the biomarkers identified by DualMarker are of interpretability biologically and closely associated with breast cancer patients' prognosis.

Inflammatory bowel disease (IBD) is a complex disease that mainly consists of two subtypes, ulcerative colitis (UC) and Crohn's disease (CD). These two diseases exhibit similar clinical symptoms, leading to a potential misdiagnosis of patients. Accurately assessing the disease status and identifying specific biomarkers are important for the diagnosis and treatment of IBD. Pathways play a significant role in the occurrence and progression of complex diseases, involving the abnormal functionality or regulatory imbalance. Although many methods integrating pathway information have been proposed to evaluate pathway activity, but these methods rarely take into account the topology of pathway networks. Some algorithms based on pathway structure do not de-noise the pathway networks and characterize the disease-specific state of a single sample from the perspective of pathways. In this study, we present a personalized pathway activation inference method (PPA-PS) based on the pathway structure, which utilizes the topology of pathways to evaluate the importance of nodes and quantify the degree of edge disturbance caused by a single disease sample. The results demonstrate that PPA-PS outperforms the compared approaches in terms of classification performance and robustness, indicating its potential as a valuable tool for the pathway biomarker identification and precise diagnosis of IBD.

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.

MOTIVATION: Biomarkers with prognostic ability and biological interpretability can be used to support decision-making in the survival analysis. Genes usually form functional modules to play synergistic roles, such as pathways. Predicting significant features from the functional level can effectively reduce the adverse effects of heterogeneity and obtain more reproducible and interpretable biomarkers. Personalized pathway activation inference can quantify the dysregulation of essential pathways involved in the initiation and progression of cancers, and can contribute to the development of personalized medical treatments. RESULTS: In this study, we propose a novel method to evaluate personalized pathway activation based on signaling entropy for survival analysis (SEPA), which is a new attempt to introduce the information-theoretic entropy in generating pathway representation for each patient. SEPA effectively integrates pathway-level information into gene expression data, converting the high-dimensional gene expression data into the low-dimensional biological pathway activation scores. SEPA shows its classification power on the prognostic pan-cancer genomic data, and the potential pathway markers identified based on SEPA have statistical significance in the discrimination of high- and low-risk cohorts and are likely to be associated with the initiation and progress of cancers. The results show that SEPA scores can be used as an indicator to precisely distinguish cancer patients with different clinical outcomes, and identify important pathway features with strong discriminative power and biological interpretability. AVAILABILITY AND IMPLEMENTATION: The MATLAB-package for SEPA is freely available from https://github.com/xingyili/SEPA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.