Abstract
Biomarker discovery in biomedicine is often cast as feature selection, yet most methods overlook gene co-localization within regulatory interaction networks, yielding isolated biomarkers with limited biological interpretability and clinical translatability. Here, we propose CNet-Cox, a disease-agnostic, Connected Network-regularized Cox proportional hazards framework that incorporates prior network connectivity into sparse feature selection to identify connected prognostic module. Applied to breast cancer, CNet-Cox revealed the network structure of 68 prognostic biomarkers associated with survival on discovery dataset (TCGA, n = 1080) and achieved a concordance index of 0.913 on internal test dataset, outperforming conventional regularized Cox methods. From these network biomarkers, we derived a six-gene prognostic risk score (PRS) and validated its robustness across seven independent bulk transcriptomic datasets (GEO; n = 1602) and a spatial transcriptomics dataset (Visium; 4992 spots). The PRS consistently improved risk stratification (log-rank p < 0.05) and produced concordant predictions with MammaPrint in spatial prognostics (Pearson r = 0.993). Although evaluated in breast cancer, CNet-Cox is readily extensible to other diseases, molecular interaction networks and time-to-event endpoints, providing a generalizable tool for digital pathology and precision oncology. Overall, our comprehensive downstream analyses highlight that CNet-Cox offers a novel network-aware survival model for systematically discovering connected biomarkers and delivering scalable, precise and interpretable risk prediction.
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Acknowledgements
The authors kindly thank the members of the lab at Shandong University, as well as the AMACL Lab and the StatBiomed Lab at the University of Hong Kong, for their valuable assistance with this project. The authors especially wish to thank Prof. Yuanhua Huang from the School of Biomedical Sciences, the University of Hong Kong, for his invaluable support and assistance. This work was partially supported by the National Natural Science Foundation of China (NSFC) under grant numbers 92374107 and 62373216, National Key Research and Development Program of China under grant number 2020YFA0712402, and Shandong Provincial Key Research and Development Program (Major Scientific and Technological Innovation Project) under grant number 2023CXGC010509 (Z.L.). This work was also supported in part by the Hong Kong RGC GRF Grant under grant number 17309522 (W.C.), the General Research Fund of the Research Grants Council of Hong Kong (no. 17209225 to Q.Z.), and the Shenzhen Loop Area Institute (no. FPF10120250014 and seed grant to Q.Z.).
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Qingpeng Zhang is an associate editor of npj Digital Medicine. The concerned author is not part of the peer review process or decision-making of the manuscript.
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Li, L., Zhao, W., Zhang, Q. et al. CNet-Cox for interpretable network biomarker discovery and survival risk scoring in precise breast cancer prognosis. npj Digit. Med. (2026). https://doi.org/10.1038/s41746-026-02756-6
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DOI: https://doi.org/10.1038/s41746-026-02756-6
