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<p>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 <b>CNet-Cox</b>, a disease-agnostic, <b>C</b>onnected <b>N</b>etwork-regularized <b>Cox</b> 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 &lt; 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.</p>

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CNet-Cox for interpretable network biomarker discovery and survival risk scoring in precise breast cancer prognosis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月25日

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  • Lingyu Li,
  • Weiqin Zhao,
  • Qingpeng Zhang,
  • Wai-Ki Ching,
  • Zhi-Ping Liu

摘要

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.