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Background <p>Breast cancer remains a major threat to women’s health worldwide. The study aims to investigate the role of immune-related programmed cell death (IPCD) pathways and related genes in breast cancer progression and prognosis.</p> Methods <p>We analyzed multi-omics datasets from TCGA, ICGC, and multiple GEO cohorts to screen for IPCD-related differentially expressed genes (DEGs) and established an IPCD-based signature (IPCDS) model for prognosis via 101 machine learning algorithm combinations. Functional enrichment analysis, survival analysis, principal component analysis (PCA), and immune correlation analysis were conducted by packages in R software.</p> Results <p>The screened IPCD-related DEGs were primarily enriched in the MAPK and PI3K-AKT signaling pathways, as well as focal adhesion. Across different cohorts, patients in the high-IPCDS groups showed the worse overall survival than those in the low-IPCDS groups, indicating the robust prognostic value. The IPCDS model demonstrated a higher C-index than other published models. To predict the response to immunotherapy, application of the TIDE algorithm to TCGA data revealed a significant significant association between IPCDS and immunotherapy response; the low-IPCDS group had a lower TIDE score, while non-responders had a higher IPCDS score. Furthermore, the IPCDS score was negatively correlated with SIAH2 expression. High expression of SIAH2 predicted better survival and was inversely correlated with immune scores, suggesting its potential role as a protective biomarker [hazard ratios (HR) = 0.64]. A significant negative correlation was also observed between SIAH2 and CD8A expression (<i>p</i> = 0.0011).</p> Conclusion <p>We established and validated a robust IPCDS model that effectively prognosticates breast cancer patients and predicts immunotherapy response. The model addresses the gap in integrating immune and programmed cell death pathways into a unified prognostic framework, outperforms existing models, and nominates SIAH2 as a potential key regulator. The elevated expression of SIAH2 is associated with a lower IPCD score, which in turn correlates with improved overall survival and reduced CD8 + T cell infiltration, highlighting its translational potential in guiding immune-targeted therapies.</p>

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Omics analysis reveals the prognostic value of IPCDS models and potential targets for immunotherapy

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月27日

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  • Shenli Huang,
  • Minmin Zhang,
  • Yingjie Chen,
  • Longgui Xie,
  • Ziran Qiu,
  • Na Jin,
  • Wenqing Cao,
  • Huawei Yang

摘要

Background

Breast cancer remains a major threat to women’s health worldwide. The study aims to investigate the role of immune-related programmed cell death (IPCD) pathways and related genes in breast cancer progression and prognosis.

Methods

We analyzed multi-omics datasets from TCGA, ICGC, and multiple GEO cohorts to screen for IPCD-related differentially expressed genes (DEGs) and established an IPCD-based signature (IPCDS) model for prognosis via 101 machine learning algorithm combinations. Functional enrichment analysis, survival analysis, principal component analysis (PCA), and immune correlation analysis were conducted by packages in R software.

Results

The screened IPCD-related DEGs were primarily enriched in the MAPK and PI3K-AKT signaling pathways, as well as focal adhesion. Across different cohorts, patients in the high-IPCDS groups showed the worse overall survival than those in the low-IPCDS groups, indicating the robust prognostic value. The IPCDS model demonstrated a higher C-index than other published models. To predict the response to immunotherapy, application of the TIDE algorithm to TCGA data revealed a significant significant association between IPCDS and immunotherapy response; the low-IPCDS group had a lower TIDE score, while non-responders had a higher IPCDS score. Furthermore, the IPCDS score was negatively correlated with SIAH2 expression. High expression of SIAH2 predicted better survival and was inversely correlated with immune scores, suggesting its potential role as a protective biomarker [hazard ratios (HR) = 0.64]. A significant negative correlation was also observed between SIAH2 and CD8A expression (p = 0.0011).

Conclusion

We established and validated a robust IPCDS model that effectively prognosticates breast cancer patients and predicts immunotherapy response. The model addresses the gap in integrating immune and programmed cell death pathways into a unified prognostic framework, outperforms existing models, and nominates SIAH2 as a potential key regulator. The elevated expression of SIAH2 is associated with a lower IPCD score, which in turn correlates with improved overall survival and reduced CD8 + T cell infiltration, highlighting its translational potential in guiding immune-targeted therapies.