ArticleChinese journal of cancer research = Chung-kuo yen cheng yen chiu2026
A programmed cell death learning signature predicts immunotherapy response and identifies AP1S1 as a regulator of immune exclusion in breast cancer.
Article in Chinese journal of cancer research = Chung-kuo yen cheng yen chiu, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: Breast cancer remains a leading cause of global cancer mortality, characterized by profound heterogeneity. While immune checkpoint blockade (ICB) has transformed oncology, its efficacy in breast cancer is often hindered by "immune-cold" microenvironments and immune exclusion. Programmed cell death (PCD) is a critical regulator of tumor immune microenvironment (TIME). However, its role in the breast cancer immune microenvironment remains poorly understood. Methods: We integrated multi-omics data from six breast cancer cohorts (N=3,764) to develop a programmed cell death learning signature (PCDsig) using over 100 machine learning combinations. The model was benchmarked against 29 published signatures. Single-cell transcriptomic analysis decoded the immune landscape and cellular crosstalk. The role of adaptor-related protein complex 1 subunit sigma 1 ( Results: PCDsig significantly stratified patient prognosis across all cohorts, consistently outperforming 29 existing models. High PCDsig scores correlated with immune-excluded phenotypes, reduced CD8+ T cell infiltration, and lower immunophenoscores. Single-cell analysis revealed that high-PCDsig tumors utilize vascular endothelial growth factor A (VEGFA) signaling to foster an immunosuppressive microenvironment. Conclusions: Our study establishes the PCDsig we developed is a potential prognostic and predictive biomarker for breast cancer. We provide the first evidence of
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