ArticleBioengineering (Basel, Switzerland)2025
Leveraging Diverse Cell-Death Patterns to Decipher the Interactive Relation of Unfavorable Outcome and Tumor Microenvironment in Breast Cancer.
Article in Bioengineering (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- A programmed cell death learning signature predicts immunotherapy response and identifies AP1S1 as a regulator of immune exclusion in breast cancer.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026Article
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Authors and funding
9 authors.
Funding
Abstract
backgroundProgrammed cell death (PCD) dynamically influences breast cancer (BC) prognosis through interactions with the tumor microenvironment (TME). We investigated 13 PCD patterns to decipher their prognostic impact and mechanistic links to TME-driven outcomes. Our study aimed to explore the complex mechanisms underlying these interactions and establish a prognostic prediction model for breast cancer.
methodsUsing TCGA and METABRIC datasets, we integrated single-sample gene set enrichment analysis (ssGSEA), weighted gene co-expression network analysis (WGCNA), and Least Absolute Shrinkage and Selection Operator (LASSO) to explore PCD-TME interactions. Multi-dimensional analyses included immune infiltration, genomic heterogeneity, and functional pathway enrichment.
resultsOur results indicated that high apoptosis and pyroptosis activity, along with low autophagy, correlated with favorable prognosis, which was driven by enhanced anti-tumor immunity, including more M1 macrophage polarization and activated CD8+ T cells in TME. PCD-related genes could promote tumor metastasis and poor prognosis via VEGF/HIF-1/MAPK signaling and immune response, including Th1/Th2 cell differentiation, while new tumor event occurrences (metastasis/secondary cancers) were linked to specific clinical features and gene mutation spectrums, including TP53/CDH1 mutations and genomic instability. We constructed a six-gene LASSO model (
conclusionOur study provided a comprehensive landscape of the cancer survival difference and related PCD-TME interaction axis and highlighted that high-apoptosis/pyroptosis states caused favorable prognosis, underlying mechanisms closely related with the TME where anti-tumor immunity would be beneficial for patient prognosis. These findings highlighted the model's potential for risk stratification in BC.
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