ArticleHeliyon2024
Identification of a PANoptosis-related prognostic model in triple-negative breast cancer, from risk assessment, immunotherapy, to personalized treatment.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Endothelial-derived PANoptosis factor IL33 is a potential immunotherapy in breast cancer.iScience · 2026Article
- Overcoming immunotherapy resistance in triple-negative breast cancer: a critical review of mast cell plasticity, metabolic reprogramming, and organoid models.Frontiers in immunology · 2026Review
- Targeting the redox-programmed cell death axis in breast cancer: from molecular mechanisms to therapeutic resistance.Cell death discovery · 2025Review
- Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Triple-negative breast cancer is a breast cancer subtype characterized by its challenging prognosis, and establishing prognostic models aids its clinical treatment. PANoptosis, a recently identified type of programmed cell death, influences tumor growth and patient outcomes. Nonetheless, the precise impact of PANoptosis-related genes on the prognosis of triple-negative breast cancer has yet to be determined. Methods: Clinical information for the triple-negative breast cancer samples was collected from the Gene Expression Omnibus and The Cancer Genome Atlas databases, while 19 PANoptosis-related genes were sourced from previous studies. We first categorized PANoptosis-related subtypes and determined the differentially expressed genes between them. Subsequently, we developed and validated a PANoptosis-associated predictive model using LASSO and Cox multivariate regression analyses. Statistical evaluations were conducted using R software, and the mRNA expression levels of the genes were quantified using real-time PCR. Results: Using consensus clustering analysis, we divided triple-negative breast cancer patients into two clusters based on PANoptosis-related genes and identified 1054 differentially expressed genes between these clusters. Prognostic-related genes were subsequently selected to re-cluster patients, validating their predictive ability. A prognostic model was then constructed based on four genes: BTN2A2, CACNA1H, PIGR, and S100B. The expression and enriched cell types of these genes were examined and the expression levels were validated in vitro. Furthermore, the model was validated, and a nomogram was created to enhance personalized risk assessment. The risk score, proven to be an independent prognostic indicator for triple-negative breast cancer, showed a positive correlation with both age and disease stage. Immune infiltration and drug sensitivity analyses suggested appropriate therapies for different risk groups. Mutation profiles and pathway enrichment were analyzed, providing insights into potential therapeutic targets. Conclusion: A PANoptosis-related prognostic model was successfully developed for triple-negative breast cancer, offering a novel approach for predicting patient prognosis and guiding treatment strategies.
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