ArticleOncology letters2026
Machine learning-based programmed cell death gene signature for prognosis and drug sensitivity in breast cancer.
Article in Oncology letters, 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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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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2 authors.
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Abstract
Breast cancer (BRCA) heterogeneity necessitates robust prognostic biomarkers. Programmed cell death (PCD) serves a key role in tumor progression and therapy response. However, the prognostic potential of PCD-related genes (CRGs) in BRCA remains to be fully elucidated. Therefore, the present study integrated transcriptomic data from The Cancer Genome Atlas, Molecular Taxonomy of Breast Cancer International Consortium and Gene Expression Omnibus databases. Differentially-expressed CRGs were identified in tumor tissues and subjected to univariate Cox regression analysis. A comprehensive machine learning framework, encompassing 101 algorithm combinations, was applied to construct an optimal PCD-based gene signature (CDS). The prognostic value of the CDS, and its association with the tumor immune microenvironment (TIME), predictive power for immunotherapy and drug sensitivity were systematically evaluated using the 'immunedeconv' and 'OncoPredict' R packages. A five-gene CDS (anoctamin 6, polo-like kinase 1, solute carrier family 7 member 5, tubulin α-1C chain and transcobalamin 1) was developed using the Stepwise Cox (both) + Elastic Net (α=0.9) model, demonstrating notably increased predictive performance (concordance index=0.79). High CDS scores were found to be independent prognostic factors for inferior overall survival and were associated with an immunosuppressive TIME, characterized by reduced CD8
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