ArticleBiology2026
Age-Specific Transcriptomic Signatures for Classification of Progression-Free Survival Outcomes in Luminal A Breast Cancer: An Integrative Machine Learning Approach.
Article in Biology, 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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Abstract
Progression-free survival (PFS) is an important clinical endpoint in Luminal A breast cancer (BC), yet the molecular determinants of recurrence remain incompletely understood. This study aimed to identify recurrence-associated transcriptomic alterations across age-defined patient subgroups and evaluate their predictive utility using machine learning (ML). Gene expression profiles and clinical data from the METABRIC cohort were analyzed in premenopausal, postmenopausal non-geriatric, and geriatric patients with Luminal A BC. Differential expression analysis identified 32 significantly dysregulated genes, of which 15 genes with |log
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