BRCAGenie: A machine learning-driven 43-gene polygenic risk score model for precision prediction of breast cancer survival.

ABSTRACT:

Background

Breast cancer is one of the most prevalent malignancies globally, imposing a substantial disease burden. Its inherent heterogeneity complicates prognosis and treatment, underscoring the need for accurate survival prediction models to guide personalised clinical decision-making. Existing studies on breast cancer survival prediction models based on gene expression have largely focused on predefined subsets of genes, such as biologically-relevant, differentially-expressed or protein-coding genes, potentially overlooking critical but uncharacterised genomic contributors. To probe deeper beyond well-characterised biological pathways and differential gene expression, this study adopts an unbiased, hypothesis-free approach by conducting machine learning-based survival analysis

SUBMITTER: Lee JW 

PROVIDER: S-EPMC12574034 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature