Machine learning-based identification of kbhb-affected tumor cell subsets as prognostic and therapeutic targets in breast cancer.

ABSTRACT:

Background

Breast cancer heterogeneity complicates prognosis and treatment. Metabolic reprogramming, particularly lysine beta-hydroxybutyrylation (Kbhb) driven by ketone bodies, influences the tumor microenvironment. However, the impact of Kbhb on specific breast cancer subpopulations remains unclear. This study aims to identify Kbhb-affected tumor cell subsets and evaluate their prognostic potential.

Methods

We integrated multi-omics data from TCGA, GEO, single-cell RNA sequencing, and spatial transcriptomics. After identifying breast cancer subpopulations influenced by Kbhb-associated genes, we validated the functional role of key genes via molecular experiments. A machine learning-based prognostic model was developed using 101 algorithm combinations.

Results

We ide

SUBMITTER: Yuan Q 

PROVIDER: S-EPMC12802299 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature