A lipid metabolism-related gene signature predicts prognosis after tamoxifen treatment in ER + breast cancer and reflects tumor microenvironment heterogeneity through single-cell analysis.

ABSTRACT:

Background

Although the clinical outcome of ER + breast cancer patients receiving tamoxifen after surgery is favorable, a proportion of patients experience recurrence or death due to disease progression.

Methods

In this study, by integrating lipid metabolism gene expression and machine learning data, a prognostic model based on gene expression was developed using the TCGA-ER + BRCA dataset (N = 183) and validated with the GSE17705 (N = 298), GSE22219 (N = 134), GSE42568 (N = 70), and GSE58644 (N = 147) datasets. Patients were stratified into high- and low-risk groups based on the median risk score of the signature. Comparative analyses of survival, genomic features, immune infiltration, and drug sensitivity were performed between these groups.

Results

Patients in the

SUBMITTER: Ma L 

PROVIDER: S-EPMC12312246 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature