CEACAM6 as a machine learning derived immune biomarker for predicting neoadjuvant chemotherapy response in HR+/HER2- breast cancer.

ABSTRACT:

Background

Hormone receptor-positive/human epidermal growth factor receptor 2-negative (HR+/HER2-) breast cancer is the most common subtype, characterized by heterogeneous neoadjuvant chemotherapy (NAC) responses and low pCR rates. Existing biomarkers have limited predictive accuracy, hindering personalized treatment. This study aimed to identify predictive biomarkers for NAC response and explore their therapeutic potential in HR+/HER2- breast cancer.

Methods

We integrated 497 HR+/HER2- samples from TCGA and 956 from nine GEO datasets (training set: n=708; test set: n=248). Differentially expressed genes (DEGs) between tumors and normal tissues (TCGA) and between pCR and residual disease (RD) groups (GEO) were identified. Overlapping DEGs were further screened using LASSO, r

SUBMITTER: Fang D 

PROVIDER: S-EPMC12420257 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature