Tumor stemness characteristics of luminal breast cancer and their application in prognostic model construction for elderly patients
10.3760/cma.j.issn.0254-9026.2025.12.015
- VernacularTitle:Luminal型乳腺癌肿瘤干细胞特征及其在老年患者预后模型构建中的应用研究
- Author:
Jicheng HUANG
1
;
Yi ZHANG
1
;
Yao LI
1
;
Bin HUA
1
Author Information
1. 北京医院普通外科乳腺中心 国家老年医学中心 中国医学科学院老年医学研究院，北京 100730
- Publication Type:Journal Article
- Keywords:
Luminal breast cancer;
tumor stemness;
prognosis
- From:
Chinese Journal of Geriatrics
2025;44(12):1720-1725
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the stemness characteristics of Luminal-type breast cancer (LBC, based on PAM50 classification) in patients aged over 60, evaluate the association between stemness and prognosis differences in LBC,, and construct a stemness-based prognostic prediction model.Methods:Based on data of LBC patients aged over 60 from the TCGA-BRCA database, we first applied single-sample gene set enrichment analysis (ssGSEA) using 26 stemness-related gene sets from the StemChecker database to calculate stemness scores for each sample.Subsequently, unsupervised clustering was used to identify stemness-related subtypes in LBC and evaluate prognostic differences between them.Furthermore, we performed weighted gene co-expression network analysis (WGCNA) and LASSO-Cox regression to construct a specific risk score model for LBC patients, which was then external validated in the METABRIC dataset.Results:Based on the analysis of tumor stemness-related gene sets, LBC patients aged over 60 could be further divided into two distinct stemness subtypes: C1 and C2.Among them, the C2 subtype showed a better prognosis ( HR=0.54, 95% CI: 0.30-0.97, P=0.035). Through WGCNA, we successfully identified key gene modules closely associated with stemness subtypes, and subsequently developed a prognostic risk score model for elderly LBC.This model not only accurately predicted survival outcomes of overall elderly LBC patients, but also demonstrated strong prognostic value within molecular subtypes: it effectively distinguished high-and low-risk populations in Luminal A ( HR=0.28, 95% CI: 0.13-0.63, P=0.001) and Luminal B ( HR=0.07, 95% CI: 0.01-0.57, P=0.001) patients. Conclusions:Integrating tumor stemness features can improve the accuracy of prognostic assessment for elderly LBC patients and provide a basis for individualized treatment.The results suggest that the risk score model constructed in this study has potential clinical application value in guiding treatment decisions for this type of elderly LBC patients.
