Ma Linxiaoxi, Qian Bei, Peng Chen, Liu Gang, Shen Hao
Department of Breast Surgery, Department of Oncology, Fudan University Shanghai Cancer CenterandKey Laboratory of Breast Cancer in ShanghaiShanghai Medical College, Fudan University, Shanghai, China.
Department of Thyroid and Breast Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, 430022, China.
BMC Med Genomics. 2025 Jul 31;18(1):123. doi: 10.1186/s12920-025-02194-5.
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.
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.
Patients in the high-risk group had worse survival outcomes than those in the low-risk group. The five-year overall survival AUC of the model was 0.858, indicating good performance. High-risk patients were characterized by USH2A and KMT2C mutations, genomic amplification, and enriched JAK-STAT pathway and cytokine-cytokine receptor interaction pathways. Resting CD4 + memory T cells, activated mast cells, and myeloid dendritic cells were significantly enriched in the low-risk group, while M0 macrophages were enriched in the high-risk group. Single-cell sequencing analyses also revealed that the model was significantly associated with macrophages and the percentage of proliferating myeloid cells. The signature was also associated with sensitivity to multiple drugs. Cell-cell interaction difference analyses suggested that cancer-related signaling pathways, especially the SIRPα/CD47/IL6 pathway, were decreased in high-risk patients, but these samples exhibited increased SPP1 interactions.
The signature captures lipid metabolic reprogramming and immunosuppression, providing a biomarker for prognosis and precision therapy in tamoxifen-treated ER + breast cancer.
尽管接受他莫昔芬治疗的雌激素受体阳性（ER+）乳腺癌患者术后临床结局良好，但仍有一部分患者因疾病进展而复发或死亡。
在本研究中，通过整合脂质代谢基因表达和机器学习数据，利用TCGA-ER+BRCA数据集（N = 183）开发了一种基于基因表达的预后模型，并在GSE17705（N = 298）、GSE22219（N = 134）、GSE42568（N = 70）和GSE58644（N = 147）数据集上进行了验证。根据特征的中位风险评分将患者分为高风险组和低风险组。对这些组之间的生存、基因组特征、免疫浸润和药物敏感性进行了比较分析。
高风险组患者的生存结局比低风险组患者更差。该模型的五年总生存曲线下面积（AUC）为0.858，表明性能良好。高风险患者的特征是USH2A和KMT2C突变、基因组扩增以及JAK-STAT通路和细胞因子-细胞因子受体相互作用通路富集。静息CD4+记忆T细胞、活化肥大细胞和髓样树突状细胞在低风险组中显著富集，而M0巨噬细胞在高风险组中富集。单细胞测序分析还显示，该模型与巨噬细胞和增殖性髓样细胞百分比显著相关。该特征还与对多种药物的敏感性相关。细胞-细胞相互作用差异分析表明，高风险患者中癌症相关信号通路，尤其是SIRPα/CD47/IL6通路减少，但这些样本中SPP1相互作用增加。
该特征捕获了脂质代谢重编程和免疫抑制，为他莫昔芬治疗的ER+乳腺癌的预后和精准治疗提供了一个生物标志物。
