Yan Penghui, Sun Hanlin, Wang Siqiao, Huang Runzhi, Shi Chaofeng, Yang Qihang, Qiao Yibo, Wang Haonan, Kong Deqian, Zhu Jiwen, Yang Yunqing, Huang Zongqiang
Department of Orthopedics, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Department of Burn Surgery, The First Affiliated Hospital of Naval Medical University, Shanghai, China.
J Cell Mol Med. 2025 Aug;29(15):e70680. doi: 10.1111/jcmm.70680.
Breast cancer (BRCA) is characterised by complex cellular heterogeneity and differentiation hierarchies, which play a crucial role in bone metastasis and therapeutic resistance. However, existing classification systems remain inadequate in capturing these complexities, limiting their effectiveness in guiding treatment strategies. To address this gap, we integrated single-cell RNA-seq profiles, spatial transcriptomes, along with 1097 bulk RNA-seq profiles of TCGA-BRCA cohort to dissect the molecular landscape of BRCA. By performing UMAP analysis, we identified nine tumour clusters and three spatially distinct spot types (immune, stromal and malignant spots) and further delineated 11 differentiation states from 2493 malignant spots. Through clustering, monocle 2 pseudo-time and prognostic analyses, we identified the prognostic BRCA cell fate-related genes, then constructed a novel BRCA stratification system (four subtypes) with differential prognosis, biological plausibility and clinical significance. Also, least absolute shrinkage and selection operator (LASSO) regression analysis was performed for the BRCA cell fate-related genes in constructing a prognostic model. The model has modest accuracy and accordance (AUC = 0.708), which could distinguish BRCA patients into high-risk or low groups. With correlation analysis, regulation networks were constructed for different subtypes based on the key cell fate-related genes, transcription factors, metastasis-related pathways, immune components and so on, to investigate the regulatory relationships between primary BRCA and BRCA bone metastasis. Afterwards, we identified the most significant inhibitors (puromycin, MS-275, megestrol, aesculetin) for bone metastatic BRCA, which might have potential translational significance. In all, we developed a novel molecular stratification system for BRCA based on the cell fate-related markers of malignant cells, which offered strong translational potential for diagnosis, prognosis and personalised therapeutic interventions.
乳腺癌（BRCA）具有复杂的细胞异质性和分化层次结构，这在骨转移和治疗耐药性中起着关键作用。然而，现有的分类系统在捕捉这些复杂性方面仍然不足，限制了它们在指导治疗策略方面的有效性。为了填补这一空白，我们整合了单细胞RNA测序图谱、空间转录组以及TCGA-BRCA队列的1097份批量RNA测序图谱，以剖析BRCA的分子景观。通过进行UMAP分析，我们识别出九个肿瘤簇和三种空间上不同的斑点类型（免疫、基质和恶性斑点），并进一步从2493个恶性斑点中描绘出11种分化状态。通过聚类、monocle 2伪时间分析和预后分析，我们确定了与BRCA细胞命运相关的预后基因，然后构建了一个具有不同预后、生物学合理性和临床意义的新型BRCA分层系统（四个亚型）。此外，在构建预后模型时，对与BRCA细胞命运相关的基因进行了最小绝对收缩和选择算子（LASSO）回归分析。该模型具有适度的准确性和一致性（AUC = 0.708），可以将BRCA患者分为高危或低危组。通过相关性分析，基于关键的细胞命运相关基因、转录因子、转移相关途径、免疫成分等构建了不同亚型的调控网络，以研究原发性BRCA与BRCA骨转移之间的调控关系。之后，我们确定了骨转移性BRCA最有效的抑制剂（嘌呤霉素、MS-275、甲地孕酮、七叶亭），这可能具有潜在的转化意义。总之，我们基于恶性细胞的细胞命运相关标志物开发了一种新型的BRCA分子分层系统，为诊断、预后和个性化治疗干预提供了强大的转化潜力。
