MAZ-mediated tumor progression and immune evasion in hormone receptor-positive breast cancer: Targeting tumor microenvironment and PCLAF plus subtype-specific therapy

被引:2
作者
Ni, Gaofeng [1 ]
Sun, Yuwei [2 ]
Jia, Hongling [2 ]
Xiahou, Zhikai [3 ]
Li, Yumeng [2 ]
Zhao, Fu [2 ]
Zang, Hongyan [1 ]
机构
[1] Binzhou Med Univ, Yantaishan Hosp, Dept Breast Surg, Yantai 264003, Peoples R China
[2] Shandong Univ Tradit Chinese Med, Clin Med Coll 1, Jinan 250014, Peoples R China
[3] Beijing Sport Univ, China Inst Sport & Hlth Sci, Beijing 100084, Peoples R China
来源
TRANSLATIONAL ONCOLOGY | 2025年 / 52卷
关键词
Single-cell RNA sequencing; Hormone receptor-positive breast cancer; Tumor microenvironment; Prognosis; Immunotherapy; ENDOCRINE THERAPY; CELLS; PROLIFERATION; INVASION; PURINE; ARREST; WOMEN; G(1);
D O I
10.1016/j.tranon.2025.102280
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Background: Breast cancer had been the most frequently diagnosed cancer among women, making up nearly onethird of all female cancers. Hormone receptor-positive breast cancer (HR+BC) was the most prevalent subtype of breast cancer and exhibited significant heterogeneity. Despite advancements in endocrine therapies, patients with advanced HR+BC often faced poor outcomes due to the development of resistance to treatment. Understanding the molecular mechanisms behind this resistance, including tumor heterogeneity and changes in the tumor microenvironment, was crucial for overcoming resistance, identifying new therapeutic targets, and developing more effective personalized treatments. Methods: The study utilized single-cell RNA sequencing (scRNA-seq) data sourced from the Gene Expression Omnibus database and The Cancer Genome Atlas to analyze HR+BC and identify key cellular characteristics. Cell type identification was achieved through Seurat's analytical tools, and subtype differentiation trajectories were inferred using Slingshot. Cellular communication dynamics between tumor cell subtypes and other cells were analyzed with the CellChat. The pySCENIC package was utilized to analyze transcription factors regulatory networks in the identified tumor cell subtypes. The results were verified by in vitro experiments. A risk scoring model was developed to assess patient outcomes. Results: This study employed scRNA-seq to conduct a comprehensive analysis of HR+BC tumor subtypes, identifying the C3 PCLAF+ tumor cells subtype, which demonstrated high proliferation and differentiation potential. C3 PCLAF+ tumor cells subtype was found to be closely associated with cancer-associated fibroblasts through the MK signaling pathway, facilitating tumor progression. Additionally, we discovered that MAZ was significantly expressed in C3 PCLAF+ tumor cells subtype, and in vitro experiments confirmed that MAZ knockdown inhibited tumor growth, accentuating its underlying ability as a therapeutic target. Furthermore, we developed a novel prognostic model based on the expression profile of key prognostic genes within the PCLAF +/MAZ regulatory network. This model linked high PCLAF+ tumor risk scores with poor survival outcomes and specific immune microenvironment characteristics. Conclusion: This study utilized scRNA-seq to reveal the role of the C3 PCLAF+ tumor cells subtype in HR+BC, emphasizing its association with poor prognosis and resistance to endocrine therapies. MAZ, identified as a key regulator, contributed to tumor progression, while the tumor microenvironment had a pivotal identity in immune evasion. The findings underscored the importance of overcoming drug resistance, recognizing novel treatment targets, and crafting tailored diagnosis regimens.
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页数:22
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