Wu Yuanyuan, Shi Youyang, Luo Zhanyang, Zhou Xiqiu, Chen Yonghao, Song Xiaoyun, Liu Sheng
Department of Breast Surgery, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Shanghai, China.
Front Cell Dev Biol. 2025 Mar 26;13:1570696. doi: 10.3389/fcell.2025.1570696. eCollection 2025.
The tumor boundary of breast cancer represents a highly heterogeneous region. In this area, the interactions between malignant and non-malignant cells influence tumor progression, immune evasion, and drug resistance. However, the spatial transcriptional profile of the tumor boundary and its role in the prognosis and treatment response of breast cancer remain unclear.
Utilizing the Cottrazm algorithm, we reconstructed the intricate boundaries and identified differentially expressed genes (DEGs) associated with these regions. Cell-cell co-positioning analysis was conducted using SpaCET, which revealed key interactions between tumor-associated macrophage (TAMs) and cancer-associated fibroblasts (CAFs). Additionally, Lasso regression analysis was employed to develop a malignant body signature (MBS), which was subsequently validated using the TCGA dataset for prognosis prediction and treatment response assessment.
Our research indicates that the tumor boundary is characterized by a rich reconstruction of the extracellular matrix (ECM), immunomodulatory regulation, and the epithelial-to-mesenchymal transition (EMT), underscoring its significance in tumor progression. Spatial colocalization analysis reveals a significant interaction between CAFs and M2-like tumor-associated macrophage (TAM), which contributes to immune exclusion and drug resistance. The MBS score effectively stratifies patients into high-risk groups, with survival outcomes for patients exhibiting high MBS scores being significantly poorer. Furthermore, drug sensitivity analysis demonstrates that high-MB tumors had poor response to chemotherapy strategies, highlighting the role of the tumor boundary in modulating therapeutic efficacy.
Collectively, we investigate the spatial transcription group and bulk data to elucidate the characteristics of tumor boundary molecules in breast cancer. The CAF-M2 phenotype emerges as a critical determinant of immunosuppression and drug resistance, suggesting that targeting this interaction may improve treatment responses. Furthermore, the MBS serves as a novel prognostic tool and offers potential strategies for guiding personalized treatment approaches in breast cancer.
乳腺癌的肿瘤边界是一个高度异质性的区域。在该区域，恶性细胞与非恶性细胞之间的相互作用影响肿瘤进展、免疫逃逸和耐药性。然而，肿瘤边界的空间转录谱及其在乳腺癌预后和治疗反应中的作用仍不清楚。
利用Cottrazm算法，我们重建了复杂的边界并鉴定了与这些区域相关的差异表达基因（DEGs）。使用SpaCET进行细胞-细胞共定位分析，揭示了肿瘤相关巨噬细胞（TAMs）与癌症相关成纤维细胞（CAFs）之间的关键相互作用。此外，采用套索回归分析来开发恶性体特征（MBS），随后使用TCGA数据集对其进行验证，以预测预后和评估治疗反应。
我们的研究表明，肿瘤边界的特征是细胞外基质（ECM）的丰富重建、免疫调节和上皮-间质转化（EMT），突出了其在肿瘤进展中的重要性。空间共定位分析揭示了CAFs与M2样肿瘤相关巨噬细胞（TAM）之间的显著相互作用，这有助于免疫排斥和耐药性。MBS评分有效地将患者分为高危组，MBS评分高的患者生存结果明显较差。此外，药物敏感性分析表明，高MBS肿瘤对化疗策略反应不佳，突出了肿瘤边界在调节治疗效果中的作用。
总体而言，我们通过研究空间转录组和大量数据来阐明乳腺癌肿瘤边界分子的特征。CAF-M2表型成为免疫抑制和耐药性的关键决定因素，表明靶向这种相互作用可能改善治疗反应。此外，MBS作为一种新的预后工具，为指导乳腺癌个性化治疗方法提供了潜在策略。
