Qu Jia, Wang Mei-Huan, Gao Yue-Hua, Zhang Hua-Wei
Department of Ultrasound, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.
Cancer Inform. 2025 Feb 3;24:11769351251316398. doi: 10.1177/11769351251316398. eCollection 2025.
The TGF-β signaling pathway is widely acknowledged for its role in various aspects of cancer progression, including cellular invasion, epithelial-mesenchymal transition, and immunosuppression. Immune checkpoint inhibitors (ICIs) and pharmacological agents that target TGF-β offer significant potential as therapeutic options for cancer. However, the specific role of TGF-β in prognostic assessment and treatment strategies for breast cancer (BC) remains unclear.
The Cancer Genome Atlas (TCGA) database was utilized to develop a predictive model incorporating five TGF-β signaling-related genes (TSRGs). The GSE161529 dataset from the Gene Expression Omnibus was employed to conduct single-cell analyses aimed at further elucidating the characteristics of these TSRGs. Additionally, an unsupervised clustering algorithm was applied to categorize BC patients into two distinct groups based on the five TSRGs, with a focus on immune response and overall survival (OS). Further investigations were conducted to explore variations in pharmacotherapy and the tumor microenvironment across different patient cohorts and clusters.
The predictive model for BC identified five TSRGs: FUT8, IFNG, ID3, KLF10, and PARD6A. Single-cell analysis revealed that IFNG is predominantly expressed in CD8+ T cells. Consensus clustering effectively categorized BC patients into two distinct clusters, with cluster B demonstrating a longer OS and a more favorable prognosis. Immunological assessments indicated a higher presence of immune checkpoints and immune cells in cluster B, suggesting a greater likelihood of responsiveness to ICIs.
The findings of this study highlight the potential of the TGF-β signaling pathway for prognostic classification and the development of personalized treatment strategies for BC patients, thereby enhancing our understanding of its significance in BC prognosis.
转化生长因子-β（TGF-β）信号通路在癌症进展的各个方面所起的作用已得到广泛认可，包括细胞侵袭、上皮-间质转化和免疫抑制。免疫检查点抑制剂（ICI）和靶向TGF-β的药物作为癌症治疗选择具有巨大潜力。然而，TGF-β在乳腺癌（BC）预后评估和治疗策略中的具体作用仍不清楚。
利用癌症基因组图谱（TCGA）数据库开发了一个包含五个与TGF-β信号相关基因（TSRG）的预测模型。使用来自基因表达综合数据库的GSE161529数据集进行单细胞分析，以进一步阐明这些TSRG的特征。此外，应用无监督聚类算法根据这五个TSRG将BC患者分为两个不同的组，重点关注免疫反应和总生存期（OS）。进一步研究以探索不同患者队列和聚类中药物治疗及肿瘤微环境的差异。
BC的预测模型确定了五个TSRG：FUT8、IFNG、ID3、KLF10和PARD6A。单细胞分析显示IFNG主要在CD8 + T细胞中表达。一致性聚类有效地将BC患者分为两个不同的聚类，聚类B显示出更长的OS和更有利的预后。免疫学评估表明聚类B中免疫检查点和免疫细胞的存在更高，提示对ICI反应的可能性更大。
本研究结果突出了TGF-β信号通路在BC患者预后分类和个性化治疗策略制定方面的潜力，从而增进了我们对其在BC预后中重要性的理解。
