Department of Breast Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Beijing Tian Tan Hospital, Capital Medical University, Beijing, China.
Medicine (Baltimore). 2023 Jun 16;102(24):e34018. doi: 10.1097/MD.0000000000034018.
The objective of this study is to assess the predictive potency of cell senescence-related genes (CSRGs) in breast cancer (BC) and establish a risk signature. Trascriptome data of CSRGs were obtained from the TCGA and GEO databases. Consensus clustering was used to generate CSRGs-based molecular clusters for BC patients. A CSRGs-derived risk signature was built using multiple Cox regression analyses of differentially expressed genes (DEGs) between clusters. The prognosis, immune infiltration, chemotherapy and immunotherapy response between different risk groups were analyzed and compared. Two molecular clusters of BC patients were generated on the basis of 79 differentially expressed CSRGs, which showed distinct prognosis and immune infiltration. A total of 1403 DEGs between the CSRGs-derived clusters were found, and 10 of them were independent prognostic genes that used to construct a risk signature. The results demonstrated that patients with older age and advanced stage presented with a higher risk scores. In addition, the risk signature was found to be associated with outcomes, immune infiltration, chemotherapy and immunotherapy response. Patients in the low-risk group showed a favorable prognosis and higher immunotherapy response than those in the high-risk group. Finally, we developed a highly stable nomogram that incorporates risk signature, chemotherapy, radiotherapy, and stage variables, enabling accurate prediction of the overall survival (OS) of individual patients. To conclude, the signature derived from CSRGs holds great promise as a biomarker for prognostic assessment of BC and may serve as a valuable tool in guiding immunotherapy.
本研究旨在评估细胞衰老相关基因（CSRGs）在乳腺癌（BC）中的预测能力，并建立风险特征。从 TCGA 和 GEO 数据库中获取 CSRGs 的转录组数据。使用共识聚类方法对 BC 患者的 CSRGs 进行分子聚类。通过对不同聚类之间差异表达基因（DEGs）的多 Cox 回归分析，构建 CSRGs 衍生的风险特征。分析并比较不同风险组之间的预后、免疫浸润、化疗和免疫治疗反应。基于 79 个差异表达 CSRGs 生成了两种 BC 患者的分子聚类，它们显示出明显不同的预后和免疫浸润。在 CSRGs 衍生的聚类之间发现了 1403 个 DEGs，其中 10 个是独立的预后基因，用于构建风险特征。结果表明，年龄较大和晚期的患者具有更高的风险评分。此外，该风险特征与结局、免疫浸润、化疗和免疫治疗反应相关。低风险组的患者预后较好，免疫治疗反应较高。最后，我们开发了一个高度稳定的列线图，其中包含风险特征、化疗、放疗和分期变量，能够准确预测个体患者的总生存期（OS）。总之，CSRGs 衍生的特征有望成为 BC 预后评估的生物标志物，并可能成为指导免疫治疗的有价值工具。
