Fang Dalang, Zhou Yu, Liao Fengqing, Lu Bimin, Li Yanghong, Lv Mian, Luo Zhizhai, Ma Yanfei
Department of gland surgery, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, Guangxi, China.
Key laboratory of tumor molecular pathology of Baise, Baise, Guangxi, China.
Discov Oncol. 2025 Feb 13;16(1):171. doi: 10.1007/s12672-025-01952-2.
Cuproptosis, a newly suggested mechanism of controlled cellular demise, which has been extensively associated with aspects of occurrence and development in breast cancer. The aim of this study was to conduct a comprehensive multi-group bioinformatics analysis based on the expression of cuproptosis-related genes (CRGs) to identify novel breast cancer subtypes to guide clinical practice. We collected TCGA-BRCA and GSE42568 datasets to investigate the expression patterns of CRGs in breast cancer. Consensus cluster analysis was performed to identify distinct subtypes. Subsequently, an investigation was carried out to examine the disparities between CRGclusters through functional enrichment analysis. Finally, we examined microsatellite instability, tumor mutation burden, drug sensitivity, infiltration of immune cells and cancer cell stemness across different CRGclusters. We identified two subtypes, where CRGcluster S2 exhibits a poorer prognosis compared to CRGcluster S1. Moreover, CRGcluster S2 demonstrated lower immune infiltration scores, higher cancer cell stemness index, and increased tumor mutation burden relative to CRGcluster S1, with the most frequently mutated gene being ATP7A. Notably, breast cancer chemotherapy drugs such as docetaxel, doxorubicin, and paclitaxel exhibited reduced sensitivity towards CRGcluster S2 when compared to CRGcluster S1. We have identified two CRGclusters in breast cancer that could serve as potential therapeutic targets and warrant further investigation in clinical trial studies for breast cancer.
铜死亡是一种新提出的细胞程序性死亡机制，与乳腺癌的发生发展密切相关。本研究旨在基于铜死亡相关基因（CRGs）的表达进行全面的多组学生物信息学分析，以识别新的乳腺癌亚型，为临床实践提供指导。我们收集了TCGA-BRCA和GSE42568数据集，以研究CRGs在乳腺癌中的表达模式。进行共识聚类分析以识别不同的亚型。随后，通过功能富集分析研究CRG簇之间的差异。最后，我们检测了不同CRG簇之间的微卫星不稳定性、肿瘤突变负荷、药物敏感性、免疫细胞浸润和癌细胞干性。我们识别出两个亚型，其中CRG簇S2的预后比CRG簇S1差。此外，与CRG簇S1相比，CRG簇S2的免疫浸润评分较低，癌细胞干性指数较高，肿瘤突变负荷增加，最常突变的基因是ATP7A。值得注意的是，与CRG簇S1相比，多西他赛、阿霉素和紫杉醇等乳腺癌化疗药物对CRG簇S2的敏感性降低。我们在乳腺癌中识别出两个CRG簇，它们可作为潜在的治疗靶点，值得在乳腺癌临床试验研究中进一步研究。
