Shang Guanmin, Xu Zhiwen, Peng Dengfu, Liao Xinghe, Zhao Jiangang
Department of Oncology, Shaoxing Central Hospital, Shaoxing, 312030, China.
Department of Oncology, The Central Affiliated Hospital, Shaoxing University, Shaoxing, 312030, China.
Sci Rep. 2025 Aug 5;15(1):28643. doi: 10.1038/s41598-025-14621-9.
Explore the relationship between breast invasive carcinoma (BRCA) and cuproptosis-related genes (CRGs). CRGs related to prognosis were calculated using Lasso analysis and multivariate Cox analysis based on BRCA data from the TCGA, CRG signatures were then generated to categorize patients based on their risk scores into high-risk and low-risk categories. The GEO dataset was used as external validation. A nomogram was constructed in order to further predict the survival of patients. We also examined differences in the infiltrative status of immune cell subsets present between the high-risk and low-risk categories. The prognostic gene expression were validated utilizing real-time quantitative PCR (RT-qPCR). We identified nine CRGs associated with survival and built a risk model to separate patients into high - and low-risk groups with distinct differences in survival time. Risk model performance was confirmed by the ROC curve and nomogram. Additionally, we found a significant difference between the two patient groups in the extent of immune cell infiltration. qRT-PCR analysis revealed differential expression of seven CRGs (AK7, CEL, GRIA3, KCNE2, NT5C1A, PGK1, NOS1) across various breast cancer cell lines compared to MCF-10 A cells, showing both positive and negative regulation in different cell lines. These results may help illuminate the functions that CRGs perform in BRCA, which might improve our knowledge of cuproptosis and facilitate the implementation of more successful immunotherapy techniques.
探索乳腺浸润性癌(BRCA)与铜死亡相关基因(CRGs)之间的关系。基于来自TCGA的BRCA数据,使用Lasso分析和多变量Cox分析计算与预后相关的CRGs,然后生成CRG特征,根据患者的风险评分将其分为高风险和低风险类别。使用GEO数据集进行外部验证。构建列线图以进一步预测患者的生存率。我们还检查了高风险和低风险类别之间存在的免疫细胞亚群浸润状态的差异。利用实时定量PCR(RT-qPCR)验证预后基因表达。我们鉴定出九个与生存相关的CRGs,并建立了一个风险模型,将患者分为生存时间有明显差异的高风险和低风险组。通过ROC曲线和列线图确认风险模型的性能。此外,我们发现两组患者在免疫细胞浸润程度上存在显著差异。qRT-PCR分析显示,与MCF-10 A细胞相比,七种CRGs(AK7、CEL、GRIA3、KCNE2、NTPC1A、PGK1、NOS1)在各种乳腺癌细胞系中的表达存在差异,在不同细胞系中表现出正负调节。这些结果可能有助于阐明CRGs在BRCA中发挥的功能,这可能会增进我们对铜死亡的了解,并促进更成功的免疫治疗技术的应用。