PDF(Pubmed)
Abstract：
UNASSIGNED: Breast carcinoma (BRCA) is a life-threatening malignancy in women and shows a poor prognosis. Cuproptosis is a novel mode of cell death but its relationship with BRCA is unclear. This study attempted to develop a cuproptosis-relevant prognostic gene signature for BRCA.
UNASSIGNED: Cuproptosis-relevant subtypes of BRCA were obtained by consensus clustering. Differential expression analysis was implemented using the \'limma\' package. Univariate Cox and multivariate Cox analyses were performed to determine a cuproptosis-relevant prognostic gene signature. The signature was constructed and validated in distinct datasets. Gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) were also conducted using the prognostic signature to uncover the underlying molecular mechanisms. ESTIMATE and CIBERSORT algorithms were applied to probe the linkage between the gene signature and tumor microenvironment (TME). Immunotherapy responsiveness was assessed using the Tumor Immune Dysfunction and Exclusion (TIDE) web tool. Real-time quantitative PCR (RT-qPCR) was performed to detect the expressions of cuproptosis-relevant prognostic genes in breast cancer cell lines.
UNASSIGNED: Thirty-eight cuproptosis-associated differentially expressed genes (DEGs) in BRCA were mined by consensus clustering and differential expression analysis. Based on univariate Cox and multivariate Cox analyses, six cuproptosis-relevant prognostic genes, namely SAA1, KRT17, VAV3, IGHG1, TFF1, and CLEC3A, were mined to establish a corresponding signature. The signature was validated using external validation sets. GSVA and GSEA showed that multiple cell cycle-linked and immune-related pathways along with biological processes were associated with the signature. The results ESTIMATE and CIBERSORT analyses revealed significantly different TMEs between the two Cusig score subgroups. Finally, RT-qPCR analysis of cell lines further confirmed the expressional trends of SAA1, KRT17, IGHG1, and CLEC3A.
UNASSIGNED: Taken together, we constructed a signature for projecting the overall survival of BRCA patients and our findings authenticated the cuproptosis-relevant prognostic genes, which are expected to provide a basis for developing prognostic molecular biomarkers and an in-depth understanding of the relationship between cuproptosis and BRCA.
UNASSIGNED: Cuproptosis-relevant subtypes of BRCA were obtained by consensus clustering. Differential expression analysis was implemented using the \'limma\' package. Univariate Cox and multivariate Cox analyses were performed to determine a cuproptosis-relevant prognostic gene signature. The signature was constructed and validated in distinct datasets. Gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) were also conducted using the prognostic signature to uncover the underlying molecular mechanisms. ESTIMATE and CIBERSORT algorithms were applied to probe the linkage between the gene signature and tumor microenvironment (TME). Immunotherapy responsiveness was assessed using the Tumor Immune Dysfunction and Exclusion (TIDE) web tool. Real-time quantitative PCR (RT-qPCR) was performed to detect the expressions of cuproptosis-relevant prognostic genes in breast cancer cell lines.
UNASSIGNED: Thirty-eight cuproptosis-associated differentially expressed genes (DEGs) in BRCA were mined by consensus clustering and differential expression analysis. Based on univariate Cox and multivariate Cox analyses, six cuproptosis-relevant prognostic genes, namely SAA1, KRT17, VAV3, IGHG1, TFF1, and CLEC3A, were mined to establish a corresponding signature. The signature was validated using external validation sets. GSVA and GSEA showed that multiple cell cycle-linked and immune-related pathways along with biological processes were associated with the signature. The results ESTIMATE and CIBERSORT analyses revealed significantly different TMEs between the two Cusig score subgroups. Finally, RT-qPCR analysis of cell lines further confirmed the expressional trends of SAA1, KRT17, IGHG1, and CLEC3A.
UNASSIGNED: Taken together, we constructed a signature for projecting the overall survival of BRCA patients and our findings authenticated the cuproptosis-relevant prognostic genes, which are expected to provide a basis for developing prognostic molecular biomarkers and an in-depth understanding of the relationship between cuproptosis and BRCA.
摘要：
■乳腺癌（BRCA）是一种危及女性生命的恶性肿瘤，预后不良。角化是一种新的细胞死亡模式，但其与BRCA的关系尚不清楚。这项研究试图开发BRCA的角化相关预后基因标签。
■通过共识聚类获得BRCA的角化相关亚型。差异表达分析使用\'limma\'包实施。进行单变量Cox和多变量Cox分析以确定角化相关的预后基因签名。在不同的数据集中构建和验证签名。还使用预后标签进行基因集变异分析（GSVA）和基因集富集分析（GSEA），以揭示潜在的分子机制。应用ESTIMATE和CIBERSORT算法来探测基因标签和肿瘤微环境（TME）之间的联系。使用肿瘤免疫功能障碍和排除（TIDE）网络工具评估免疫治疗反应性。采用实时定量PCR(RT-qPCR)技术检测乳腺癌细胞株中与角化相关的预后基因的表达。
■通过共识聚类和差异表达分析，挖掘了BRCA中38个与角化相关的差异表达基因（DEGs）。基于单变量Cox和多变量Cox分析，六个与角化相关的预后基因，即SAA1、KRT17、VAV3、IGHG1、TFF1和CLEC3A，被挖掘以建立相应的签名。已使用外部验证集验证签名。GSVA和GSEA表明，多个细胞周期相关和免疫相关途径以及生物学过程与签名相关。ESTIMATE和CIBERSORT分析的结果显示，两个Cumsig评分亚组之间的TME存在显着差异。最后,细胞系的RT-qPCR分析进一步证实了SAA1、KRT17、IGHG1和CLEC3A的表达趋势。
■放在一起，我们构建了一个标记来预测BRCA患者的总体生存率，并且我们的发现验证了与角化相关的预后基因，这有望为开发预后性分子生物标志物和深入了解杯突与BRCA之间的关系提供基础。
■通过共识聚类获得BRCA的角化相关亚型。差异表达分析使用\'limma\'包实施。进行单变量Cox和多变量Cox分析以确定角化相关的预后基因签名。在不同的数据集中构建和验证签名。还使用预后标签进行基因集变异分析（GSVA）和基因集富集分析（GSEA），以揭示潜在的分子机制。应用ESTIMATE和CIBERSORT算法来探测基因标签和肿瘤微环境（TME）之间的联系。使用肿瘤免疫功能障碍和排除（TIDE）网络工具评估免疫治疗反应性。采用实时定量PCR(RT-qPCR)技术检测乳腺癌细胞株中与角化相关的预后基因的表达。
■通过共识聚类和差异表达分析，挖掘了BRCA中38个与角化相关的差异表达基因（DEGs）。基于单变量Cox和多变量Cox分析，六个与角化相关的预后基因，即SAA1、KRT17、VAV3、IGHG1、TFF1和CLEC3A，被挖掘以建立相应的签名。已使用外部验证集验证签名。GSVA和GSEA表明，多个细胞周期相关和免疫相关途径以及生物学过程与签名相关。ESTIMATE和CIBERSORT分析的结果显示，两个Cumsig评分亚组之间的TME存在显着差异。最后,细胞系的RT-qPCR分析进一步证实了SAA1、KRT17、IGHG1和CLEC3A的表达趋势。
■放在一起，我们构建了一个标记来预测BRCA患者的总体生存率，并且我们的发现验证了与角化相关的预后基因，这有望为开发预后性分子生物标志物和深入了解杯突与BRCA之间的关系提供基础。
