Abstract：
BACKGROUND: Breast cancer (BC) ranks as the most prevalent malignancy affecting women globally, with apoptosis playing a pivotal role in its pathological progression. Despite the crucial role of apoptosis in BC development, there is limited research exploring the relationship between BC prognosis and apoptosis-related genes (ARGs). Therefore, this study aimed to establish a BC-specific risk model centered on apoptosis-related factors, presenting a novel approach for predicting prognosis and immune responses in BC patients.
METHODS: Utilizing data from The Cancer Gene Atlas (TCGA), Cox regression analysis was employed to identify differentially prognostic ARGs and construct prognostic models. The accuracy and clinical relevance of the model, along with its efficacy in predicting immunotherapy outcomes, were evaluated using independent datasets, Receiver Operator Characteristic (ROC) curves, and nomogram. Additionally, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses were used to predict potential mechanical pathways. The CellMiner database is used to assess drug sensitivity of model genes.
RESULTS: A survival risk model comprising eight prognostically relevant apoptotic genes (PMAIP1, TP53AIP1, TUBA3D, TUBA1C, BCL2A1, EMP1, GSN, F2) was established based on BC patient samples from TCGA. Calibration curves validated the ROC curve and nomogram, demonstrating excellent accuracy and clinical utility. In samples from the Gene Expression Omnibus (GEO) datasets and immunotherapy groups, the low-risk group (LRG) demonstrated enhanced immune cell infiltration and improved immunotherapy responses. Model genes also displayed positive associations with sensitivity to multiple drugs, including vemurafenib, dabrafenib, PD-98059, and palbociclib.
CONCLUSIONS: This study successfully developed and validated a prognostic model based on ARGs, offering new insights into prognosis and immune response prediction in BC patients. These findings hold promise as valuable references for future research endeavors in this field.
METHODS: Utilizing data from The Cancer Gene Atlas (TCGA), Cox regression analysis was employed to identify differentially prognostic ARGs and construct prognostic models. The accuracy and clinical relevance of the model, along with its efficacy in predicting immunotherapy outcomes, were evaluated using independent datasets, Receiver Operator Characteristic (ROC) curves, and nomogram. Additionally, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses were used to predict potential mechanical pathways. The CellMiner database is used to assess drug sensitivity of model genes.
RESULTS: A survival risk model comprising eight prognostically relevant apoptotic genes (PMAIP1, TP53AIP1, TUBA3D, TUBA1C, BCL2A1, EMP1, GSN, F2) was established based on BC patient samples from TCGA. Calibration curves validated the ROC curve and nomogram, demonstrating excellent accuracy and clinical utility. In samples from the Gene Expression Omnibus (GEO) datasets and immunotherapy groups, the low-risk group (LRG) demonstrated enhanced immune cell infiltration and improved immunotherapy responses. Model genes also displayed positive associations with sensitivity to multiple drugs, including vemurafenib, dabrafenib, PD-98059, and palbociclib.
CONCLUSIONS: This study successfully developed and validated a prognostic model based on ARGs, offering new insights into prognosis and immune response prediction in BC patients. These findings hold promise as valuable references for future research endeavors in this field.
摘要：
背景：乳腺癌（BC）是影响全球女性最普遍的恶性肿瘤，细胞凋亡在其病理进程中起着关键作用。尽管细胞凋亡在BC发育中起着至关重要的作用，关于BC预后与凋亡相关基因（ARGs）之间关系的研究有限。因此,本研究旨在建立以凋亡相关因子为中心的BC特异性风险模型，提出了一种预测BC患者预后和免疫反应的新方法。
方法：利用癌症基因图谱(TCGA)的数据，Cox回归分析用于识别差异预后ARGs并构建预后模型。模型的准确性和临床相关性，连同其在预测免疫治疗结果方面的功效，使用独立的数据集进行评估，接收器操作员特征(ROC)曲线,和列线图。此外,京都基因和基因组百科全书（KEGG）和基因本体论（GO）分析用于预测潜在的机械途径。CellMiner数据库用于评估模型基因的药物敏感性。
结果：生存风险模型包含8个预后相关的凋亡基因（PMAIP1，TP53AIP1，TUBA3D，TUBA1C,BCL2A1,EMP1,GSN,F2)基于来自TCGA的BC患者样品建立。校准曲线验证了ROC曲线和列线图，展示了出色的准确性和临床实用性。在来自基因表达综合(GEO)数据集和免疫治疗组的样本中，低危组(LRG)表现出增强的免疫细胞浸润和改善的免疫治疗反应.模型基因也显示与多种药物的敏感性正相关，包括Vemurafenib,Dabrafenib,PD-98059和palbociclib。
结论：本研究成功开发并验证了基于ARGs的预后模型，为BC患者的预后和免疫反应预测提供新的见解。这些发现有望为该领域未来的研究工作提供有价值的参考。
方法：利用癌症基因图谱(TCGA)的数据，Cox回归分析用于识别差异预后ARGs并构建预后模型。模型的准确性和临床相关性，连同其在预测免疫治疗结果方面的功效，使用独立的数据集进行评估，接收器操作员特征(ROC)曲线,和列线图。此外,京都基因和基因组百科全书（KEGG）和基因本体论（GO）分析用于预测潜在的机械途径。CellMiner数据库用于评估模型基因的药物敏感性。
结果：生存风险模型包含8个预后相关的凋亡基因（PMAIP1，TP53AIP1，TUBA3D，TUBA1C,BCL2A1,EMP1,GSN,F2)基于来自TCGA的BC患者样品建立。校准曲线验证了ROC曲线和列线图，展示了出色的准确性和临床实用性。在来自基因表达综合(GEO)数据集和免疫治疗组的样本中，低危组(LRG)表现出增强的免疫细胞浸润和改善的免疫治疗反应.模型基因也显示与多种药物的敏感性正相关，包括Vemurafenib,Dabrafenib,PD-98059和palbociclib。
结论：本研究成功开发并验证了基于ARGs的预后模型，为BC患者的预后和免疫反应预测提供新的见解。这些发现有望为该领域未来的研究工作提供有价值的参考。
