Department of Breast and Thyroid Surgery, The First Affiliated Hospital of Chongqing Medical University, 400016 Chongqing, China.
Department of Hepatobiliary surgery, The First Affiliated Hospital of Chongqing Medical University, 400016 Chongqing, China.
Front Biosci (Landmark Ed). 2024 Jun 27;29(7):239. doi: 10.31083/j.fbl2907239.
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.
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.
A survival risk model comprising eight prognostically relevant apoptotic genes (, , , , , , , ) 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.
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.
乳腺癌（BC）是全球女性中最常见的恶性肿瘤，细胞凋亡在其病理进展中起着关键作用。尽管细胞凋亡在 BC 的发展中起着至关重要的作用，但对于 BC 预后与细胞凋亡相关基因（ARGs）之间的关系的研究还很有限。因此，本研究旨在建立一个以细胞凋亡相关因素为中心的 BC 特异性风险模型，为预测 BC 患者的预后和免疫反应提供一种新方法。
利用来自癌症基因图谱（TCGA）的数据，采用 Cox 回归分析鉴定差异预后的 ARGs 并构建预后模型。使用独立数据集、接收者操作特征（ROC）曲线和列线图评估模型的准确性和临床相关性及其预测免疫治疗结果的效果。此外，京都基因与基因组百科全书（KEGG）和基因本体论（GO）分析用于预测潜在的机械途径。使用 CellMiner 数据库评估模型基因的药物敏感性。
基于 TCGA 中 BC 患者样本，建立了一个由 8 个与预后相关的凋亡基因（、、、、、、、）组成的生存风险模型。校准曲线验证了 ROC 曲线和列线图，表明具有良好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，低风险组（LRG）表现出增强的免疫细胞浸润和改善的免疫治疗反应。模型基因还与对多种药物的敏感性呈正相关，包括vemurafenib、dabrafenib、PD-98059 和 palbociclib。
本研究成功地建立和验证了基于 ARGs 的预后模型，为 BC 患者的预后和免疫反应预测提供了新的见解。这些发现有望成为该领域未来研究的有价值的参考。
