Department of Breast Surgery, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.
Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
JAMA Netw Open. 2020 Oct 1;3(10):e2014622. doi: 10.1001/jamanetworkopen.2020.14622.
Breast cancer (BC), a common malignant tumor, ranks first among cancers in terms of morbidity and mortality among female patients. Currently, identifying effective prognostic models has a significant association with the prediction of the overall survival of patients with BC and guidance of clinicians in early diagnosis and treatment.
To identify a potential DNA repair-related prognostic signature through a comprehensive evaluation and to further improve the accuracy of prediction of the overall survival of patients with BC.
DESIGN, SETTING, AND PARTICIPANTS: In this prognostic study, conducted from October 9, 2019, to February 3, 2020, the gene expression profiles and clinical data of patients with BC were collected from The Cancer Genome Atlas database. This study consisted of a training set from The Cancer Genome Atlas database and 2 validation cohorts from the Gene Expression Omnibus, which included 1096 patients with BC. A prognostic signature based on 8 DNA repair-related genes (DRGs) was developed to predict overall survival among female patients with BC.
Primary screening prognostic biomarkers were analyzed using univariate Cox proportional hazards regression analysis and the least absolute shrinkage and selection operator Cox proportional hazards regression. A risk model was completely established through multivariate Cox proportional hazards regression analysis. Finally, a prognostic nomogram, combining the DRG signature and clinical characteristics of patients, was constructed. To examine the potential mechanisms of the DRGs, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed.
In this prognostic study based on samples from 1096 women with BC (mean [SD] age, 59.6 [13.1] years), 8 DRGs (MDC1, RPA3, MED17, DDB2, SFPQ, XRCC4, CYP19A1, and PARP3) were identified as prognostic biomarkers. The time-dependent receiver operating characteristic curve analysis suggested that the 8-gene signature had a good predictive accuracy. In the training cohort, the areas under the curve were 0.708 for 3-year survival and 0.704 for 5-year survival. In the validation cohort, the areas under the curve were 0.717 for 3-year survival and 0.772 for 5-year survival in the GSE9893 data set and 0.691 for 3-year survival and 0.718 for 5-year survival in the GSE42568 data set. This DRG signature mainly involved some regulation pathways of vascular endothelial cell proliferation.
In this study, a prognostic signature using 8 DRGs was developed that successfully predicted overall survival among female patients with BC. This risk model provides new clinical evidence for the diagnostic accuracy and targeted treatment of BC.
背景：乳腺癌（BC）是一种常见的恶性肿瘤，在女性患者中发病率和死亡率均位居癌症之首。目前，确定有效的预后模型与预测 BC 患者的总体生存率密切相关，有助于临床医生进行早期诊断和治疗。
目的：通过综合评估确定一个潜在的与 DNA 修复相关的预后签名，进一步提高预测 BC 患者总体生存率的准确性。
设计、设置和参与者：在这项预后研究中，从 2019 年 10 月 9 日至 2020 年 2 月 3 日，从癌症基因组图谱数据库中收集了 BC 患者的基因表达谱和临床数据。该研究包括癌症基因组图谱数据库中的训练集和来自基因表达综合数据库的 2 个验证队列，共包含 1096 例 BC 患者。基于 8 个与 DNA 修复相关的基因（DRGs）建立了一个预后签名，用于预测女性 BC 患者的总体生存率。
主要结局和测量：使用单因素 Cox 比例风险回归分析和最小绝对收缩和选择算子 Cox 比例风险回归分析对初步筛选的预后生物标志物进行分析。通过多因素 Cox 比例风险回归分析完全建立风险模型。最后，构建了一个结合 DRG 特征和患者临床特征的预后列线图。为了研究 DRGs 的潜在机制，进行了基因本体论和京都基因与基因组百科全书富集分析。
结果：在这项基于 1096 名女性 BC 患者样本的预后研究中（平均[标准差]年龄，59.6[13.1]岁），确定了 8 个 DRGs（MDC1、RPA3、MED17、DDB2、SFPQ、XRCC4、CYP19A1 和 PARP3）作为预后生物标志物。时间依赖性接收器工作特征曲线分析表明，该 8 基因签名具有良好的预测准确性。在训练队列中，3 年生存率的曲线下面积为 0.708，5 年生存率的曲线下面积为 0.704。在验证队列中，GSE9893 数据集的 3 年生存率和 5 年生存率的曲线下面积分别为 0.717 和 0.772，GSE42568 数据集的 3 年生存率和 5 年生存率分别为 0.691 和 0.718。该 DRG 签名主要涉及血管内皮细胞增殖的一些调节途径。
结论和相关性：在这项研究中，开发了一个使用 8 个 DRGs 的预后签名，可以成功预测女性 BC 患者的总体生存率。该风险模型为 BC 的诊断准确性和靶向治疗提供了新的临床证据。
