Transcription Factor Profiling to Predict Recurrence-Free Survival in Breast Cancer: Development and Validation of a Nomogram to Optimize Clinical Management
Transcription Factor Profiling to Predict Recurrence-Free Survival in Breast Cancer: Development and Validation of a Nomogram to Optimize Clinical Management
复制标题
转录因子分析可预测乳腺癌无复发生存期：开发和验证列线图以优化临床管理
DOI：
10.3389/fgene.2020.00333
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发表时间：
2020-04-24
影响因子：
3.7
通讯作者：
Huang, Tao
中科院分区：
文献类型：
--
作者：
Chen, Hengyu;Ma, Xianxiong;Huang, Tao
乳腺癌（BC）是最常见的癌症，也是年轻女性癌症相关死亡的主要原因。已经报道了BC的几种预后和预测转录因子（TF）标志物;然而，由于数据集小、BC的异质性和数据预处理方法的变化，它们是不一致的。本研究旨在确定一个有效的预测TF签名与BC患者的预后。我们分析了癌症基因组图谱（TCGA）数据库中868例BC患者的TF数据，以研究与无复发生存期（RFS）相关的TF生物标志物。这些患者被分为训练和内部验证数据集，GSE 2034和GSE 42568用作外部验证集。在训练数据集中，通过单变量考克斯比例风险分析、最小绝对收缩和选择算子（LASSO）考克斯回归分析和多变量考克斯比例风险分析，将9-TF特征确定为与BC患者的RFS关键相关。Kaplan-Meier分析显示，9-TF特征可以在内部验证数据集和两个外部验证数据集中显著区分高风险和低风险患者。受试者工作特征（ROC）分析进一步验证了9-TF特征对BC患者RFS的预测性能良好。此外，我们根据风险评分和淋巴结状态开发了诺模图，并进行了C指数、ROC和校准图分析，表明它显示出良好的性能和临床价值。总之，我们使用综合生物信息学方法来鉴定有效的预测性九-TF特征，其可能是BC预后的潜在生物标志物。
Breast cancer (BC) is the most frequently diagnosed cancer and the leading cause of cancer-related death in young women. Several prognostic and predictive transcription factor (TF) markers have been reported for BC; however, they are inconsistent due to small datasets, the heterogeneity of BC, and variation in data pre-processing approaches. This study aimed to identify an effective predictive TF signature for the prognosis of patients with BC. We analyzed the TF data of 868 patients with BC in The Cancer Genome Atlas (TCGA) database to investigate TF biomarkers relevant to recurrence-free survival (RFS). These patients were separated into training and internal validation datasets, with GSE2034 and GSE42568 used as external validation sets. A nine-TF signature was identified as crucially related to the RFS of patients with BC by univariate Cox proportional hazard analysis, least absolute shrinkage and selection operator (LASSO) Cox regression analysis, and multivariate Cox proportional hazard analysis in the training dataset. Kaplan–Meier analysis revealed that the nine-TF signature could significantly distinguish high- and low-risk patients in both the internal validation dataset and the two external validation sets. Receiver operating characteristic (ROC) analysis further verified that the nine-TF signature showed a good performance for predicting the RFS of patients with BC. In addition, we developed a nomogram based on risk score and lymph node status, with C-index, ROC, and calibration plot analysis, suggesting that it displays good performance and clinical value. In summary, we used integrated bioinformatics approaches to identify an effective predictive nine-TF signature which may be a potential biomarker for BC prognosis.