Han Yuying, Zheng Qiyu, Tian Ye, Ji Zhengguo, Ye Haihong
Department of Medical Genetics and Developmental Biology, School of Basic Medical Sciences, Beijing Key Laboratory of Neural Regeneration and Repair, Capital Medical University, Beijing, China.
Department of Urology, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
J Surg Oncol. 2019 Jun;119(8):1145-1154. doi: 10.1002/jso.25446. Epub 2019 Mar 18.
Bladder cancer is one of the most common and highly recurrent cancers worldwide. Recurrence-associated genes may potentially predict cancer recurrence. We aimed to construct a recurrence-associated gene panel to improve the prognostic prediction of bladder cancer.
Based on DNA sequencing and clinical data from the TCGA-BLCA project, we identified 10 potential driver genes significantly associated with recurrence of bladder cancer. We performed multivariable logistic regression analysis to construct an optimized recurrence prediction model with nine recurrence-associated genes (EME1, AKAP9, ZNF91, PARD3, STAG2, ZFP36L2, METTL3, POLR3B, and MUC7) and clinical information as the independent variables.
The area under the receiver operating characteristic (ROC) curve was 0.80 in this model, much higher than that of the baseline model (AUC = 0.73) and the same trend was also validated in its subset. Decision curve analysis also revealed that there is a significant net benefit gained by adding nine genes mutation to the baseline model. Furthermore, Kaplan-Meier survival analysis showed that eight out of the nine genes (excluding MUC7) had good effects on the overall prognosis of patients.
This nine-gene panel will most likely be a useful tool for prognostic evaluation and will facilitate the personalized management of patients with bladder cancer.
膀胱癌是全球最常见且复发率很高的癌症之一。复发相关基因可能具有预测癌症复发的潜力。我们旨在构建一个复发相关基因panel以改善膀胱癌的预后预测。
基于TCGA-BLCA项目的DNA测序和临床数据，我们鉴定出10个与膀胱癌复发显著相关的潜在驱动基因。我们进行多变量逻辑回归分析，以9个复发相关基因（EME1、AKAP9、ZNF91、PARD3、STAG2、ZFP36L2、METTL3、POLR3B和MUC7）和临床信息作为自变量构建一个优化的复发预测模型。
该模型的受试者工作特征（ROC）曲线下面积为0.80，远高于基线模型（AUC = 0.73），并且在其亚组中也验证了相同趋势。决策曲线分析还表明，在基线模型中添加9个基因突变可获得显著的净效益。此外，Kaplan-Meier生存分析表明，9个基因中的8个（不包括MUC7）对患者的总体预后有良好影响。
这个9基因panel很可能是一个用于预后评估的有用工具，并将有助于膀胱癌患者的个性化管理。
