Yuying Han1, Qiyu Zheng1, Ye Tian2, Zhengguo Ji2, Haihong Ye1. 1. 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. 2. Department of Urology, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
Abstract
BACKGROUND AND OBJECTIVES: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
BACKGROUND AND OBJECTIVES: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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.