Bladder cancer is one of the most common cancers, and its histopathological type is mainly bladder urothelial carcinoma, accounting for about 90%. The prognostic biomarkers of bladder cancer are classified into clinical features biomarkers and molecular biomarkers. Nevertheless, due to the existence of individual specificity, patients with similar pathological characteristics still have great differences in the risk of disease recurrence. Therefore, it is often inaccurate to predict the survival status of patients based on clinical characteristic biomarkers, and a prognostic molecular biomarker that can grade the risk of bladder cancer patients is needed.
A total of three bladder urothelial carcinoma datasets were used in this study from the Cancer Genome Atlas database and Gene Expression Omnibus database. In order to avoid overfitting, all samples were randomly divided into one training set and three validation sets, which were used to construct and test the prognostic biomarker model of bladder urothelial carcinoma. Univariate and multivariate Cox regression were used to screen candidate mRNAs and construct prognostic biomarkers model. Kaplan-Meier survival analysis and the receiver operating characteristic (ROC) curve were used to evaluate the predictive performance of the model.
A prognostic biomarker model of bladder urothelial carcinoma combining with eight mRNA was constructed. Kaplan-Meier analyses indicated that a significant difference in the survival time of patients between the high-risk and the low-risk group. The area under the ROC curve were 0.632 (95% confidence interval (CI) [0.541-0.723]), 0.693 (95% CI [0.601-0.784]) and 0.686 (95% CI [0.540-0.831]) when the model was used to predict the patient's survival time in three validation datasets. The model showed high accuracy and applicability.
PeerJ. 2019 Oct 22*** epublish ***
Rui Zhu, Xin Yang, Wenna Guo, Xin-Jian Xu, Liucun Zhu
Department of Mathematics, Shanghai University, Shanghai, China., School of Life Sciences, Shanghai University, Shanghai, China., School of Life Sciences, Zhengzhou University, Zhengzhou, Henan, China.