Objectives: Radical cystectomy is the gold-standard treatment for muscle-invasive bladder cancer and aggressive non-muscle-invasive bladder cancer. To enhance clinical decision-making regarding patients with bladder cancer who underwent radical cystectomy, a recurrence prediction biomarker with high accuracy is urgently needed. In this study, we developed a model for the prediction of bladder cancer recurrence after radical cystectomy by combining serum microRNA and a pathological factor.
Methods: We retrospectively analyzed the clinical records of 81 patients with bladder cancer who underwent radical cystectomy between 2008 and 2016. The dataset was divided into two, and Fisher linear discriminant analysis was used to construct a prognostic model for future recurrence in the training set (n = 41). The performance of the model was evaluated in the validation set (n = 40).
Results: Thirty patients had recurrence after having undergone radical cystectomy. A prognostic model for recurrence was constructed by combining a pathological factor (i.e. positive pathological lymph node status) and three microRNAs (miR-23a-3p, miR-3679-3p, and miR-3195). The model showed a sensitivity of 0.87, a specificity of 0.80, and an area under the receiver operating characteristic curve of 0.88 (0.77-0.98) in the validation set. Furthermore, Kaplan-Meier analysis revealed that patients with a low prediction index have significantly longer overall survival than patients with a high prediction index (P = 0.041).
Conclusion: A combination of serum microRNA profiles and lymph node statuses is useful for the prediction of oncological outcomes after radical cystectomy in patients with bladder cancer.
Keywords: biomarker; bladder cancer; cystectomy; liquid biopsy; serum microRNA.
© 2022 The Japanese Urological Association.