Modeling and validation of serum miR-18a and miR-122 levels as predictors of recurrence after laparoscopic radical cystectomy procedure for bladder cancer based on nomogram model

TL;DR

This study developed a nomogram model using miR-18a and miR-122 levels to predict bladder cancer recurrence after surgery.

Contribution

A novel nomogram model was created and validated for predicting recurrence using specific miRNA levels and clinical factors.

Findings

Preoperative miR-18a and miR-122 levels were significantly associated with recurrence.

The model showed good predictive ability with AUCs of 0.796 and 0.762 in training and validation sets.

Decision curve analysis confirmed the model's clinical utility.

Abstract

This study aimed to identify factors influencing recurrence after laparoscopic radical cystectomy for bladder cancer (BC) based on serum levels of miR-18a and miR-122, and to develop and validate a nomogram prediction model.

The relevant information of BC patients who received laparoscopic radical cystectomy procedure in our hospital from January 2021 to October 2022 was collected retrospectively. The patients were divided into a training set and a validation set at a ratio of 7:3 by the complete randomization method. Independent predictive variables included in the Nomogram model were determined and modeled through univariate analysis and multivariate Logistic regression analysis. The receiver operating characteristic curve (ROC) and calibration curves were used to evaluate the predictive efficacy of the model, and decision curve analysis (DCA) was used to evaluate its clinical application value.

A total of 280 research subjects were included. Recurrence occurred in 46 (23.47%) of the 196 patients in the training set and 21 (25.00%) of the 84 patients in the validation set. The results of the multivariate logistic regression analysis showed that preoperative serum miR-18a levels, preoperative serum miR-122 levels, postoperative serum carcinoembryonic antigen levels, postoperative serum carbohydrate antigen 19–9 levels, and postoperative antibiotic use duration were significantly associated with recurrence after laparoscopic radical cystectomy for BC. The model was well calibrated and fitted in the training and validation sets. The ROC curve showed that the AUC of the nomogram model to predict postoperative recurrence were 0.796(95% CI: 0.688-0.904) and 0.762(95% CI: 0. 578-0.946), respectively. DCA indicated that the model had clinical application value.

The Nomogram model for recurrence after laparoscopic radical cystectomy procedure for BC has good prediction ability.

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