A urinary microRNA signature can predict the presence of bladder urothelial carcinoma in patients undergoing surveillance

Abstract

Background:The objective of this study was to determine whether microRNA (miRNA) profiling of urine could identify the presence of urothelial carcinoma of the bladder (UCB) and to compare its performance characteristics to that of cystoscopy.Methods:In the discovery cohort we screened 81 patients, which included 21 benign controls, 30 non-recurrers and 30 patients with active cancer (recurrers), using a panel of 12 miRNAs. Data analysis was performed using a machine learning approach of a Support Vector Machine classifier with a Student's t-test feature selection procedure. This was trained using a three-fold cross validation approach and performance was measured using the area under the receiver operator characteristic curve (AUC). The miRNA signature was validated in an independent cohort of a further 50 patients.Results:The best predictor to distinguish patients with UCB from non-recurrers was achieved using a combination of six miRNAs (AUC=0.85). This validated in an independent cohort (AUC=0.74) and detected UCB with a high sensitivity (88%) and sufficient specificity (48%) with all significant cancers identified. The performance of the classifier was best in detecting clinically significant disease such as presence of T1 Stage disease (AUC=0.92) and high-volume disease (AUC=0.81). Cystoscopy rates in the validation cohort would have been reduced by 30%.Conclusions:Urinary profiling using this panel of miRNAs shows promise for detection of tumour recurrence in the surveillance of UCB. Such a panel may be useful in reducing the morbidity and costs associated with cystoscopic surveillance, and now merits prospective evaluation.

Current status of artificial intelligence applications in urology and their potential to influence clinical practice

“…Urothelial cancer is a serious urological malignancy with high rate of recurrence. AI algorithms have been developed to predict cancer recurrence and patient survival (Table ). Using clinicopathological data, Lam et al.…”

“…Urothelial cancer is a serious urological malignancy with high rate of recurrence. AI algorithms have been developed to predict cancer recurrence and patient survival [30][31][32][33] ( Table 2). Using clinicopathological data, Lam et al [30] and Wang et al [31] developed and validated an array of AI algorithms to predict 5-year survival rate after radical cystectomy.…”

Increased accuracy of a novel mRNA‐based urine test for bladder cancer surveillance

“…The application of the Cxbladder Monitor in patients with microhaematuria reduced the number of invasive diagnostic procedures such as cystoscopy and CT scans . A novel miRNA signature panel (miR16, miR200c, miR205, miR21, miR221 and miR34a) developed to monitor patients with BC in the active surveillance regime achieved a high overall sensitivity of 88%, but a low specificity of 48%, and also had a limited ability to detect patients with low‐volume tumours (AUC = 0.69) . Several years ago, Mengual et al.…”

“…In addition, RT‐PCR‐based urinary tests have the advantage of being reliable, easy to perform, and objective in contrast to cytology . One of the major limitations of the use of mRNA‐based techniques is the difficulty in obtaining a sufficient quantity of high‐quality RNA from voided urine . To overcome this problem, a positive ABL1 signal, indicating that the urine sample contains sufficient human cells and human RNA, was required for a valid test result (positive or negative) when using the Xpert BC Monitor.…”

Exploration of bladder cancer molecular mechanisms based on miRNA-mRNA regulatory network

“…In the present study, 6 miRNAs documented in previous research were used for the construction of miRNA-mRNA pairings contributing to bladder cancer. Combination of the 6 miRNAs has been demonstrated to be the best predictor to distinguish patients with urothelial bladder cancer from normal controls (14). miRNA-mRNA regulatory network was constructed using CyTargetLinker plugin (15) of Cytoscape (16).…”