A Panel of Three Serum MicroRNAs as a Potential Diagnostic Biomarker for Urothelial Carcinoma

Abstract

<b><i>Objective:</i></b> Urothelial carcinoma (UCa) is one of the most common malignancies of the genitourinary system, and its early diagnosis is vital to improve the survival of UCa patients. Therefore, novel noninvasive markers are urgently needed to improve the diagnosis of UCa. The present study aims to identify microRNAs (miRNAs) relevant for the diagnosis of UCa. <b><i>Materials and Methods:</i></b> We enrolled a total of 152 UCa patients and 135 healthy controls at a single institution, between 2012 and 2020. The expression levels of candidate miRNAs were calculated from serum samples based on quantitative reverse transcription-polymerase chain reaction. miRNAs with a good diagnostic value were selected and confirmed step by step in a four-phase test. The area under the curve (AUC) of each miRNA was obtained by analyzing the receiver operating characteristic (ROC) curve, which was used to evaluate the sensitivity, specificity, and corresponding cutoff values of miRNAs. Backward stepwise logistic regression was used to identify a panel of miRNAs that could distinguish UCa from healthy controls. <b><i>Results:</i></b> Four miRNAs were relevant for diagnosis: miR17-5p (AUC = 0.786, <i>p</i> &#x3c; 0.001), miR-125a-5p (AUC = 0.681, <i>p</i> &#x3c; 0.01), miR145-5p (AUC = 0.737, <i>p</i> &#x3c; 0.001), and miR-224-5p (AUC = 0.872, <i>p</i> &#x3c; 0.001). These miRNAs were used to construct a diagnostic panel. The final optimal combination to diagnose UCa included three miRNAs, namely, miR17-5p, miR145-5p, and miR-224-5p. The ROC curve of the panel was constructed, and its AUC was 0.961 (95% CI: 0.931–0.991; sensitivity = 93.8%, and specificity = 87.5%). <b><i>Conclusion:</i></b> In this study, we discovered four miRNAs (miR17-5p, miR-125a-5p, miR145-5p, and miR-224-5p) that were relevant for UCa diagnosis, and successfully developed a panel using three of these miRNAs. This panel may serve as a new biomarker tool with high sensitivity and specificity to diagnose UCa in patients.