Gene mutation detection of urinary sediment cells for NMIBC early diagnose and prediction of NMIBC relapse after surgery

Early diagnose of bladder cancer could lead to good prognosis and high 5-year-survival rate. Among bladder cancer, about 75% patients with were nonmuscle-invasive bladder cancer (NMIBC). Patients were painful and easily get infected during bladder cancer diagnosis, which mainly depends on invasive cystoscopy and low-sensitivity urine exfoliation cytology. Meanwhile, relapse after surgery was also becoming the major problem for patients. Exploring noninvasive, high-sensitivity, and painless method is very important and meaningful for NMIBC treatment. Firstly, we found potential related gene mutation sites for NMIBC by searching COSMIC database and related study. Urinary sediment cells of patients both in normal group (patients with benign) and NMIMC group were collected before and after operation for potential gene mutation site detecting. Meanwhile, the urinary sediment cells of relapse patients and good prognosis people in NMIBC group after surgery were also collected for further Gene mutation detection and NMIBC relapse after surgery prediction. Fourteen genes (152 mutation sites) were selected between 95 NMIBC patients and 67 control patients, which were FGFR3, TP53, PIK3CA, and others. Compared with control group, mutation ratio of above 14 genes was higher in NMIBC group. NMIBC diagnose model was established by 5 times cross-validation and had a good effects, which included the all mutation site in FGFR3, TP53, PIK3CA, ARID1A, STAG2, and KTM2D. On the contrary, the relapse rate was 30.5% among 95 patients for about 1.5-year follow-up time. Compared with control group, smoking rate and tumor grade were higher in relapse group. Meanwhile, mutation rate of FGFR3, TP53, PIK3CA, ERBB3, and TSC1 in relapse group were higher than that in normal group. According to the mutation sites of FGFR3, TP53, PIK3CA, and ERBB3 and the combination of urinary sediment cells genetic mutation and relapse status, a predicted model for NMIBC relapse was also established, which had 90% accuracy. The diagnosed NMIBC model (based on FGFR3, TP53, PIK3CA, ARID1A, STAG2, and KTM2D gene mutation) and predicted relapse model (based on FGFR3, TP53, PIK3CA, and ERBB3 gene mutation) possess high accuracy and would be applied in early diagnose and early predicting relapse of patients.