Abstract:Objective To evaluate the relationship between preoperative gamma-glutamyl transpeptidase (GGT) and postoperative prognosis of patients with non-metastatic renal cell carcinoma (nmRCC).Methods Clinical data of 118 nmRCC patients treated with radical nephrectomy or partial nephrectomy at Nanchong Central Hospital from January 2013 to July 2018 were collected. The optimal critical value of GGT was determined according to the working curve of subjects receiver operator characteristic and divided into high GGT group and low GGT group, comparing the clinical data of patients in the two groups such as gender, age, tumor diameter, etc. Survival analyses were performed by using the Kaplan-Meier method, survival curves were plotted, and the differences in survival curves of the groups were compared by using the Log-rank method, and the differences in the survival curves of the groups were analyzed by using the Cox multifactorial regression analysis Cox multifactorial regression was used to analyze the independent risk factors affecting the prognosis of nmRCC patients. Results The optimal cut-off value of GGT was 48.5 based on the maximum Jordon index of the ROC curve.The results showed that the percentage of T3-T4 staging was higher in the high-GGT group than in the low-GGT group (38.9% vs 13.0%, P<0.05) and the percentage of G3-G4 grading was higher in the high-GGT group than in the low-GGT group (33.3% vs 3.0%, P<0.05). OS and RFS were lower in the high GGT group than in the low GGT group (Log-rank=33.743,P<0.001;Log-rank=30.854,P<0.001).The results of Cox multifactorial analysis showed that tumor size ≥4 cm, higher tumor T-stage, preoperative GGT ≥48.5 and higher G-grade were independent risk factors affecting postoperative OS and RFS in nmRCC patients (P<0.05) Conclusion Preoperative high GGT in nmRCC patients is an important predictor of high T stage and high G grading of the tumor, as well as an independent risk factor for poor postoperative overall survival and relapse free survival in patients, which can predict their prognosis