Therapeutic strategies and predictive models for Xp11.2 translocation/TFE3 gene fusion renal cell carcinoma in adults based on data of two Chinese medical centers.

Abstract

This study aims to establish an effective predictive model for predicting Xp11.2 translocation/TFE3 gene fusion renal cell carcinoma (TFE3-RCC) and develop optimal therapeutic strategies. Data from 4961 patients diagnosed with renal cell carcinoma at two medical centers in China were retrospectively analyzed. A cohort of 1571 patients from Zhejiang Provincial People's Hospital (Ra cohort) was selected to construct the model. Another cohort of 1124 patients from the Second Affiliated Hospital of Zhejiang Chinese Medical University was used for external validation (the Ha cohort). All patients with TFE3-RCC in both cohorts were included in the Ta cohort for the prognostic analysis. Univariate and multivariate binary logistic regression analyses were performed to identify independent predictors of the predictive nomogram. The apparent performance of the model was validated. Decision curve analysis was also performed to assess the clinical utility of the developed model. Factors associated with progression and prognosis in the Ta cohort were analyzed using the log-rank method, and Cox regression analysis and Kaplan-Meier survival curves were used to describe the effects of factors on prognosis and progression. Univariate and multivariate logistic regression analyses demonstrated that age, sex, BMI, smoking, eosinophils, and LDL were independent predictors of TFE3-RCC. Therefore, a predictive nomogram for TFE3-RCC, which had good discriminatory power (AUC = 0.796), was constructed. External validation (AUC = 0.806) also revealed good predictive ability. The calibration curves displayed good consistency between the predicted and observed incidences of TFE3-RCC. Invasion of regional lymph nodes, tyrosine kinase inhibitors, and surgical methods were independent factors associated with progression. Tyrosine kinase inhibitors are independent prognostic factors. This study not only proposed a high-precision clinical prediction model composed of various variables for the early diagnosis of Xp11.2 translocation/TFE3 gene fusion renal cell carcinoma but also optimized therapeutic strategies through prognostic analysis.