Research Paper Volume 16, Issue 2 pp 1696—1711
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
- 1 Department of Urology, Urology and Nephrology Center, Zhejiang Provincial People’s Hospital, Hangzhou Medical College, Hangzhou, Zhejiang 310011, China
- 2 Graduate School of Bengbu Medical College, Bengbu, Anhui 233030, China
- 3 Department of Clinical Laboratory, Zhejiang Provincial People’s Hospital, Hangzhou, Zhejiang 310011, China
- 4 Department of Thoracic Surgery, The Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang 310005, China
- 5 Department of Urology, The Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang 310005, China
- 6 Department of Clinical Laboratory, The Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang 310005, China
- 7 Department of Medical Psychology, The Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang 310005, China
- 8 Department of Urology, Zhejiang Medical and Health Group Hangzhou Hospital of Hangzhou Medical College, Hangzhou, Zhejiang 310022, China
Received: September 28, 2023 Accepted: December 14, 2023 Published: January 22, 2024
https://doi.org/10.18632/aging.205452How to Cite
Copyright: © 2024 Yang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Objective: 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.
Methods: 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.
Results: 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.
Conclusion: 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.
Abbreviations
TFE3-RCC: Xp11.2 translocation/TFE3 gene fusion renal cell carcinoma; RCC: Renal cell carcinoma; TKI: Tyrosine Kinase Inhibitors; BMI: Body Mass Index; AUC: Area under the receiver operating characteristic curve; IHC: Immunohistochemistry test; FISH: Fluorescence in situ hybridization; EOS: Eosinophils; LDL: Low-density lipoprotein; MON: Monocyte; BAS: Basophil; ALB: Albumin; ALT: Alanine transaminase; AST: Aspartate aminotransferase; GGT: γ-Glutamyl Transferase; BA: Bile acids; GLU: Blood glucose; BUN: Urea; UA: Uric acid; TC: Total cholesterol; TG: Triglyceride; HDL: High-density lipoprotein; LDL: Low-density lipoprotein; LDH: Lactate dehydrogenase.