Wang Xuhui
Department of Urology, The Affiliated People's Hospital of Ningbo University, No.251, Baizhang East Road, Yinzhou District, Ningbo, 315040, China.
Discov Oncol. 2024 Dec 18;15(1):780. doi: 10.1007/s12672-024-01669-8.
To summarize the clinicopathological characteristics and prognostic factors of papillary renal cell carcinoma (pRCC) and to construct clinical and molecular prognostic nomograms using existing databases.
Clinical prognostic models were developed using the Surveillance, Epidemiology, and End Results (SEER) database, while molecular prognostic models were constructed using The Cancer Genome Atlas (TCGA) database. Cox regression and LASSO regression were employed to identify clinicopathological features and molecular markers related to prognosis. The accuracy of the prognostic models was assessed using ROC curves, C-index, decision curve analysis (DCA) curves, and calibration plots.
In the 2004-2015 SEER cohort, Cox regression analysis revealed that age, grade, AJCC stage, N stage, M stage, and surgery were independent predictors of overall survival (OS) and cancer-specific survival (CSS) in pRCC patients. ROC curves, C-index, and DCA curves indicated that the prognostic nomogram based on clinical independent predictors had better predictive ability than TNM staging and SEER staging. Additionally, in the TCGA cohort, M stage, clinical stage, and the molecular markers IDO1 and PLK1 were identified as independent risk factors. The prognostic nomogram based on molecular independent risk factors effectively predicted the 3-year and 5-year OS and CSS for pRCC patients.
The clinical and molecular nomograms constructed in this study provide robust predictive tools for individualized prognosis in pRCC patients, offering better accuracy than traditional staging systems.
总结乳头状肾细胞癌（pRCC）的临床病理特征和预后因素，并利用现有数据库构建临床和分子预后列线图。
使用监测、流行病学和最终结果（SEER）数据库开发临床预后模型，同时使用癌症基因组图谱（TCGA）数据库构建分子预后模型。采用Cox回归和LASSO回归来识别与预后相关的临床病理特征和分子标志物。使用ROC曲线、C指数、决策曲线分析（DCA）曲线和校准图评估预后模型的准确性。
在2004 - 2015年SEER队列中，Cox回归分析显示年龄、分级、美国癌症联合委员会（AJCC）分期、N分期、M分期和手术是pRCC患者总生存期（OS）和癌症特异性生存期（CSS）的独立预测因素。ROC曲线、C指数和DCA曲线表明，基于临床独立预测因素的预后列线图比TNM分期和SEER分期具有更好的预测能力。此外，在TCGA队列中，M分期、临床分期以及分子标志物IDO1和PLK1被确定为独立危险因素。基于分子独立危险因素的预后列线图有效地预测了pRCC患者的3年和5年OS及CSS。
本研究构建的临床和分子列线图为pRCC患者的个体化预后提供了强大的预测工具，比传统分期系统具有更高的准确性。
