Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Department of Pathology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Front Immunol. 2022 Feb 7;13:798471. doi: 10.3389/fimmu.2022.798471. eCollection 2022.
It is of great urgency to explore useful prognostic markers and develop a robust prognostic model for patients with clear-cell renal cell carcinoma (ccRCC). Three independent patient cohorts were included in this study. We applied a high-level neural network based on TensorFlow to construct the robust model by using the deep learning algorithm. The deep learning-based model (FB-risk) could perform well in predicting the survival status in the 5-year follow-up, which could also significantly distinguish the patients with high overall survival risk in three independent patient cohorts of ccRCC and a pan-cancer cohort. High FB-risk was found to be partially associated with negative regulation of the immune system. In addition, the novel phenotyping of ccRCC based on the F-box gene family could robustly stratify patients with different survival risks. The different mutation landscapes and immune characteristics were also found among different clusters. Furthermore, the novel phenotyping of ccRCC based on the F-box gene family could perform well in the robust stratification of survival and immune response in ccRCC, which might have potential for application in clinical practices.
探索有用的预后标志物并为透明细胞肾细胞癌 (ccRCC) 患者开发稳健的预后模型迫在眉睫。本研究纳入了三个独立的患者队列。我们应用了基于 TensorFlow 的高级神经网络，通过深度学习算法构建稳健模型。基于深度学习的模型 (FB-risk) 可以很好地预测 5 年随访期间的生存状态，并且可以在三个独立的 ccRCC 患者队列和泛癌队列中显著区分高总体生存风险的患者。高 FB-risk 被发现与免疫系统的负调节部分相关。此外，基于 F-box 基因家族的 ccRCC 的新型表型可以稳健地区分具有不同生存风险的患者。还发现了不同簇之间不同的突变景观和免疫特征。此外，基于 F-box 基因家族的 ccRCC 的新型表型可以很好地对 ccRCC 的生存和免疫反应进行稳健分层，这可能具有在临床实践中的应用潜力。
