Robust Prediction of Prognosis and Immunotherapeutic Response for Clear Cell Renal Cell Carcinoma Through Deep Learning Algorithm

被引:10
|
作者
Chen, Siteng [1 ]
Zhang, Encheng [1 ]
Jiang, Liren [2 ]
Wang, Tao [1 ]
Guo, Tuanjie [1 ]
Gao, Feng [2 ]
Zhang, Ning [3 ]
Wang, Xiang [1 ]
Zheng, Junhua [1 ]
机构
[1] Shanghai Jiao Tong Univ, Sch Med, Shanghai Gen Hosp, Dept Urol, Shanghai, Peoples R China
[2] Shanghai Jiao Tong Univ, Sch Med, Shanghai Gen Hosp, Dept Pathol, Shanghai, Peoples R China
[3] Shanghai Jiao Tong Univ, Sch Med, Ruijin Hosp, Dept Urol, Shanghai, Peoples R China
来源
FRONTIERS IN IMMUNOLOGY | 2022年 / 13卷
基金
中国国家自然科学基金;
关键词
deep learning; renal cell carcinoma; F-box family; immunotherapy; prognosis; F-BOX PROTEINS; CANCER; DEGRADATION;
D O I
10.3389/fimmu.2022.798471
中图分类号
R392 [医学免疫学]; Q939.91 [免疫学];
学科分类号
100102 ;
摘要
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.
引用
收藏
页数:11
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