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

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 robu...

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Autores principales: Chen, Siteng, Zhang, Encheng, Jiang, Liren, Wang, Tao, Guo, Tuanjie, Gao, Feng, Zhang, Ning, Wang, Xiang, Zheng, Junhua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8860306/
https://www.ncbi.nlm.nih.gov/pubmed/35197975
http://dx.doi.org/10.3389/fimmu.2022.798471
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author Chen, Siteng
Zhang, Encheng
Jiang, Liren
Wang, Tao
Guo, Tuanjie
Gao, Feng
Zhang, Ning
Wang, Xiang
Zheng, Junhua
author_facet Chen, Siteng
Zhang, Encheng
Jiang, Liren
Wang, Tao
Guo, Tuanjie
Gao, Feng
Zhang, Ning
Wang, Xiang
Zheng, Junhua
author_sort Chen, Siteng
collection PubMed
description 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.
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spelling pubmed-88603062022-02-22 Robust Prediction of Prognosis and Immunotherapeutic Response for Clear Cell Renal Cell Carcinoma Through Deep Learning Algorithm Chen, Siteng Zhang, Encheng Jiang, Liren Wang, Tao Guo, Tuanjie Gao, Feng Zhang, Ning Wang, Xiang Zheng, Junhua Front Immunol Immunology 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. Frontiers Media S.A. 2022-02-07 /pmc/articles/PMC8860306/ /pubmed/35197975 http://dx.doi.org/10.3389/fimmu.2022.798471 Text en Copyright © 2022 Chen, Zhang, Jiang, Wang, Guo, Gao, Zhang, Wang and Zheng https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Immunology
Chen, Siteng
Zhang, Encheng
Jiang, Liren
Wang, Tao
Guo, Tuanjie
Gao, Feng
Zhang, Ning
Wang, Xiang
Zheng, Junhua
Robust Prediction of Prognosis and Immunotherapeutic Response for Clear Cell Renal Cell Carcinoma Through Deep Learning Algorithm
title Robust Prediction of Prognosis and Immunotherapeutic Response for Clear Cell Renal Cell Carcinoma Through Deep Learning Algorithm
title_full Robust Prediction of Prognosis and Immunotherapeutic Response for Clear Cell Renal Cell Carcinoma Through Deep Learning Algorithm
title_fullStr Robust Prediction of Prognosis and Immunotherapeutic Response for Clear Cell Renal Cell Carcinoma Through Deep Learning Algorithm
title_full_unstemmed Robust Prediction of Prognosis and Immunotherapeutic Response for Clear Cell Renal Cell Carcinoma Through Deep Learning Algorithm
title_short Robust Prediction of Prognosis and Immunotherapeutic Response for Clear Cell Renal Cell Carcinoma Through Deep Learning Algorithm
title_sort robust prediction of prognosis and immunotherapeutic response for clear cell renal cell carcinoma through deep learning algorithm
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8860306/
https://www.ncbi.nlm.nih.gov/pubmed/35197975
http://dx.doi.org/10.3389/fimmu.2022.798471
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