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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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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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. |
format | Online Article Text |
id | pubmed-8860306 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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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