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Dynamic prognostic model for kidney renal clear cell carcinoma (KIRC) patients by combining clinical and genetic information

We aim to construct more accurate prognostic model for KIRC patients by combining the clinical and genetic information and monitor the disease progression in dynamically updated manner. By obtaining cross-validated prognostic indices from clinical and genetic model, we combine the two sources inform...

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Autores principales: Zhao, Huiling, Cao, Yuting, Wang, Yue, Zhang, Liya, Chen, Chen, Wang, Yaoyan, Lu, Xiaofan, Liu, Shengjie, Yan, Fangrong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6279814/
https://www.ncbi.nlm.nih.gov/pubmed/30514856
http://dx.doi.org/10.1038/s41598-018-35981-5
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author Zhao, Huiling
Cao, Yuting
Wang, Yue
Zhang, Liya
Chen, Chen
Wang, Yaoyan
Lu, Xiaofan
Liu, Shengjie
Yan, Fangrong
author_facet Zhao, Huiling
Cao, Yuting
Wang, Yue
Zhang, Liya
Chen, Chen
Wang, Yaoyan
Lu, Xiaofan
Liu, Shengjie
Yan, Fangrong
author_sort Zhao, Huiling
collection PubMed
description We aim to construct more accurate prognostic model for KIRC patients by combining the clinical and genetic information and monitor the disease progression in dynamically updated manner. By obtaining cross-validated prognostic indices from clinical and genetic model, we combine the two sources information into the Super learner model, and then introduce the time-varying effect into the combined model using the landmark method for real-time dynamic prediction. The Super learner model has better prognostic performance since it can not only employ the preferable clinical prognostic model constructed by oneself or reported in the current literature, but also incorporate genome level information to strengthen effectiveness. Apart from this, four representative patients’ mortality curves are drawn in the dynamically updated manner based on the Super learner model. It is found that effectively reducing the two prognostic indices value through suitable treatments might achieve the purpose of controlling the mortality of patients. Combining clinical and genetic information in the Super learner model would enhance the prognostic performance and yield more accurate results for dynamic predictions. Doctors could give patients more personalized treatment with dynamically updated monitoring of disease status, as well as some candidate prognostic factors for future research.
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spelling pubmed-62798142018-12-07 Dynamic prognostic model for kidney renal clear cell carcinoma (KIRC) patients by combining clinical and genetic information Zhao, Huiling Cao, Yuting Wang, Yue Zhang, Liya Chen, Chen Wang, Yaoyan Lu, Xiaofan Liu, Shengjie Yan, Fangrong Sci Rep Article We aim to construct more accurate prognostic model for KIRC patients by combining the clinical and genetic information and monitor the disease progression in dynamically updated manner. By obtaining cross-validated prognostic indices from clinical and genetic model, we combine the two sources information into the Super learner model, and then introduce the time-varying effect into the combined model using the landmark method for real-time dynamic prediction. The Super learner model has better prognostic performance since it can not only employ the preferable clinical prognostic model constructed by oneself or reported in the current literature, but also incorporate genome level information to strengthen effectiveness. Apart from this, four representative patients’ mortality curves are drawn in the dynamically updated manner based on the Super learner model. It is found that effectively reducing the two prognostic indices value through suitable treatments might achieve the purpose of controlling the mortality of patients. Combining clinical and genetic information in the Super learner model would enhance the prognostic performance and yield more accurate results for dynamic predictions. Doctors could give patients more personalized treatment with dynamically updated monitoring of disease status, as well as some candidate prognostic factors for future research. Nature Publishing Group UK 2018-12-04 /pmc/articles/PMC6279814/ /pubmed/30514856 http://dx.doi.org/10.1038/s41598-018-35981-5 Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Zhao, Huiling
Cao, Yuting
Wang, Yue
Zhang, Liya
Chen, Chen
Wang, Yaoyan
Lu, Xiaofan
Liu, Shengjie
Yan, Fangrong
Dynamic prognostic model for kidney renal clear cell carcinoma (KIRC) patients by combining clinical and genetic information
title Dynamic prognostic model for kidney renal clear cell carcinoma (KIRC) patients by combining clinical and genetic information
title_full Dynamic prognostic model for kidney renal clear cell carcinoma (KIRC) patients by combining clinical and genetic information
title_fullStr Dynamic prognostic model for kidney renal clear cell carcinoma (KIRC) patients by combining clinical and genetic information
title_full_unstemmed Dynamic prognostic model for kidney renal clear cell carcinoma (KIRC) patients by combining clinical and genetic information
title_short Dynamic prognostic model for kidney renal clear cell carcinoma (KIRC) patients by combining clinical and genetic information
title_sort dynamic prognostic model for kidney renal clear cell carcinoma (kirc) patients by combining clinical and genetic information
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6279814/
https://www.ncbi.nlm.nih.gov/pubmed/30514856
http://dx.doi.org/10.1038/s41598-018-35981-5
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