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Predicting Deep Learning Based Multi-Omics Parallel Integration Survival Subtypes in Lung Cancer Using Reverse Phase Protein Array Data

Mortality attributed to lung cancer accounts for a large fraction of cancer deaths worldwide. With increasing mortality figures, the accurate prediction of prognosis has become essential. In recent years, multi-omics analysis has emerged as a useful survival prediction tool. However, the methodology...

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Autores principales: Takahashi, Satoshi, Asada, Ken, Takasawa, Ken, Shimoyama, Ryo, Sakai, Akira, Bolatkan, Amina, Shinkai, Norio, Kobayashi, Kazuma, Komatsu, Masaaki, Kaneko, Syuzo, Sese, Jun, Hamamoto, Ryuji
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7603376/
https://www.ncbi.nlm.nih.gov/pubmed/33086649
http://dx.doi.org/10.3390/biom10101460
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author Takahashi, Satoshi
Asada, Ken
Takasawa, Ken
Shimoyama, Ryo
Sakai, Akira
Bolatkan, Amina
Shinkai, Norio
Kobayashi, Kazuma
Komatsu, Masaaki
Kaneko, Syuzo
Sese, Jun
Hamamoto, Ryuji
author_facet Takahashi, Satoshi
Asada, Ken
Takasawa, Ken
Shimoyama, Ryo
Sakai, Akira
Bolatkan, Amina
Shinkai, Norio
Kobayashi, Kazuma
Komatsu, Masaaki
Kaneko, Syuzo
Sese, Jun
Hamamoto, Ryuji
author_sort Takahashi, Satoshi
collection PubMed
description Mortality attributed to lung cancer accounts for a large fraction of cancer deaths worldwide. With increasing mortality figures, the accurate prediction of prognosis has become essential. In recent years, multi-omics analysis has emerged as a useful survival prediction tool. However, the methodology relevant to multi-omics analysis has not yet been fully established and further improvements are required for clinical applications. In this study, we developed a novel method to accurately predict the survival of patients with lung cancer using multi-omics data. With unsupervised learning techniques, survival-associated subtypes in non-small cell lung cancer were first detected using the multi-omics datasets from six categories in The Cancer Genome Atlas (TCGA). The new subtypes, referred to as integration survival subtypes, clearly divided patients into longer and shorter-surviving groups (log-rank test: p = 0.003) and we confirmed that this is independent of histopathological classification (Chi-square test of independence: p = 0.94). Next, an attempt was made to detect the integration survival subtypes using only one categorical dataset. Our machine learning model that was only trained on the reverse phase protein array (RPPA) could accurately predict the integration survival subtypes (AUC = 0.99). The predicted subtypes could also distinguish between high and low risk patients (log-rank test: p = 0.012). Overall, this study explores novel potentials of multi-omics analysis to accurately predict the prognosis of patients with lung cancer.
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spelling pubmed-76033762020-11-01 Predicting Deep Learning Based Multi-Omics Parallel Integration Survival Subtypes in Lung Cancer Using Reverse Phase Protein Array Data Takahashi, Satoshi Asada, Ken Takasawa, Ken Shimoyama, Ryo Sakai, Akira Bolatkan, Amina Shinkai, Norio Kobayashi, Kazuma Komatsu, Masaaki Kaneko, Syuzo Sese, Jun Hamamoto, Ryuji Biomolecules Article Mortality attributed to lung cancer accounts for a large fraction of cancer deaths worldwide. With increasing mortality figures, the accurate prediction of prognosis has become essential. In recent years, multi-omics analysis has emerged as a useful survival prediction tool. However, the methodology relevant to multi-omics analysis has not yet been fully established and further improvements are required for clinical applications. In this study, we developed a novel method to accurately predict the survival of patients with lung cancer using multi-omics data. With unsupervised learning techniques, survival-associated subtypes in non-small cell lung cancer were first detected using the multi-omics datasets from six categories in The Cancer Genome Atlas (TCGA). The new subtypes, referred to as integration survival subtypes, clearly divided patients into longer and shorter-surviving groups (log-rank test: p = 0.003) and we confirmed that this is independent of histopathological classification (Chi-square test of independence: p = 0.94). Next, an attempt was made to detect the integration survival subtypes using only one categorical dataset. Our machine learning model that was only trained on the reverse phase protein array (RPPA) could accurately predict the integration survival subtypes (AUC = 0.99). The predicted subtypes could also distinguish between high and low risk patients (log-rank test: p = 0.012). Overall, this study explores novel potentials of multi-omics analysis to accurately predict the prognosis of patients with lung cancer. MDPI 2020-10-19 /pmc/articles/PMC7603376/ /pubmed/33086649 http://dx.doi.org/10.3390/biom10101460 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Takahashi, Satoshi
Asada, Ken
Takasawa, Ken
Shimoyama, Ryo
Sakai, Akira
Bolatkan, Amina
Shinkai, Norio
Kobayashi, Kazuma
Komatsu, Masaaki
Kaneko, Syuzo
Sese, Jun
Hamamoto, Ryuji
Predicting Deep Learning Based Multi-Omics Parallel Integration Survival Subtypes in Lung Cancer Using Reverse Phase Protein Array Data
title Predicting Deep Learning Based Multi-Omics Parallel Integration Survival Subtypes in Lung Cancer Using Reverse Phase Protein Array Data
title_full Predicting Deep Learning Based Multi-Omics Parallel Integration Survival Subtypes in Lung Cancer Using Reverse Phase Protein Array Data
title_fullStr Predicting Deep Learning Based Multi-Omics Parallel Integration Survival Subtypes in Lung Cancer Using Reverse Phase Protein Array Data
title_full_unstemmed Predicting Deep Learning Based Multi-Omics Parallel Integration Survival Subtypes in Lung Cancer Using Reverse Phase Protein Array Data
title_short Predicting Deep Learning Based Multi-Omics Parallel Integration Survival Subtypes in Lung Cancer Using Reverse Phase Protein Array Data
title_sort predicting deep learning based multi-omics parallel integration survival subtypes in lung cancer using reverse phase protein array data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7603376/
https://www.ncbi.nlm.nih.gov/pubmed/33086649
http://dx.doi.org/10.3390/biom10101460
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