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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...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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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. |
format | Online Article Text |
id | pubmed-7603376 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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