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Integrating genetic mutations and expression profiles for survival prediction of lung adenocarcinoma

BACKGROUND: Lung adenocarcinoma (LUAD) is a set of heterogeneous diseases with distinct genetic and transcriptomic characteristics. Since the introduction of the 2011 International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society histologic classificati...

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Autores principales: Song, Yueqiang, Chen, Donglai, Zhang, Xi, Luo, Yuping, Li, Siguang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley & Sons Australia, Ltd 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6501026/
https://www.ncbi.nlm.nih.gov/pubmed/30993904
http://dx.doi.org/10.1111/1759-7714.13072
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author Song, Yueqiang
Chen, Donglai
Zhang, Xi
Luo, Yuping
Li, Siguang
author_facet Song, Yueqiang
Chen, Donglai
Zhang, Xi
Luo, Yuping
Li, Siguang
author_sort Song, Yueqiang
collection PubMed
description BACKGROUND: Lung adenocarcinoma (LUAD) is a set of heterogeneous diseases with distinct genetic and transcriptomic characteristics. Since the introduction of the 2011 International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society histologic classification, increasing evidence has provided insights into genomic mutations and rearrangements among individual histologic subtypes of LUAD. However, how genotypic and phenotypic features of LUAD are interconnected is not well understood. METHODS: We obtained the genomic, transcriptomic, and clinical data sets of 488 LUAD patients from The Cancer Genome Atlas database. Advanced statistical models were used to disentangle the interactions between genetic mutations and expression profiles, and to assess the alterations and changes in expression of each histologic subtype. The prognostic impacts of genetic mutations, expression profiles, and clinicopathological features were integrated to predict the outcomes of LUAD patients. RESULTS: From our data, one or more genetic mutations correlate with expression levels of 6054/18175 (33.3%) genes and explain 8–40% of observed variability in LUAD. The genetic mutations and expression profiles varied remarkably among the histologic subtypes of LUAD, which helped to explain the different prognostic impact based on subtype classification. Genomic, transcriptomic, and clinical data were all shown to have utility for predicting overall and recurrence‐free survival, with the largest contribution from the transcriptome. CONCLUSION: Our prediction model integrating genetic mutations, expression profiles, and clinicopathological features exhibited superior accuracy over the current tumor node metastasis staging system to prognosticate outcomes of patients with LUAD (overall survival 67% vs. 55%, recurrence‐free survival 57% vs. 49%; P < 0.01).
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spelling pubmed-65010262019-05-10 Integrating genetic mutations and expression profiles for survival prediction of lung adenocarcinoma Song, Yueqiang Chen, Donglai Zhang, Xi Luo, Yuping Li, Siguang Thorac Cancer Original Articles BACKGROUND: Lung adenocarcinoma (LUAD) is a set of heterogeneous diseases with distinct genetic and transcriptomic characteristics. Since the introduction of the 2011 International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society histologic classification, increasing evidence has provided insights into genomic mutations and rearrangements among individual histologic subtypes of LUAD. However, how genotypic and phenotypic features of LUAD are interconnected is not well understood. METHODS: We obtained the genomic, transcriptomic, and clinical data sets of 488 LUAD patients from The Cancer Genome Atlas database. Advanced statistical models were used to disentangle the interactions between genetic mutations and expression profiles, and to assess the alterations and changes in expression of each histologic subtype. The prognostic impacts of genetic mutations, expression profiles, and clinicopathological features were integrated to predict the outcomes of LUAD patients. RESULTS: From our data, one or more genetic mutations correlate with expression levels of 6054/18175 (33.3%) genes and explain 8–40% of observed variability in LUAD. The genetic mutations and expression profiles varied remarkably among the histologic subtypes of LUAD, which helped to explain the different prognostic impact based on subtype classification. Genomic, transcriptomic, and clinical data were all shown to have utility for predicting overall and recurrence‐free survival, with the largest contribution from the transcriptome. CONCLUSION: Our prediction model integrating genetic mutations, expression profiles, and clinicopathological features exhibited superior accuracy over the current tumor node metastasis staging system to prognosticate outcomes of patients with LUAD (overall survival 67% vs. 55%, recurrence‐free survival 57% vs. 49%; P < 0.01). John Wiley & Sons Australia, Ltd 2019-04-16 2019-05 /pmc/articles/PMC6501026/ /pubmed/30993904 http://dx.doi.org/10.1111/1759-7714.13072 Text en © 2019 The Authors. Thoracic Cancer published by China Lung Oncology Group and John Wiley & Sons Australia, Ltd This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Articles
Song, Yueqiang
Chen, Donglai
Zhang, Xi
Luo, Yuping
Li, Siguang
Integrating genetic mutations and expression profiles for survival prediction of lung adenocarcinoma
title Integrating genetic mutations and expression profiles for survival prediction of lung adenocarcinoma
title_full Integrating genetic mutations and expression profiles for survival prediction of lung adenocarcinoma
title_fullStr Integrating genetic mutations and expression profiles for survival prediction of lung adenocarcinoma
title_full_unstemmed Integrating genetic mutations and expression profiles for survival prediction of lung adenocarcinoma
title_short Integrating genetic mutations and expression profiles for survival prediction of lung adenocarcinoma
title_sort integrating genetic mutations and expression profiles for survival prediction of lung adenocarcinoma
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6501026/
https://www.ncbi.nlm.nih.gov/pubmed/30993904
http://dx.doi.org/10.1111/1759-7714.13072
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