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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley & Sons Australia, Ltd
2019
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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). |
format | Online Article Text |
id | pubmed-6501026 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | John Wiley & Sons Australia, Ltd |
record_format | MEDLINE/PubMed |
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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