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Identification of an eight-gene prognostic signature for lung adenocarcinoma

BACKGROUND: Lung adenocarcinoma (LUAD) is the leading cause of cancer-related death worldwide. The main obstacle to early diagnosis or monitoring of patients at high risk of poor survival has been the lack of essential predictive biomarkers. METHODS: RNA-sequencing was performed on LUAD affected tis...

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Autores principales: Li, Shicheng, Xuan, Yunpeng, Gao, Bing, Sun, Xiao, Miao, Shuncheng, Lu, Tong, Wang, Yuanyong, Jiao, Wenjie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove Medical Press 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6138967/
https://www.ncbi.nlm.nih.gov/pubmed/30237740
http://dx.doi.org/10.2147/CMAR.S173941
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author Li, Shicheng
Xuan, Yunpeng
Gao, Bing
Sun, Xiao
Miao, Shuncheng
Lu, Tong
Wang, Yuanyong
Jiao, Wenjie
author_facet Li, Shicheng
Xuan, Yunpeng
Gao, Bing
Sun, Xiao
Miao, Shuncheng
Lu, Tong
Wang, Yuanyong
Jiao, Wenjie
author_sort Li, Shicheng
collection PubMed
description BACKGROUND: Lung adenocarcinoma (LUAD) is the leading cause of cancer-related death worldwide. The main obstacle to early diagnosis or monitoring of patients at high risk of poor survival has been the lack of essential predictive biomarkers. METHODS: RNA-sequencing was performed on LUAD affected tissue and paired adjacent to noncancerous tissue samples and Gene Expression Omnibus dataset GSE19188 and GSE33532 were used to obtain an intersection of differential expressed genes and construct a protein–protein interaction network to get hub genes. Then corresponding overall survival information of two cohorts of LUAD patients from our hospital and The Cancer Genome Atlas project-LUAD were included in the present study. An analysis of the Kyoto Encyclopedia of Genes and Genomes database and Gene Ontology were carried out to study the signature mechanism. RESULTS: In our study, we identified eight candidate genes (DLGAP5, KIF11, RAD51AP1, CCNB1, AURKA, CDC6, OIP5 and NCAPG) closely related to survival in LUAD. A linear prognostic model of the eight genes was constructed and weighted by the regression coefficient (β) from the multivariate Cox regression analysis of The Cancer Genome Atlas-LUAD cohort to divide patients into low- and high-risk groups. The prognostic ability of the signature was validated in LUAD patients at our hospital. Patients assigned to the high-risk group exhibited poor overall survival compared to patients in the low-risk group. Finally, functional enrichment analysis showed that cell division played a vital role in the development of LUAD. CONCLUSION: The study identified an mRNA signature including eight genes, which may serve as a potential prognostic marker of LUAD.
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spelling pubmed-61389672018-09-20 Identification of an eight-gene prognostic signature for lung adenocarcinoma Li, Shicheng Xuan, Yunpeng Gao, Bing Sun, Xiao Miao, Shuncheng Lu, Tong Wang, Yuanyong Jiao, Wenjie Cancer Manag Res Original Research BACKGROUND: Lung adenocarcinoma (LUAD) is the leading cause of cancer-related death worldwide. The main obstacle to early diagnosis or monitoring of patients at high risk of poor survival has been the lack of essential predictive biomarkers. METHODS: RNA-sequencing was performed on LUAD affected tissue and paired adjacent to noncancerous tissue samples and Gene Expression Omnibus dataset GSE19188 and GSE33532 were used to obtain an intersection of differential expressed genes and construct a protein–protein interaction network to get hub genes. Then corresponding overall survival information of two cohorts of LUAD patients from our hospital and The Cancer Genome Atlas project-LUAD were included in the present study. An analysis of the Kyoto Encyclopedia of Genes and Genomes database and Gene Ontology were carried out to study the signature mechanism. RESULTS: In our study, we identified eight candidate genes (DLGAP5, KIF11, RAD51AP1, CCNB1, AURKA, CDC6, OIP5 and NCAPG) closely related to survival in LUAD. A linear prognostic model of the eight genes was constructed and weighted by the regression coefficient (β) from the multivariate Cox regression analysis of The Cancer Genome Atlas-LUAD cohort to divide patients into low- and high-risk groups. The prognostic ability of the signature was validated in LUAD patients at our hospital. Patients assigned to the high-risk group exhibited poor overall survival compared to patients in the low-risk group. Finally, functional enrichment analysis showed that cell division played a vital role in the development of LUAD. CONCLUSION: The study identified an mRNA signature including eight genes, which may serve as a potential prognostic marker of LUAD. Dove Medical Press 2018-09-10 /pmc/articles/PMC6138967/ /pubmed/30237740 http://dx.doi.org/10.2147/CMAR.S173941 Text en © 2018 Li et al. This work is published and licensed by Dove Medical Press Limited The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed.
spellingShingle Original Research
Li, Shicheng
Xuan, Yunpeng
Gao, Bing
Sun, Xiao
Miao, Shuncheng
Lu, Tong
Wang, Yuanyong
Jiao, Wenjie
Identification of an eight-gene prognostic signature for lung adenocarcinoma
title Identification of an eight-gene prognostic signature for lung adenocarcinoma
title_full Identification of an eight-gene prognostic signature for lung adenocarcinoma
title_fullStr Identification of an eight-gene prognostic signature for lung adenocarcinoma
title_full_unstemmed Identification of an eight-gene prognostic signature for lung adenocarcinoma
title_short Identification of an eight-gene prognostic signature for lung adenocarcinoma
title_sort identification of an eight-gene prognostic signature for lung adenocarcinoma
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6138967/
https://www.ncbi.nlm.nih.gov/pubmed/30237740
http://dx.doi.org/10.2147/CMAR.S173941
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