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A Prognostic 14-Gene Expression Signature for Lung Adenocarcinoma: A Study Based on TCGA Data Mining

BACKGROUND: Lung adenocarcinoma (LUAD), a major and fatal subtype of lung cancer, caused lots of mortalities and showed different outcomes in prognosis. This study was to assess key genes and to develop a prognostic signature for the patient therapy with LUAD. METHOD: RNA expression profile and clin...

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Autores principales: Liu, Jie, Hou, Shiqiang, Wang, Jinyi, Chai, Zhengjun, Hong, Xuan, Zhao, Tian, Sun, Zhengliang, Bai, Liandi, Gao, Hongyan, Gao, Jing, Chen, Guohan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7769675/
https://www.ncbi.nlm.nih.gov/pubmed/33414898
http://dx.doi.org/10.1155/2020/8847226
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author Liu, Jie
Hou, Shiqiang
Wang, Jinyi
Chai, Zhengjun
Hong, Xuan
Zhao, Tian
Sun, Zhengliang
Bai, Liandi
Gao, Hongyan
Gao, Jing
Chen, Guohan
author_facet Liu, Jie
Hou, Shiqiang
Wang, Jinyi
Chai, Zhengjun
Hong, Xuan
Zhao, Tian
Sun, Zhengliang
Bai, Liandi
Gao, Hongyan
Gao, Jing
Chen, Guohan
author_sort Liu, Jie
collection PubMed
description BACKGROUND: Lung adenocarcinoma (LUAD), a major and fatal subtype of lung cancer, caused lots of mortalities and showed different outcomes in prognosis. This study was to assess key genes and to develop a prognostic signature for the patient therapy with LUAD. METHOD: RNA expression profile and clinical data from 522 LUAD patients were accessed and downloaded from the Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were extracted and analyzed between normal tissues and LUAD samples. Then, a 14-DEG signature was developed and identified for the survival prediction in LUAD patients by means of univariate and multivariate Cox regression analyses. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed to predict the potential biological functions and pathways of these DEGs. RESULTS: Twenty-two out of 5924 DEGs in the TCGA dataset were screened and associated with the overall survival (OS) of LUAD patients. 14CID="C008" value=" "DEGs were finally selected and included in our development and validation model by risk score analysis. The ROC analysis indicated that the specificity and sensitivity of this profile signature were high. Further functional enrichment analyses indicated that these DEGs might regulate genes that affect the function of release of sequestered calcium ion into cytosol and pathways that associated with vibrio cholerae infection. CONCLUSION: Our study developed a novel 14-DEG signature providing more efficient and persuasive prognostic information beyond conventional clinicopathological factors for survival prediction of LUAD patients.
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spelling pubmed-77696752021-01-06 A Prognostic 14-Gene Expression Signature for Lung Adenocarcinoma: A Study Based on TCGA Data Mining Liu, Jie Hou, Shiqiang Wang, Jinyi Chai, Zhengjun Hong, Xuan Zhao, Tian Sun, Zhengliang Bai, Liandi Gao, Hongyan Gao, Jing Chen, Guohan Oxid Med Cell Longev Research Article BACKGROUND: Lung adenocarcinoma (LUAD), a major and fatal subtype of lung cancer, caused lots of mortalities and showed different outcomes in prognosis. This study was to assess key genes and to develop a prognostic signature for the patient therapy with LUAD. METHOD: RNA expression profile and clinical data from 522 LUAD patients were accessed and downloaded from the Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were extracted and analyzed between normal tissues and LUAD samples. Then, a 14-DEG signature was developed and identified for the survival prediction in LUAD patients by means of univariate and multivariate Cox regression analyses. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed to predict the potential biological functions and pathways of these DEGs. RESULTS: Twenty-two out of 5924 DEGs in the TCGA dataset were screened and associated with the overall survival (OS) of LUAD patients. 14CID="C008" value=" "DEGs were finally selected and included in our development and validation model by risk score analysis. The ROC analysis indicated that the specificity and sensitivity of this profile signature were high. Further functional enrichment analyses indicated that these DEGs might regulate genes that affect the function of release of sequestered calcium ion into cytosol and pathways that associated with vibrio cholerae infection. CONCLUSION: Our study developed a novel 14-DEG signature providing more efficient and persuasive prognostic information beyond conventional clinicopathological factors for survival prediction of LUAD patients. Hindawi 2020-12-19 /pmc/articles/PMC7769675/ /pubmed/33414898 http://dx.doi.org/10.1155/2020/8847226 Text en Copyright © 2020 Jie Liu et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Liu, Jie
Hou, Shiqiang
Wang, Jinyi
Chai, Zhengjun
Hong, Xuan
Zhao, Tian
Sun, Zhengliang
Bai, Liandi
Gao, Hongyan
Gao, Jing
Chen, Guohan
A Prognostic 14-Gene Expression Signature for Lung Adenocarcinoma: A Study Based on TCGA Data Mining
title A Prognostic 14-Gene Expression Signature for Lung Adenocarcinoma: A Study Based on TCGA Data Mining
title_full A Prognostic 14-Gene Expression Signature for Lung Adenocarcinoma: A Study Based on TCGA Data Mining
title_fullStr A Prognostic 14-Gene Expression Signature for Lung Adenocarcinoma: A Study Based on TCGA Data Mining
title_full_unstemmed A Prognostic 14-Gene Expression Signature for Lung Adenocarcinoma: A Study Based on TCGA Data Mining
title_short A Prognostic 14-Gene Expression Signature for Lung Adenocarcinoma: A Study Based on TCGA Data Mining
title_sort prognostic 14-gene expression signature for lung adenocarcinoma: a study based on tcga data mining
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7769675/
https://www.ncbi.nlm.nih.gov/pubmed/33414898
http://dx.doi.org/10.1155/2020/8847226
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