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Identification of key candidate genes associated with prognosis of lung adenocarcinoma by integrated bioinformatical analysis

BACKGROUND: Lung adenocarcinoma (LUAD) is the most frequent histologic type of lung cancer and the morbidity of LUAD is increasing rapidly in the worldwide. But the mechanism of LUAD is still largely unknown. METHODS: In this study, we analyzed three microarrays of gene expression profiles, containi...

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Autores principales: Li, Jinghang, Li, Yanxiu, Jin, Min, Huang, Lin, Wang, Xiaowei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8798186/
https://www.ncbi.nlm.nih.gov/pubmed/35117293
http://dx.doi.org/10.21037/tcr-20-2110
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author Li, Jinghang
Li, Yanxiu
Jin, Min
Huang, Lin
Wang, Xiaowei
author_facet Li, Jinghang
Li, Yanxiu
Jin, Min
Huang, Lin
Wang, Xiaowei
author_sort Li, Jinghang
collection PubMed
description BACKGROUND: Lung adenocarcinoma (LUAD) is the most frequent histologic type of lung cancer and the morbidity of LUAD is increasing rapidly in the worldwide. But the mechanism of LUAD is still largely unknown. METHODS: In this study, we analyzed three microarrays of gene expression profiles, containing 196 LUAD samples and 137 normal samples, to explore the potential key candidate genes in LUAD by integrated bioinformatical analysis. RESULTS: A total of 240 shared differentially expressed genes (DEGs) were identified and pathways enrichment were analyzed. DEGs-associated protein-protein interaction (PPI) network was constructed and top 20 hub genes were established by calculating the degree of connectivity. We further validated these genes in TCGA and GTEx projects, and found all of these hub genes were differentially expressed in LUAD patients except TIMP1 and FOS. In these candidate genes, ten genes (TPX2, CENPF, TYMS, PRC1, NEK2, CCNB2, KIAA0101, CDC20, TOP2A and SPP1) were confirmed to associate with the prognosis of LUAD. Out of these ten genes, CENPF had the highest genetic alteration at a rate of 4% in LUAD patients, and the expression of CENPF was significantly increased in different subgroups of all age, gender, race, smoking condition and cancer stage groups of LUAD patients. CONCLUSIONS: Our study contributes to comprehend the role of genes in LUAD and provides possible therapeutic targets for further clinical application.
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spelling pubmed-87981862022-02-02 Identification of key candidate genes associated with prognosis of lung adenocarcinoma by integrated bioinformatical analysis Li, Jinghang Li, Yanxiu Jin, Min Huang, Lin Wang, Xiaowei Transl Cancer Res Original Article BACKGROUND: Lung adenocarcinoma (LUAD) is the most frequent histologic type of lung cancer and the morbidity of LUAD is increasing rapidly in the worldwide. But the mechanism of LUAD is still largely unknown. METHODS: In this study, we analyzed three microarrays of gene expression profiles, containing 196 LUAD samples and 137 normal samples, to explore the potential key candidate genes in LUAD by integrated bioinformatical analysis. RESULTS: A total of 240 shared differentially expressed genes (DEGs) were identified and pathways enrichment were analyzed. DEGs-associated protein-protein interaction (PPI) network was constructed and top 20 hub genes were established by calculating the degree of connectivity. We further validated these genes in TCGA and GTEx projects, and found all of these hub genes were differentially expressed in LUAD patients except TIMP1 and FOS. In these candidate genes, ten genes (TPX2, CENPF, TYMS, PRC1, NEK2, CCNB2, KIAA0101, CDC20, TOP2A and SPP1) were confirmed to associate with the prognosis of LUAD. Out of these ten genes, CENPF had the highest genetic alteration at a rate of 4% in LUAD patients, and the expression of CENPF was significantly increased in different subgroups of all age, gender, race, smoking condition and cancer stage groups of LUAD patients. CONCLUSIONS: Our study contributes to comprehend the role of genes in LUAD and provides possible therapeutic targets for further clinical application. AME Publishing Company 2020-11 /pmc/articles/PMC8798186/ /pubmed/35117293 http://dx.doi.org/10.21037/tcr-20-2110 Text en 2020 Translational Cancer Research. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.
spellingShingle Original Article
Li, Jinghang
Li, Yanxiu
Jin, Min
Huang, Lin
Wang, Xiaowei
Identification of key candidate genes associated with prognosis of lung adenocarcinoma by integrated bioinformatical analysis
title Identification of key candidate genes associated with prognosis of lung adenocarcinoma by integrated bioinformatical analysis
title_full Identification of key candidate genes associated with prognosis of lung adenocarcinoma by integrated bioinformatical analysis
title_fullStr Identification of key candidate genes associated with prognosis of lung adenocarcinoma by integrated bioinformatical analysis
title_full_unstemmed Identification of key candidate genes associated with prognosis of lung adenocarcinoma by integrated bioinformatical analysis
title_short Identification of key candidate genes associated with prognosis of lung adenocarcinoma by integrated bioinformatical analysis
title_sort identification of key candidate genes associated with prognosis of lung adenocarcinoma by integrated bioinformatical analysis
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8798186/
https://www.ncbi.nlm.nih.gov/pubmed/35117293
http://dx.doi.org/10.21037/tcr-20-2110
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