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Identification of the susceptibility genes for COVID-19 in lung adenocarcinoma with global data and biological computation methods

INTRODUCTION: The risk of infection with COVID-19 is high in lung adenocarcinoma (LUAD) patients, and there is a dearth of studies on the molecular mechanism underlying the high susceptibility of LUAD patients to COVID-19 from the perspective of the global differential expression landscape. OBJECTIV...

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Autores principales: Gao, Li, Li, Guo-Sheng, Li, Jian-Di, He, Juan, Zhang, Yu, Zhou, Hua-Fu, Kong, Jin-Liang, Chen, Gang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Research Network of Computational and Structural Biotechnology 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8605816/
https://www.ncbi.nlm.nih.gov/pubmed/34840672
http://dx.doi.org/10.1016/j.csbj.2021.11.026
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author Gao, Li
Li, Guo-Sheng
Li, Jian-Di
He, Juan
Zhang, Yu
Zhou, Hua-Fu
Kong, Jin-Liang
Chen, Gang
author_facet Gao, Li
Li, Guo-Sheng
Li, Jian-Di
He, Juan
Zhang, Yu
Zhou, Hua-Fu
Kong, Jin-Liang
Chen, Gang
author_sort Gao, Li
collection PubMed
description INTRODUCTION: The risk of infection with COVID-19 is high in lung adenocarcinoma (LUAD) patients, and there is a dearth of studies on the molecular mechanism underlying the high susceptibility of LUAD patients to COVID-19 from the perspective of the global differential expression landscape. OBJECTIVES: To fill the research void on the molecular mechanism underlying the high susceptibility of LUAD patients to COVID-19 from the perspective of the global differential expression landscape. METHODS: Herein, we identified genes, specifically the differentially expressed genes (DEGs), correlated with the susceptibility of LUAD patients to COVID-19. These were obtained by calculating standard mean deviation (SMD) values for 49 SARS-CoV-2-infected LUAD samples and 24 non-affected LUAD samples, as well as 3931 LUAD samples and 3027 non-cancer lung samples from 40 pooled RNA-seq and microarray datasets. Hub susceptibility genes significantly related to COVID-19 were further selected by weighted gene co-expression network analysis. Then, the hub genes were further analyzed via an examination of their clinical significance in multiple datasets, a correlation analysis of the immune cell infiltration level, and their interactions with the interactome sets of the A549 cell line. RESULTS: A total of 257 susceptibility genes were identified, and these genes were associated with RNA splicing, mitochondrial functions, and proteasomes. Ten genes, MEA1, MRPL24, PPIH, EBNA1BP2, MRTO4, RABEPK, TRMT112, PFDN2, PFDN6, and NDUFS3, were confirmed to be the hub susceptibility genes for COVID-19 in LUAD patients, and the hub susceptibility genes were significantly correlated with the infiltration of multiple immune cells. CONCLUSION: In conclusion, the susceptibility genes for COVID-19 in LUAD patients discovered in this study may increase our understanding of the high risk of COVID-19 in LUAD patients.
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spelling pubmed-86058162021-11-22 Identification of the susceptibility genes for COVID-19 in lung adenocarcinoma with global data and biological computation methods Gao, Li Li, Guo-Sheng Li, Jian-Di He, Juan Zhang, Yu Zhou, Hua-Fu Kong, Jin-Liang Chen, Gang Comput Struct Biotechnol J Research Article INTRODUCTION: The risk of infection with COVID-19 is high in lung adenocarcinoma (LUAD) patients, and there is a dearth of studies on the molecular mechanism underlying the high susceptibility of LUAD patients to COVID-19 from the perspective of the global differential expression landscape. OBJECTIVES: To fill the research void on the molecular mechanism underlying the high susceptibility of LUAD patients to COVID-19 from the perspective of the global differential expression landscape. METHODS: Herein, we identified genes, specifically the differentially expressed genes (DEGs), correlated with the susceptibility of LUAD patients to COVID-19. These were obtained by calculating standard mean deviation (SMD) values for 49 SARS-CoV-2-infected LUAD samples and 24 non-affected LUAD samples, as well as 3931 LUAD samples and 3027 non-cancer lung samples from 40 pooled RNA-seq and microarray datasets. Hub susceptibility genes significantly related to COVID-19 were further selected by weighted gene co-expression network analysis. Then, the hub genes were further analyzed via an examination of their clinical significance in multiple datasets, a correlation analysis of the immune cell infiltration level, and their interactions with the interactome sets of the A549 cell line. RESULTS: A total of 257 susceptibility genes were identified, and these genes were associated with RNA splicing, mitochondrial functions, and proteasomes. Ten genes, MEA1, MRPL24, PPIH, EBNA1BP2, MRTO4, RABEPK, TRMT112, PFDN2, PFDN6, and NDUFS3, were confirmed to be the hub susceptibility genes for COVID-19 in LUAD patients, and the hub susceptibility genes were significantly correlated with the infiltration of multiple immune cells. CONCLUSION: In conclusion, the susceptibility genes for COVID-19 in LUAD patients discovered in this study may increase our understanding of the high risk of COVID-19 in LUAD patients. Research Network of Computational and Structural Biotechnology 2021-11-20 /pmc/articles/PMC8605816/ /pubmed/34840672 http://dx.doi.org/10.1016/j.csbj.2021.11.026 Text en © 2021 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research Article
Gao, Li
Li, Guo-Sheng
Li, Jian-Di
He, Juan
Zhang, Yu
Zhou, Hua-Fu
Kong, Jin-Liang
Chen, Gang
Identification of the susceptibility genes for COVID-19 in lung adenocarcinoma with global data and biological computation methods
title Identification of the susceptibility genes for COVID-19 in lung adenocarcinoma with global data and biological computation methods
title_full Identification of the susceptibility genes for COVID-19 in lung adenocarcinoma with global data and biological computation methods
title_fullStr Identification of the susceptibility genes for COVID-19 in lung adenocarcinoma with global data and biological computation methods
title_full_unstemmed Identification of the susceptibility genes for COVID-19 in lung adenocarcinoma with global data and biological computation methods
title_short Identification of the susceptibility genes for COVID-19 in lung adenocarcinoma with global data and biological computation methods
title_sort identification of the susceptibility genes for covid-19 in lung adenocarcinoma with global data and biological computation methods
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8605816/
https://www.ncbi.nlm.nih.gov/pubmed/34840672
http://dx.doi.org/10.1016/j.csbj.2021.11.026
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