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:	Li Gao, Guo-Sheng Li, Jian-Di Li, Juan He, Yu Zhang, Hua-Fu Zhou, Jin-Liang Kong, Gang Chen
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	LUAD COVID-19 DEG Susceptibility Immune infiltration WGCNA Biotechnology TP248.13-248.65
Acceso en línea:	https://doaj.org/article/d87b240f10c3459ab66d41658ff89469
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spelling	oai:doaj.org-article:d87b240f10c3459ab66d41658ff894692021-11-26T04:26:56ZIdentification of the susceptibility genes for COVID-19 in lung adenocarcinoma with global data and biological computation methods2001-037010.1016/j.csbj.2021.11.026https://doaj.org/article/d87b240f10c3459ab66d41658ff894692021-01-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2001037021004918https://doaj.org/toc/2001-0370Introduction: 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.Li GaoGuo-Sheng LiJian-Di LiJuan HeYu ZhangHua-Fu ZhouJin-Liang KongGang ChenElsevierarticleLUADCOVID-19DEGSusceptibilityImmune infiltrationWGCNABiotechnologyTP248.13-248.65ENComputational and Structural Biotechnology Journal, Vol 19, Iss , Pp 6229-6239 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	LUAD COVID-19 DEG Susceptibility Immune infiltration WGCNA Biotechnology TP248.13-248.65
spellingShingle	LUAD COVID-19 DEG Susceptibility Immune infiltration WGCNA Biotechnology TP248.13-248.65 Li Gao Guo-Sheng Li Jian-Di Li Juan He Yu Zhang Hua-Fu Zhou Jin-Liang Kong Gang Chen Identification of the susceptibility genes for COVID-19 in lung adenocarcinoma with global data and biological computation methods
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.
format	article
author	Li Gao Guo-Sheng Li Jian-Di Li Juan He Yu Zhang Hua-Fu Zhou Jin-Liang Kong Gang Chen
author_facet	Li Gao Guo-Sheng Li Jian-Di Li Juan He Yu Zhang Hua-Fu Zhou Jin-Liang Kong Gang Chen
author_sort	Li Gao
title	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_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_sort	identification of the susceptibility genes for covid-19 in lung adenocarcinoma with global data and biological computation methods
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/d87b240f10c3459ab66d41658ff89469
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_version_	1718409932624101376

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

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