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Network Pharmacology and Bioinformatics Analysis Identifies Potential Therapeutic Targets of Paxlovid Against LUAD/COVID-19
BACKGROUND: Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a pandemic in many countries around the world. The virus is highly contagious and has a high fatality rate. Lung adenocarcinoma (LUAD) patients may have higher suscepti...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9493477/ https://www.ncbi.nlm.nih.gov/pubmed/36157452 http://dx.doi.org/10.3389/fendo.2022.935906 |
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author | Zhang, Wentao Yang, Zhe Zhou, Fengge Wei, Yanjun Ma, Xiaoqing |
author_facet | Zhang, Wentao Yang, Zhe Zhou, Fengge Wei, Yanjun Ma, Xiaoqing |
author_sort | Zhang, Wentao |
collection | PubMed |
description | BACKGROUND: Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a pandemic in many countries around the world. The virus is highly contagious and has a high fatality rate. Lung adenocarcinoma (LUAD) patients may have higher susceptibility and mortality to COVID-19. While Paxlovid is the first oral drug approved by the U.S. Food and Drug Administration (FDA) for COVID-19, its specific drug mechanism for lung cancer patients infected with COVID-19 remains to be further studied. METHODS: COVID-19 related genes were obtained from NCBI, GeneCards, and KEGG, and then the transcriptome data for LUAD was downloaded from TCGA. The drug targets of Paxlovid were revealed through BATMAN-TCM, DrugBank, SwissTargetPrediction, and TargetNet. The genes related to susceptibility to COVID-19 in LUAD patients were obtained through differential analysis. The interaction of LUAD/COVID-19 related genes was evaluated and displayed by STRING, and a COX risk regression model was established to screen and evaluate the correlation between genes and clinical characteristics. The Venn diagram was drawn to select the candidate targets of Paxlovid against LUAD/COVID-19, and the functional analysis of the target genes was performed using KEGG and GO enrichment analysis. Finally, Cytoscape was used to screen and visualize the Hub Gene, and Autodock was used for molecular docking between the drug and the target. RESULT: Bioinformatics analysis was performed by combining COVID-19-related genes with the gene expression and clinical data of LUAD, including analysis of prognosis-related genes, survival rate, and hub genes screened out by the prognosis model. The key targets of Paxlovid against LUAD/COVID-19 were obtained through network pharmacology, the most important targets include IL6, IL12B, LBP. Furthermore, pathway analysis showed that Paxlovid modulates the IL-17 signaling pathway, the cytokine-cytokine receptor interaction, during LUAD/COVID-19 treatment. CONCLUSIONS: Based on bioinformatics and network pharmacology, the prognostic signature of LUAD/COVID-19 patients was screened. And identified the potential therapeutic targets and molecular pathways of Paxlovid Paxlovid in the treatment of LUAD/COVID. As promising features, prognostic signatures and therapeutic targets shed light on improving the personalized management of patients with LUAD. |
format | Online Article Text |
id | pubmed-9493477 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-94934772022-09-23 Network Pharmacology and Bioinformatics Analysis Identifies Potential Therapeutic Targets of Paxlovid Against LUAD/COVID-19 Zhang, Wentao Yang, Zhe Zhou, Fengge Wei, Yanjun Ma, Xiaoqing Front Endocrinol (Lausanne) Endocrinology BACKGROUND: Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a pandemic in many countries around the world. The virus is highly contagious and has a high fatality rate. Lung adenocarcinoma (LUAD) patients may have higher susceptibility and mortality to COVID-19. While Paxlovid is the first oral drug approved by the U.S. Food and Drug Administration (FDA) for COVID-19, its specific drug mechanism for lung cancer patients infected with COVID-19 remains to be further studied. METHODS: COVID-19 related genes were obtained from NCBI, GeneCards, and KEGG, and then the transcriptome data for LUAD was downloaded from TCGA. The drug targets of Paxlovid were revealed through BATMAN-TCM, DrugBank, SwissTargetPrediction, and TargetNet. The genes related to susceptibility to COVID-19 in LUAD patients were obtained through differential analysis. The interaction of LUAD/COVID-19 related genes was evaluated and displayed by STRING, and a COX risk regression model was established to screen and evaluate the correlation between genes and clinical characteristics. The Venn diagram was drawn to select the candidate targets of Paxlovid against LUAD/COVID-19, and the functional analysis of the target genes was performed using KEGG and GO enrichment analysis. Finally, Cytoscape was used to screen and visualize the Hub Gene, and Autodock was used for molecular docking between the drug and the target. RESULT: Bioinformatics analysis was performed by combining COVID-19-related genes with the gene expression and clinical data of LUAD, including analysis of prognosis-related genes, survival rate, and hub genes screened out by the prognosis model. The key targets of Paxlovid against LUAD/COVID-19 were obtained through network pharmacology, the most important targets include IL6, IL12B, LBP. Furthermore, pathway analysis showed that Paxlovid modulates the IL-17 signaling pathway, the cytokine-cytokine receptor interaction, during LUAD/COVID-19 treatment. CONCLUSIONS: Based on bioinformatics and network pharmacology, the prognostic signature of LUAD/COVID-19 patients was screened. And identified the potential therapeutic targets and molecular pathways of Paxlovid Paxlovid in the treatment of LUAD/COVID. As promising features, prognostic signatures and therapeutic targets shed light on improving the personalized management of patients with LUAD. Frontiers Media S.A. 2022-09-08 /pmc/articles/PMC9493477/ /pubmed/36157452 http://dx.doi.org/10.3389/fendo.2022.935906 Text en Copyright © 2022 Zhang, Yang, Zhou, Wei and Ma https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Endocrinology Zhang, Wentao Yang, Zhe Zhou, Fengge Wei, Yanjun Ma, Xiaoqing Network Pharmacology and Bioinformatics Analysis Identifies Potential Therapeutic Targets of Paxlovid Against LUAD/COVID-19 |
title | Network Pharmacology and Bioinformatics Analysis Identifies Potential Therapeutic Targets of Paxlovid Against LUAD/COVID-19 |
title_full | Network Pharmacology and Bioinformatics Analysis Identifies Potential Therapeutic Targets of Paxlovid Against LUAD/COVID-19 |
title_fullStr | Network Pharmacology and Bioinformatics Analysis Identifies Potential Therapeutic Targets of Paxlovid Against LUAD/COVID-19 |
title_full_unstemmed | Network Pharmacology and Bioinformatics Analysis Identifies Potential Therapeutic Targets of Paxlovid Against LUAD/COVID-19 |
title_short | Network Pharmacology and Bioinformatics Analysis Identifies Potential Therapeutic Targets of Paxlovid Against LUAD/COVID-19 |
title_sort | network pharmacology and bioinformatics analysis identifies potential therapeutic targets of paxlovid against luad/covid-19 |
topic | Endocrinology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9493477/ https://www.ncbi.nlm.nih.gov/pubmed/36157452 http://dx.doi.org/10.3389/fendo.2022.935906 |
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