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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.
  • Zhang W; Tumor Research and Therapy Center, Shandong Provincial Hospital Affiliated to Shandong First Medical Unversity, Jinan, China.
  • Yang Z; Shandong First Medical Unversity, Jinan, China.
  • Zhou F; Tumor Research and Therapy Center, Shandong Provincial Hospital Affiliated to Shandong First Medical Unversity, Jinan, China.
  • Wei Y; Tumor Research and Therapy Center, Shandong Provincial Hospital Affiliated to Shandong First Medical Unversity, Jinan, China.
  • Ma X; Tumor Research and Therapy Center, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
Front Endocrinol (Lausanne) ; 13: 935906, 2022.
Article in English | MEDLINE | ID: covidwho-2123396
ABSTRACT

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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Adenocarcinoma of Lung / COVID-19 / COVID-19 Drug Treatment / Lung Neoplasms Type of study: Experimental Studies / Prognostic study / Systematic review/Meta Analysis Limits: Humans Country/Region as subject: North America Language: English Journal: Front Endocrinol (Lausanne) Year: 2022 Document Type: Article Affiliation country: Fendo.2022.935906

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Adenocarcinoma of Lung / COVID-19 / COVID-19 Drug Treatment / Lung Neoplasms Type of study: Experimental Studies / Prognostic study / Systematic review/Meta Analysis Limits: Humans Country/Region as subject: North America Language: English Journal: Front Endocrinol (Lausanne) Year: 2022 Document Type: Article Affiliation country: Fendo.2022.935906