{"title":"The Tp53 Gene And Covid 19 Virus: A Correlation Analysis","authors":"L. C, K. P K","doi":"10.2174/1875692119666220617160537","DOIUrl":null,"url":null,"abstract":"\n\nTo discover the most effective anti-cancer medicine for cancer patients who are infected with SARS-Cov-2.\n\n\n\nThe correlation between TP53 and SARS-CoV-2 was discovered using biomolecular networking analysis.\n\n\n\nCancer patients with TP53 gene mutations are more likely to be infected with the SARS-Cov-2 virus since it is the most frequently mutated tumor suppressor gene in human cancer. The main goal of this study is to discover the most effective and efficient anti-cancer therapy for patients with SARS-Cov-2 infection.\n\n\n\nTopp gene analysis was used to prioritize candidate genes based on molecular function, biological process, and pathway analysis. Biomolecular networking was carried out using Cytoscape 2.8.2. The Protein-protein Interaction network was used to identify the functionally associated proteins. Protein-Drug Interaction network was used to observe the molecular therapeutic efficiency of drugs. The network was further analyzed using Cytohubba to find the hub nodes. The molecular docking was used to study the protein-ligand interaction and the protein-ligand complex was further evaluated through molecular dynamic simulation to determine its stability.\n\n\n\nFunctionally relevant genes were prioritized through Toppgene analysis. Through Cytohabba study it was found that the genes UBE2N, BRCA1, BARD1, TP53, and DPP4 was having a high degree and centrality score. The drugs 5-fluorouracil, Methotrexate, Temozolomide, Favipiravir, and Levofloxacin have a substantial association with the hub protei, according to protein-drug interaction analysis. Finally, a docking study revealed that 5-fluorouracil have the highest connection value and stability when compared to Methotrexate, Favipiravir, and Levofloxacin.\n\n\n\nThe biomolecular networking study used to discover the link between TP53 and SARS-CoV-2 found that 5-fluorouracil, had a higher affinity for binding to TP53 and its related genes, such as UBE2N, BRCA1, RARD1, and SARS-CoV-2 specific DPP4. For cancer patients with TP53 gene mutations and covid 19 infection, these treatments were determined to be the most effective.\n","PeriodicalId":11056,"journal":{"name":"Current Pharmacogenomics and Personalized Medicine","volume":"18 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Pharmacogenomics and Personalized Medicine","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2174/1875692119666220617160537","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Pharmacology, Toxicology and Pharmaceutics","Score":null,"Total":0}
引用次数: 0
Abstract
To discover the most effective anti-cancer medicine for cancer patients who are infected with SARS-Cov-2.
The correlation between TP53 and SARS-CoV-2 was discovered using biomolecular networking analysis.
Cancer patients with TP53 gene mutations are more likely to be infected with the SARS-Cov-2 virus since it is the most frequently mutated tumor suppressor gene in human cancer. The main goal of this study is to discover the most effective and efficient anti-cancer therapy for patients with SARS-Cov-2 infection.
Topp gene analysis was used to prioritize candidate genes based on molecular function, biological process, and pathway analysis. Biomolecular networking was carried out using Cytoscape 2.8.2. The Protein-protein Interaction network was used to identify the functionally associated proteins. Protein-Drug Interaction network was used to observe the molecular therapeutic efficiency of drugs. The network was further analyzed using Cytohubba to find the hub nodes. The molecular docking was used to study the protein-ligand interaction and the protein-ligand complex was further evaluated through molecular dynamic simulation to determine its stability.
Functionally relevant genes were prioritized through Toppgene analysis. Through Cytohabba study it was found that the genes UBE2N, BRCA1, BARD1, TP53, and DPP4 was having a high degree and centrality score. The drugs 5-fluorouracil, Methotrexate, Temozolomide, Favipiravir, and Levofloxacin have a substantial association with the hub protei, according to protein-drug interaction analysis. Finally, a docking study revealed that 5-fluorouracil have the highest connection value and stability when compared to Methotrexate, Favipiravir, and Levofloxacin.
The biomolecular networking study used to discover the link between TP53 and SARS-CoV-2 found that 5-fluorouracil, had a higher affinity for binding to TP53 and its related genes, such as UBE2N, BRCA1, RARD1, and SARS-CoV-2 specific DPP4. For cancer patients with TP53 gene mutations and covid 19 infection, these treatments were determined to be the most effective.
期刊介绍：
Current Pharmacogenomics and Personalized Medicine (Formerly ‘Current Pharmacogenomics’) Current Pharmacogenomics and Personalized Medicine (CPPM) is an international peer reviewed biomedical journal that publishes expert reviews, and state of the art analyses on all aspects of pharmacogenomics and personalized medicine under a single cover. The CPPM addresses the complex transdisciplinary challenges and promises emerging from the fusion of knowledge domains in therapeutics and diagnostics (i.e., theragnostics). The journal bears in mind the increasingly globalized nature of health research and services.
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