Bioinformatics and machine learning approach identifies potential drug targets and pathways in COVID-19

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dc.authoridMoni, Mohammad Ali/0000-0003-0756-1006
dc.authoridAuwul, Dr. Md. Rabiul/0000-0003-2505-5422
dc.contributor.authorAuwul, Md Rabiul
dc.contributor.authorRahman, Md Rezanur
dc.contributor.authorGov, Esra
dc.contributor.authorShahjaman, Md
dc.contributor.authorMoni, Mohammad Ali
dc.date.accessioned2025-01-06T17:37:15Z
dc.date.available2025-01-06T17:37:15Z
dc.date.issued2021
dc.description.abstractCurrent coronavirus disease-2019 (COVID-19) pandemic has caused massive loss of lives. Clinical trials of vaccines and drugs are currently being conducted around the world; however, till now no effective drug is available for COVID-19. Identification of key genes and perturbed pathways in COVID-19 may uncover potential drug targets and biomarkers. We aimed to identify key gene modules and hub targets involved in COVID-19. We have analyzed SARS-CoV-2 infected peripheral blood mononuclear cell (PBMC) transcriptomic data through gene coexpression analysis. We identified 1520 and 1733 differentially expressed genes (DEGs) from the GSE152418 and CRA002390 PBMC datasets, respectively (FDR < 0.05). We found four key gene modules and hub gene signature based on module membership (MMhub) statistics and protein-protein interaction (PPI) networks (PPIhub). Functional annotation by enrichment analysis of the genes of these modules demonstrated immune and inflammatory response biological processes enriched by the DEGs. The pathway analysis revealed the hub genes were enriched with the IL-17 signaling pathway, cytokine-cytokine receptor interaction pathways. Then, we demonstrated the classification performance of hub genes (PLK1, AURKB, AURKA, CDK1, CDC20, KIF11, CCNB1, KIF2C, DTL and CDC6) with accuracy >0.90 suggesting the biomarker potential of the hub genes. The regulatory network analysis showed transcription factors and microRNAs that target these hub genes. Finally, drug-gene interactions analysis suggests amsacrine, BRD-K68548958, naproxol, palbociclib and teniposide as the top-scored repurposed drugs. The identified biomarkers and pathways might be therapeutic targets to the COVID-19.
dc.identifier.doi10.1093/bib/bbab120
dc.identifier.issn1467-5463
dc.identifier.issn1477-4054
dc.identifier.issue5
dc.identifier.pmid33839760
dc.identifier.scopus2-s2.0-85108942105
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1093/bib/bbab120
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2164
dc.identifier.volume22
dc.identifier.wosWOS:000709461800142
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherOxford Univ Press
dc.relation.ispartofBriefings in Bioinformatics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241211
dc.subjectCOVID-19
dc.subjectdifferentially expressed genes
dc.subjectgene coexpression network
dc.subjectsystems biology
dc.subjectprotein-protein interaction
dc.subjectmachine learning
dc.titleBioinformatics and machine learning approach identifies potential drug targets and pathways in COVID-19
dc.typeArticle

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