{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T01:04:06Z","timestamp":1780103046116,"version":"3.54.0"},"reference-count":52,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2021,4,12]],"date-time":"2021-04-12T00:00:00Z","timestamp":1618185600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,9,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Current 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\u2009&amp;lt;\u20090.05). We found four key gene modules and hub gene signature based on module membership (MMhub) statistics and protein\u2013protein 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\u2013cytokine 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 &amp;gt;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\u2013gene 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.<\/jats:p>","DOI":"10.1093\/bib\/bbab120","type":"journal-article","created":{"date-parts":[[2021,3,15]],"date-time":"2021-03-15T12:09:42Z","timestamp":1615810182000},"source":"Crossref","is-referenced-by-count":97,"title":["Bioinformatics and machine learning approach identifies potential drug targets and pathways in COVID-19"],"prefix":"10.1093","volume":"22","author":[{"given":"Md Rabiul","family":"Auwul","sequence":"first","affiliation":[{"name":"School of Economics and Statistics, Guangzhou University, Guangzhou 510006, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md Rezanur","family":"Rahman","sequence":"additional","affiliation":[{"name":"Department of Biochemistry and Biotechnology, School of Biomedical Science, Khwaja Yunus Ali University, Sirajgonj-6751, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Esra","family":"Gov","sequence":"additional","affiliation":[{"name":"Department of Bioengineering, Adana Alparslan Turkes Science and Technology University, Adana-01250, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md","family":"Shahjaman","sequence":"additional","affiliation":[{"name":"Department of Statistics, Begum Rokeya University, Rangpur-5400, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad Ali","family":"Moni","sequence":"additional","affiliation":[{"name":"WHO Collaborating Centre on eHealth, UNSW Digital Health, School of Public Health and Community Medicine, Faculty of Medicine, UNSW Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2021,4,12]]},"reference":[{"key":"2021090815124643600_ref1","first-page":"157","article-title":"WHO declares COVID-19 a pandemic","volume":"90","author":"Cucinotta","year":"2020","journal-title":"Acta Biomed"},{"key":"2021090815124643600_ref2","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1016\/S0140-6736(20)30211-7","article-title":"Epidemiological and clinical characteristics of 99 cases of 2019 coronavirus pneumonia in Wuhan, China: a descriptive study","volume":"395","author":"Chen","year":"2020","journal-title":"Lancet"},{"key":"2021090815124643600_ref3","doi-asserted-by":"crossref","first-page":"113661","DOI":"10.1016\/j.eswa.2020.113661","article-title":"A machine learning model to identify early stage symptoms of SARS-Cov-2 infected patients","volume":"160","author":"Ahamad","year":"2020","journal-title":"Expert systems with applications"},{"key":"2021090815124643600_ref4","doi-asserted-by":"publisher","DOI":"10.1101\/2020.03.24.004655","article-title":"SARS-CoV-2 launches a unique transcriptional signature from in vitro, ex vivo, and in vivo systems","author":"Blanco-Melo","year":"2020","journal-title":"bioRxiv"},{"key":"2021090815124643600_ref5","doi-asserted-by":"crossref","first-page":"173594","DOI":"10.1016\/j.ejphar.2020.173594","article-title":"Integrative transcriptomics analysis of lung epithelial cells and identification of repurposable drug candidates for COVID-19","volume":"887","author":"Islam","year":"2020","journal-title":"Eur J Pharmacol"},{"key":"2021090815124643600_ref6","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1080\/22221751.2020.1747363","article-title":"Transcriptomic characteristics of bronchoalveolar lavage fluid and peripheral blood mononuclear cells in COVID-19 patients","volume":"9","author":"Xiong","year":"2020","journal-title":"Emerg Microbes Infect"},{"key":"2021090815124643600_ref7","doi-asserted-by":"crossref","first-page":"1210","DOI":"10.1126\/science.abc6261","article-title":"Systems biological assessment of immunity to mild versus severe COVID-19 infection in humans","volume":"369","author":"Arunachalam","year":"2020","journal-title":"Science (80-)"},{"key":"2021090815124643600_ref8","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1016\/j.chom.2020.03.021","article-title":"A dynamic immune response shapes COVID-19 progression","volume":"27","author":"Ong","year":"2020","journal-title":"Cell Host Microbe"},{"key":"2021090815124643600_ref9","doi-asserted-by":"crossref","first-page":"102571","DOI":"10.1016\/j.autrev.2020.102571","article-title":"Transcriptional landscape of SARS-CoV-2 infection dismantles pathogenic pathways activated by the virus, proposes unique sex-specific differences and predicts tailored therapeutic strategies","volume":"19","author":"Fagone","year":"2020","journal-title":"Autoimmun Rev"},{"key":"2021090815124643600_ref10","doi-asserted-by":"crossref","first-page":"20848","DOI":"10.1038\/s41598-020-77632-8","article-title":"Investigation of COVID-19 comorbidities reveals genes and pathways coincident with the SARS-CoV-2 viral disease","volume":"10","author":"Dolan","year":"2020","journal-title":"Sci Rep"},{"key":"2021090815124643600_ref11","doi-asserted-by":"crossref","first-page":"1415","DOI":"10.1093\/bib\/bbab003","article-title":"Diseasome and comorbidities complexities of SARS-CoV-2 infection with common malignant diseases","volume":"22","author":"Satu","year":"2021","journal-title":"Brief Bioinform"},{"key":"2021090815124643600_ref12","doi-asserted-by":"crossref","first-page":"1324","DOI":"10.1093\/bib\/bbaa376","article-title":"Gene expression profiling of SARS-CoV-2 infections reveal distinct primary lung cell and systemic immune infection responses that identify pathways relevant in COVID-19 disease","volume":"22","author":"Moni","year":"2020","journal-title":"Brief Bioinform"},{"key":"2021090815124643600_ref13","doi-asserted-by":"crossref","first-page":"1387","DOI":"10.1093\/bib\/bbaa426","article-title":"Bioinformatics and system biology approach to identify the influences of COVID-19 on cardiovascular and hypertensive comorbidities","volume":"22","author":"Nashiry","year":"2021","journal-title":"Brief Bioinform"},{"key":"2021090815124643600_ref14","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1186\/1471-2105-9-559","article-title":"WGCNA: an R package for weighted correlation network analysis","volume":"9","author":"Langfelder","year":"2008","journal-title":"BMC Bioinformatics"},{"key":"2021090815124643600_ref15","doi-asserted-by":"crossref","first-page":"134950","DOI":"10.1016\/j.neulet.2020.134950","article-title":"Weighted gene co-expression network analysis reveals specific modules and biomarkers in Parkinson \u2019 s disease","volume":"728","author":"Jin","year":"2020","journal-title":"Neurosci Lett"},{"key":"2021090815124643600_ref16","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1186\/s12864-017-3761-z","article-title":"Associating transcriptional modules with colon cancer survival through weighted gene co-expression network analysis","volume":"18","author":"Iu","year":"2017","journal-title":"BMC Genomics"},{"key":"2021090815124643600_ref17","first-page":"1","article-title":"Weighted gene coexpression network analysis identified MicroRNA coexpression modules and related pathways in type 2 diabetes mellitus","volume":"2019","author":"Feng","year":"2019","journal-title":"Oxid Med Cell Longev"},{"key":"2021090815124643600_ref18","doi-asserted-by":"crossref","first-page":"991","DOI":"10.1093\/nar\/gks1193","article-title":"NCBI GEO: archive for functional genomics data sets\u2014update","volume":"41","author":"Barrett","year":"2013","journal-title":"Nucleic Acids Res"},{"key":"2021090815124643600_ref19","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1186\/s13059-014-0550-8","article-title":"Oderated estimation of fold change and dispersion for RNA-seq data with DESeq2","volume":"15","author":"Love","year":"2014","journal-title":"Geneome Biol"},{"key":"2021090815124643600_ref20","doi-asserted-by":"crossref","first-page":"e47","DOI":"10.1093\/nar\/gkv007","article-title":"Limma powers differential expression analyses for RNA-sequencing and microarray studies","volume":"43","author":"Ritchie","year":"2015","journal-title":"Nucleic Acids Res"},{"key":"2021090815124643600_ref21","doi-asserted-by":"crossref","first-page":"e1001057","DOI":"10.1371\/journal.pcbi.1001057","article-title":"Is my network module preserved and reproducible?","volume":"7","author":"Langfelder","year":"2011","journal-title":"PLoS Comput Biol"},{"key":"2021090815124643600_ref22","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbaa365","article-title":"Bioinformatics and machine learning methodologies to identify the effects of central nervous system disorders on glioblastoma progression","author":"Rahman","year":"2021","journal-title":"Brief Bioinform"},{"key":"2021090815124643600_ref23","doi-asserted-by":"crossref","first-page":"237","DOI":"10.3390\/genes12020237","article-title":"Identification of common pathogenetic processes between schizophrenia and diabetes mellitus by systems biology analysis","volume":"12","author":"Rahman","year":"2021","journal-title":"Genes (Basel)"},{"key":"2021090815124643600_ref24","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1038\/nprot.2008.211","article-title":"Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources","volume":"4","author":"Huang","year":"2009","journal-title":"Nat Protoc"},{"key":"2021090815124643600_ref25","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1089\/omi.2011.0118","article-title":"clusterProfiler: an R package for comparing biological themes among gene clusters","volume":"16","author":"Yu","year":"2012","journal-title":"Omi A J Integr Biol"},{"key":"2021090815124643600_ref26","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.gdata.2017.10.006","article-title":"Co-expression network analysis identi fi ed six hub genes in association with progression and prognosis in human clear cell renal cell carcinoma (ccRCC)","volume":"14","author":"Yuan","year":"2017","journal-title":"Genomics Data"},{"key":"2021090815124643600_ref27","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1145\/130385.130401","volume-title":"Proc. 5th Annu. Work. Comput. Learn. theory","author":"Boser","year":"1992"},{"key":"2021090815124643600_ref28","first-page":"278","volume-title":"Proc. Int. Conf. Doc. Anal. Recognition, ICDAR","author":"Ho","year":"1995"},{"key":"2021090815124643600_ref29","first-page":"2493","article-title":"Classification and clustering of sequencing data using a Poisson model","volume-title":"Annals of Applied Statistics","author":"Witten","year":"2011"},{"key":"2021090815124643600_ref30","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1186\/s12859-016-1208-1","article-title":"NBLDA: negative binomial linear discriminant analysis for RNA-Seq data","volume":"17","author":"Dong","year":"2016","journal-title":"BMC Bioinf"},{"key":"2021090815124643600_ref31","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1198\/016214502753479248","article-title":"Comparison of discrimination methods for the classification of tumors using gene expression data","volume":"97","author":"Dudoit","year":"2002","journal-title":"J Am Stat Assoc"},{"key":"2021090815124643600_ref32","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.cmpb.2019.04.007","article-title":"MLSeq: machine learning interface for RNA-sequencing data","volume":"175","author":"Goksuluk","year":"2019","journal-title":"Comput Methods Programs Biomed"},{"key":"2021090815124643600_ref33","doi-asserted-by":"crossref","first-page":"D362","DOI":"10.1093\/nar\/gkw937","article-title":"The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible","volume":"45","author":"Szklarczyk","year":"2017","journal-title":"Nucleic Acids Res"},{"key":"2021090815124643600_ref34","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1093\/bioinformatics\/btq675","article-title":"Cytoscape 2.8: new features for data integration and network visualization","volume":"27","author":"Smoot","year":"2011","journal-title":"Bioinformatics"},{"key":"2021090815124643600_ref35","doi-asserted-by":"crossref","first-page":"D260","DOI":"10.1093\/nar\/gkx1126","article-title":"JASPAR 2018: update of the open-access database of transcription factor binding profiles and its web framework","volume":"46","author":"Khan","year":"2018","journal-title":"Nucleic Acids Res"},{"key":"2021090815124643600_ref36","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1038\/nprot.2015.052","article-title":"NetworkAnalyst for statistical, visual and network-based meta-analysis of gene expression data","volume":"10","author":"Xia","year":"2015","journal-title":"Nat Protoc"},{"key":"2021090815124643600_ref37","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1261\/rna.2239606","article-title":"TarBase: a comprehensive database of experimentally supported animal microRNA targets","volume":"12","author":"Sethupathy","year":"2006","journal-title":"RNA"},{"key":"2021090815124643600_ref38","doi-asserted-by":"crossref","first-page":"D163","DOI":"10.1093\/nar\/gkq1107","article-title":"miRTarBase: a database curates experimentally validated microRNA\u2013target interactions","volume":"39","author":"Hsu","year":"2011","journal-title":"Nucleic Acids Res"},{"key":"2021090815124643600_ref39","doi-asserted-by":"crossref","first-page":"20","DOI":"10.3390\/medicina55010020","article-title":"Identification of prognostic biomarker signatures and candidate drugs in colorectal cancer: insights from systems biology analysis","volume":"55","author":"Rahman","year":"2019","journal-title":"Medicina (Kaunas)"},{"key":"2021090815124643600_ref40","doi-asserted-by":"crossref","first-page":"2150","DOI":"10.1093\/bioinformatics\/bty060","article-title":"L1000FWD: fireworks visualization of drug-induced transcriptomic signatures","volume":"34","author":"Wang","year":"2018","journal-title":"Bioinformatics"},{"key":"2021090815124643600_ref41","article-title":"Network bioinformatics analysis provides insight into drug repurposing for COVID-2019","author":"Li","year":"2019"},{"key":"2021090815124643600_ref42","doi-asserted-by":"crossref","first-page":"882","DOI":"10.1093\/bib\/bbaa155","article-title":"Virus-CKB: an integrated bioinformatics platform and analysis resource for COVID-19 research","volume":"22","author":"Feng","year":"2020","journal-title":"Brief Bioinform"},{"key":"2021090815124643600_ref43","doi-asserted-by":"crossref","first-page":"3350","DOI":"10.1093\/bioinformatics\/btaa160","article-title":"HLPpred-fuse: improved and robust prediction of hemolytic peptide and its activity by fusing multiple feature representation","volume":"36","author":"Hasan","year":"2020","journal-title":"Bioinformatics"},{"key":"2021090815124643600_ref44","doi-asserted-by":"crossref","DOI":"10.1016\/j.gpb.2019.04.004","article-title":"iLBE for computational identification of linear B-cell epitopes by integrating sequence and evolutionary features","author":"Hasan","year":"2020","journal-title":"Genomics Proteomics Bioinformatics"},{"key":"2021090815124643600_ref45","article-title":"Meta-i6mA: an interspecies predictor for identifying DNA N6-methyladenine sites of plant genomes by exploiting informative features in an integrative machine-learning framework","author":"Hasan","year":"2020","journal-title":"Brief Bioinform"},{"key":"2021090815124643600_ref46","first-page":"e1000525","article-title":"PLK1 down-regulates parainfluenza virus 5 gene expression","volume":"5","author":"Sun","year":"2009","journal-title":"PLoS One"},{"key":"2021090815124643600_ref47","doi-asserted-by":"crossref","first-page":"11277","DOI":"10.18632\/aging.103524","article-title":"Re-analysis of SARS-CoV-2-infected host cell proteomics time-course data by impact pathway analysis and network analysis: a potential link with inflammatory response","volume":"12","author":"Bock","year":"2020","journal-title":"Aging (Albany NY)"},{"key":"2021090815124643600_ref48","doi-asserted-by":"crossref","first-page":"943","DOI":"10.3389\/fvets.2020.586826","article-title":"A mini-review on cell cycle regulation of coronavirus infection","volume":"7","author":"Su","year":"2020","journal-title":"Front Vet Sci"},{"key":"2021090815124643600_ref49","doi-asserted-by":"crossref","DOI":"10.1155\/2019\/1245072","article-title":"CDK1, CCNB1, CDC20, BUB1, MAD2L1, MCM3, BUB1B, MCM2, and RFC4 may be potential therapeutic targets for hepatocellular carcinoma using integrated bioinformatic analysis","volume":"2019","author":"Yang","year":"2019","journal-title":"Biomed Res Int"},{"key":"2021090815124643600_ref50","article-title":"Efficacy of addition of naproxen in the treatment of critically ill patients hospitalized for COVID-19 infection (ENACOVID)","year":"2020"},{"key":"2021090815124643600_ref51","doi-asserted-by":"crossref","first-page":"106177","DOI":"10.1016\/j.ijantimicag.2020.106177","article-title":"Design of novel viral attachment inhibitors of the spike glycoprotein (S) of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) through virtual screening and dynamics","volume":"56","author":"Oany","year":"2020","journal-title":"International journal of antimicrobial agents"},{"key":"2021090815124643600_ref52","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/07391102.2020.1842807","article-title":"SARS-CoV-2 Mpro inhibitors: identification of anti-SARS-CoV-2 Mpro compounds from FDA approved drugs","volume":"38","author":"Bharadwaj","year":"2020","journal-title":"J Biomol Struct Dyn"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/bib\/article-pdf\/22\/5\/bbab120\/40261413\/bbab120.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"http:\/\/academic.oup.com\/bib\/article-pdf\/22\/5\/bbab120\/40261413\/bbab120.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,8]],"date-time":"2021-09-08T15:18:47Z","timestamp":1631114327000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbab120\/6220170"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,12]]},"references-count":52,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2021,9,2]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbab120","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2021,9]]},"published":{"date-parts":[[2021,4,12]]},"article-number":"bbab120"}}