Network medicine approaches for identification of novel prognostic systems biomarkers and drug candidates for papillary thyroid carcinoma

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dc.authoridGOV, ESRA/0000-0002-5256-4778
dc.authoridKori, Medi/0000-0002-4589-930X
dc.authoridTemiz, Kubra/0000-0002-3660-3204
dc.contributor.authorKori, Medi
dc.contributor.authorTemiz, Kubra
dc.contributor.authorGov, Esra
dc.date.accessioned2025-01-06T17:37:00Z
dc.date.available2025-01-06T17:37:00Z
dc.date.issued2023
dc.description.abstractPapillary thyroid carcinoma (PTC) is one of the most common endocrine carcinomas worldwide and the aetiology of this cancer is still not well understood. Therefore, it remains important to understand the disease mechanism and find prognostic biomarkers and/or drug candidates for PTC. Compared with approaches based on single-gene assessment, network medicine analysis offers great promise to address this need. Accordingly, in the present study, we performed differential co-expressed network analysis using five transcriptome datasets in patients with PTC and healthy controls. Following meta-analysis of the transcriptome datasets, we uncovered common differentially expressed genes (DEGs) for PTC and, using these genes as proxies, found a highly clustered differentially expressed co-expressed module: a 'PTC-module'. Using independent data, we demonstrated the high prognostic capacity of the PTC-module and designated this module as a prognostic systems biomarker. In addition, using the nodes of the PTC-module, we performed drug repurposing and text mining analyzes to identify novel drug candidates for the disease. We performed molecular docking simulations, and identified: 4-demethoxydaunorubicin hydrochloride, AS605240, BRD-A60245366, ER 27319 maleate, sinensetin, and TWS119 as novel drug candidates whose efficacy was also confirmed by in silico analyzes. Consequently, we have highlighted here the need for differential co-expression analysis to gain a systems-level understanding of a complex disease, and we provide candidate prognostic systems biomarker and novel drugs for PTC.
dc.description.sponsorshipTurkiye Saglimath;k Enstituleri Bascedil;kanlimath;gimath; [2019-TA-01/3436]
dc.description.sponsorshipTurkiye Sagl & imath;k Enstituleri Ba & scedil;kanl & imath;g & imath;, Grant/Award Number: 2019-TA-01/3436
dc.identifier.doi10.1111/jcmm.18002
dc.identifier.endpage4180
dc.identifier.issn1582-1838
dc.identifier.issn1582-4934
dc.identifier.issue24
dc.identifier.pmid37859510
dc.identifier.scopus2-s2.0-85174236506
dc.identifier.scopusqualityQ1
dc.identifier.startpage4171
dc.identifier.urihttps://doi.org/10.1111/jcmm.18002
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2079
dc.identifier.volume27
dc.identifier.wosWOS:001085884200001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherWiley
dc.relation.ispartofJournal of Cellular and Molecular Medicine
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241211
dc.subjectco-expressed module
dc.subjectdifferential co-expression network analysis
dc.subjectdrug repurposing
dc.subjectpapillary thyroid carcinoma
dc.subjectprognostic systems biomarkers
dc.subjecttext mining
dc.titleNetwork medicine approaches for identification of novel prognostic systems biomarkers and drug candidates for papillary thyroid carcinoma
dc.typeArticle

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