Novel Genomic Biomarker Candidates for Cervical Cancer As Identified by Differential Co-Expression Network Analysis

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dc.authoridKori, Medi/0000-0002-4589-930X
dc.authoridArga, Kazim Yalcin/0000-0002-6036-1348
dc.contributor.authorKori, Medi
dc.contributor.authorGov, Esra
dc.contributor.authorArga, Kazim Yalcin
dc.date.accessioned2025-01-06T17:36:57Z
dc.date.available2025-01-06T17:36:57Z
dc.date.issued2019
dc.description.abstractCervical cancer is the second most common malignancy and the third reason for mortality among women in developing countries. Although infection by the oncogenic human papilloma viruses is a major cause, genomic contributors are still largely unknown. Network analyses, compared with candidate gene studies, offer greater promise to map the interactions among genomic loci contributing to cervical cancer risk. We report here a differential co-expression network analysis in five gene expression datasets (GSE7803, GSE9750, GSE39001, GSE52903, and GSE63514, from the Gene Expression Omnibus) in patients with cervical cancer and healthy controls. Kaplan-Meier Survival and principle component analyses were employed to evaluate prognostic and diagnostic performances of biomarker candidates, respectively. As a result, seven distinct co-expressed gene modules were identified. Among these, five modules (with sizes of 9-45 genes) presented high prognostic and diagnostic capabilities with hazard ratios of 2.28-11.3, and diagnostic odds ratios of 85.2-548.8. Moreover, these modules were associated with several key biological processes such as cell cycle regulation, keratinization, neutrophil degranulation, and the phospholipase D signaling pathway. In addition, transcription factors ETS1 and GATA2 were noted as common regulatory elements. These genomic biomarker candidates identified by differential co-expression network analysis offer new prospects for translational cancer research, not to mention personalized medicine to forecast cervical cancer susceptibility and prognosis. Looking into the future, we also suggest that the search for a molecular basis of common complex diseases should be complemented by differential co-expression analyses to obtain a systems-level understanding of disease phenotype variability.
dc.identifier.doi10.1089/omi.2019.0025
dc.identifier.endpage273
dc.identifier.issn1536-2310
dc.identifier.issn1557-8100
dc.identifier.issue5
dc.identifier.pmid31038390
dc.identifier.scopus2-s2.0-85065869977
dc.identifier.scopusqualityQ2
dc.identifier.startpage261
dc.identifier.urihttps://doi.org/10.1089/omi.2019.0025
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2044
dc.identifier.volume23
dc.identifier.wosWOS:000466554500001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherMary Ann Liebert, Inc
dc.relation.ispartofOmics-A Journal of Integrative Biology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectcervical cancer
dc.subjectgenomics
dc.subjectbiomarkers
dc.subjectdiagnostics
dc.subjectpersonalized medicine
dc.subjectdifferential co-expression network analysis
dc.titleNovel Genomic Biomarker Candidates for Cervical Cancer As Identified by Differential Co-Expression Network Analysis
dc.typeArticle

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