A 19-Gene Signature of Serous Ovarian Cancer Identified by Machine Learning and Systems Biology: Prospects for Diagnostics and Personalized Medicine

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dc.authoridArga, Kazim Yalcin/0000-0002-6036-1348
dc.authoridKori, Medi/0000-0002-4589-930X
dc.authoridGOV, ESRA/0000-0002-5256-4778
dc.contributor.authorKori, Medi
dc.contributor.authorDemirtas, Talip Yasir
dc.contributor.authorComertpay, Betul
dc.contributor.authorArga, Kazim Yalcin
dc.contributor.authorSinha, Raghu
dc.contributor.authorGov, Esra
dc.date.accessioned2025-01-06T17:44:20Z
dc.date.available2025-01-06T17:44:20Z
dc.date.issued2024
dc.description.abstractOvarian cancer is a major cause of cancer deaths among women. Early diagnosis and precision/personalized medicine are essential to reduce mortality and morbidity of ovarian cancer, as with new molecular targets to accelerate drug discovery. We report here an integrated systems biology and machine learning (ML) approach based on the differential coexpression analysis to identify candidate systems biomarkers (i.e., gene modules) for serous ovarian cancer. Accordingly, four independent transcriptome datasets were statistically analyzed independently and common differentially expressed genes (DEGs) were identified. Using these DEGs, coexpressed gene pairs were unraveled. Subsequently, differential coexpression networks between the coexpressed gene pairs were reconstructed so as to identify the differentially coexpressed gene modules. Based on the established criteria, SOV-module was identified as being significant, consisting of 19 genes. Using independent datasets, the diagnostic capacity of the SOV-module was evaluated using principal component analysis (PCA) and ML techniques. PCA showed a sensitivity and specificity of 96.7% and 100%, respectively, and ML analysis showed an accuracy of up to 100% in distinguishing phenotypes in the present study sample. The prognostic capacity of the SOV-module was evaluated using survival and ML analyses. We found that the SOV-module's performance for prognostics was significant (p-value = 1.36 x 10-4) with an accuracy of 63% in discriminating between survival and death using ML techniques. In summary, the reported genomic systems biomarker candidate offers promise for personalized medicine in diagnosis and prognosis of serous ovarian cancer and warrants further experimental and translational clinical studies.
dc.description.sponsorshipYOK 100/2000 Doctoral Fellowship Program
dc.description.sponsorshipThe scholarship under the YOK 100/2000 Doctoral Fellowship Program provided to Betul Comertpay is greatly acknowledged.
dc.identifier.doi10.1089/omi.2023.0273
dc.identifier.endpage101
dc.identifier.issn1536-2310
dc.identifier.issn1557-8100
dc.identifier.issue2
dc.identifier.pmid38320250
dc.identifier.startpage90
dc.identifier.urihttps://doi.org/10.1089/omi.2023.0273
dc.identifier.urihttps://hdl.handle.net/20.500.14669/3008
dc.identifier.volume28
dc.identifier.wosWOS:001157008900001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
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.subjectserous ovarian cancer
dc.subjectpersonalized medicine
dc.subjectwomen's health
dc.subjectsystems biomarkers
dc.subjectdiagnostics
dc.subjectprognosis
dc.titleA 19-Gene Signature of Serous Ovarian Cancer Identified by Machine Learning and Systems Biology: Prospects for Diagnostics and Personalized Medicine
dc.typeArticle

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