Differential co-expression network analysis elucidated genes associated with sensitivity to farnesyltransferase inhibitor and prognosis of acute myeloid leukemia

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dc.authorid0000-0002-4589-930X
dc.authorid0000-0003-1686-3866
dc.authorid0000-0002-6036-1348
dc.authorid0000-0001-8961-5113
dc.contributor.authorKeleşoğlu, Nurdan
dc.contributor.authorKori, Medi
dc.contributor.authorYılmaz, Betül Karademir
dc.contributor.authorDuru, Özlem Ateş
dc.contributor.authorArga, Kazım Yalçın
dc.date.accessioned2024-09-25T19:56:09Z
dc.date.available2024-09-25T19:56:09Z
dc.date.issued2023
dc.departmentBAİBÜ, Mühendislik Fakültesi, Kimya Mühendisliği Bölümü
dc.description.abstractAcute myeloid leukemia (AML) is a heterogeneous disease and the most common form of acute leukemia with a poor prognosis. Due to its complexity, the disease requires the identification of biomarkers for reliable prognosis. To identify potential disease genes that regulate patient prognosis, we used differential co-expression network analysis and transcriptomics data from relapsed, refractory, and previously untreated AML patients based on their response to treatment in the present study. In addition, we combined functional genomics and transcriptomics data to identify novel and therapeutically potential systems biomarkers for patients who do or do not respond to treatment. As a result, we constructed co-expression networks for response and non-response cases and identified a highly interconnected group of genes consisting of SECISBP2L, MAN1A2, PRPF31, VASP, and SNAPC1 in the response network and a group consisting of PHTF2, SLC11A2, PDLIM5, OTUB1, and KLRD1 in the non-response network, both of which showed high prognostic performance with hazard ratios of 4.12 and 3.66, respectively. Remarkably, ETS1, GATA2, AR, YBX1, and FOXP3 were found to be important transcription factors in both networks. The prognostic indicators reported here could be considered as a resource for identifying tumorigenesis and chemoresistance to farnesyltransferase inhibitor. They could help identify important research directions for the development of new prognostic and therapeutic techniques for AML.en_US
dc.description.sponsorshipHealth Institute of Turkey (TUSEB)en_US
dc.description.sponsorshipNo Statement Availableen_US
dc.identifier.doi10.1002/cam4.6804
dc.identifier.endpage22436en_US
dc.identifier.issn2045-7634
dc.identifier.issue24en_US
dc.identifier.pmid38069522en_US
dc.identifier.scopus2-s2.0-85179354517en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage22420en_US
dc.identifier.urihttps://doi.org/10.1002/cam4.6804
dc.identifier.urihttps://hdl.handle.net/20.500.12491/13148
dc.identifier.volume12en_US
dc.identifier.wosWOS:001117136000001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorKeleşoğlu, Nurdan
dc.language.isoenen_US
dc.publisherWileyen_US
dc.relation.ispartofCancer Medicineen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzYK_20240925en_US
dc.subjectAcute Myeloid Leukemiaen_US
dc.subjectBiomarkersen_US
dc.subjectNetwork Biomedicineen_US
dc.subjectTherapeuticsen_US
dc.subjectTranscriptomicsen_US
dc.titleDifferential co-expression network analysis elucidated genes associated with sensitivity to farnesyltransferase inhibitor and prognosis of acute myeloid leukemiaen_US
dc.typeArticleen_US

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