A Differential Transcriptional Regulome Approach to Unpack Cancer Biology: Insights on Renal Cell Carcinoma Subtypes

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dc.authoridCaliskan Iscan, Aysegul/0000-0003-1887-9167
dc.authoridArga, Kazim Yalcin/0000-0002-6036-1348
dc.contributor.authorCaliskan, Aysegul
dc.contributor.authorArga, Kazim Yalcin
dc.date.accessioned2024-05-19T14:39:46Z
dc.date.available2024-05-19T14:39:46Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractCancer research calls for new approaches that account for the regulatory complexities of biology. We present, in this study, the differential transcriptional regulome (DIFFREG) approach for the identification and prioritization of key transcriptional regulators and apply it to the case of renal cell carcinoma (RCC) biology. Of note, RCC has a poor prognosis and the biomarker and drug discovery studies to date have tended to focus on gene expression independent from mutations and/or post-translational modifications. DIFFREG focuses on the differential regulation between transcription factors (TFs) and their target genes rather than differential gene expression and integrates transcriptome profiling with the human transcriptional regulatory network to analyze differential gene regulation between healthy and RCC cases. In this study, RNA-seq tissue samples (n = 1020) from the Cancer Genome Atlas (TCGA), including healthy and tumor subjects, were integrated with a comprehensive human TF-gene interactome dataset (1122603 interactions between 1289 TFs and 25177 genes). Comparative analysis of DIFFREG profiles, consisting of perturbed TF-gene interactions, from three common subtypes (clear cell RCC, papillary RCC and chromophobe RCC) revealed subtype-specific alterations, supporting the hypothesis that these signatures in the transcriptional regulome profiles may be considered potential biomarkers that may play an important role in elucidating the molecular mechanisms of RCC development and translating knowledge about the genetic basis of RCC into the clinic. In addition, these indicators may help oncologists make the best decisions for diagnosis and prognosis management.en_US
dc.identifier.doi10.1089/omi.2023.0167
dc.identifier.endpage545en_US
dc.identifier.issn1536-2310
dc.identifier.issn1557-8100
dc.identifier.issue11en_US
dc.identifier.pmid37943533en_US
dc.identifier.scopus2-s2.0-85177238881en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage536en_US
dc.identifier.urihttps://doi.org10.1089/omi.2023.0167
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4845
dc.identifier.volume27en_US
dc.identifier.wosWOS:001104521400004en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherMary Ann Liebert, Incen_US
dc.relation.ispartofOmics-A Journal of Integrative Biologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectCancer Biologyen_US
dc.subjectDifferential Regulomeen_US
dc.subjectRegulomicsen_US
dc.subjectRenal Cell Carcinomaen_US
dc.subjectRegulatory Genomicsen_US
dc.titleA Differential Transcriptional Regulome Approach to Unpack Cancer Biology: Insights on Renal Cell Carcinoma Subtypesen_US
dc.typeArticleen_US

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