Predicting amplification of mycn using cpg methylation biomarkers in neuroblastoma

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dc.contributor.authorGiwa, Abdulazeez
dc.contributor.authorRossouw, Sophia Catherine
dc.contributor.authorFatai, Azeez
dc.date.accessioned2023-06-07T09:33:34Z
dc.date.available2023-06-07T09:33:34Z
dc.date.issued2021
dc.description.abstractNeuroblastoma is the most common extracranial solid tumor in childhood. Amplification of MYCN in neuroblastoma is a predictor of poor prognosis. Materials and methods: DNA methylation data from the TARGET data matrix were stratified into MYCN amplified and non-amplified groups. Differential methylation analysis, clustering, recursive feature elimination (RFE), machine learning (ML), Cox regression analysis and Kaplan–Meier estimates were performed. Results and Conclusion: 663 CpGs were differentially methylated between the two groups. A total of 25 CpGs were selected by RFE for clustering and ML, and a 100% clustering accuracy was obtained. ML validation on three external datasets produced high accuracy scores of 100%, 97% and 93%. Eight survival-associated CpGs were also identified. Therapeutic interventions may need to be targeted to patient subgroups.en_US
dc.identifier.citationGiwa, A. et al. (2021). Predicting amplification of mycn using cpg methylation biomarkers in neuroblastoma. Future Oncology, 17 (34) , 4769-4783. https://doi.org/10.2217/fon-2021-0522en_US
dc.identifier.issn1744-8301
dc.identifier.urihttps://doi.org/10.2217/fon-2021-0522
dc.identifier.urihttp://hdl.handle.net/10566/9056
dc.language.isoenen_US
dc.publisherFuture Science Groupen_US
dc.subjectNeuroblastomaen_US
dc.subjectBioinformaticsen_US
dc.subjectBiochemistryen_US
dc.subjectMachine learningen_US
dc.titlePredicting amplification of mycn using cpg methylation biomarkers in neuroblastomaen_US
dc.typeArticleen_US

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