Giwa, AbdulazeezRossouw, Sophia CatherineFatai, Azeez2023-06-072023-06-072021Giwa, 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-2021-05221744-8301https://doi.org/10.2217/fon-2021-0522http://hdl.handle.net/10566/9056Neuroblastoma 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.enNeuroblastomaBioinformaticsBiochemistryMachine learningArticle