| Autor: |
Giwa A; SAMRC Bioinformatics Unit, South African National Bioinformatics Institute, University of the Western Cape, Bellville, 7535, South Africa., Rossouw SC; SAMRC Bioinformatics Unit, South African National Bioinformatics Institute, University of the Western Cape, Bellville, 7535, South Africa., Fatai A; Department of Biochemistry, Lagos State University, Nigeria., Gamieldien J; SAMRC Bioinformatics Unit, South African National Bioinformatics Institute, University of the Western Cape, Bellville, 7535, South Africa., Christoffels A; SAMRC Bioinformatics Unit, South African National Bioinformatics Institute, University of the Western Cape, Bellville, 7535, South Africa., Bendou H; SAMRC Bioinformatics Unit, South African National Bioinformatics Institute, University of the Western Cape, Bellville, 7535, South Africa. |
| Abstrakt: |
Background: Neuroblastoma 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. |
