Browsing by Author "Giwa, Abdulazeez"

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    Computational analysis of multi-omic data for the elucidation of molecular mechanisms of neuroblastoma
    (University of Western Cape, 2021) Giwa, Abdulazeez; Bendou, Hocine
    Neuroblastoma is the most common extracranial solid tumor in childhood. The survival rates of patients with neuroblastoma, especially those in the high-risk category, are still low despite varied therapies. The detailed understanding of the molecular mechanisms underlying the pathogenesis of neuroblastoma is essential to develop better therapeutics and improve the poor survival rates. This study provides a multi-omic analysis of neuroblastoma datasets from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) neuroblastoma project and the Gene Expression Omnibus (GEO) data portals to better understand the molecular mechanisms of neuroblastoma.
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    Identification of novel prognostic markers of survival time in high-risk neuroblastoma using gene expression profiles
    (Impact Journals, 2020) Giwa, Abdulazeez; Fatai, Azeez A.; Gamieldien, Junaid
    Neuroblastoma is the most common extracranial solid tumor in childhood. Patients in high-risk group often have poor outcomes with low survival rates despite several treatment options. This study aimed to identify a genetic signature from gene expression profiles that can serve as prognostic indicators of survival time in patients of high-risk neuroblastoma, and that could be potential therapeutic targets. RNA-seq count data was downloaded from UCSC Xena browser and samples grouped into Short Survival (SS) and Long Survival (LS) groups. Differential gene expression (DGE) analysis, enrichment analyses, regulatory network analysis and machine learning (ML) prediction of survival group were performed. Forty differentially expressed genes (DEGs) were identified including genes involved in molecular function activities essential for tumor proliferation. DEGs used as features for prediction of survival groups included EVX2, NHLH2, PRSS12, POU6F2, HOXD10, MAPK15, RTL1, LGR5, CYP17A1, OR10AB1P, MYH14, LRRTM3, GRIN3A, HS3ST5, CRYAB and NXPH3. An accuracy score of 82% was obtained by the ML classification models. SMIM28 was revealed to possibly have a role in tumor proliferation and aggressiveness. Our results indicate that these DEGs can serve as prognostic indicators of survival in high-risk neuroblastoma patients and will assist clinicians in making better therapeutic and patient management decisions. © 2020 Giwa et al.
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    Predicting amplification of mycn using cpg methylation biomarkers in neuroblastoma
    (Future Science Group, 2021) Giwa, Abdulazeez; Rossouw, Sophia Catherine; Fatai, Azeez
    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.