Browsing by Author "Rossouw, Sophia Catherine"

Now showing 1 - 3 of 3
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    Optimisation of proteomics techniques for archival tumour blocks of a South African cohort of colorectal cancer
    (University of Western Cape, 2020) Rossouw, Sophia Catherine; Christoffels, Alan; Rigby, Jonathan
    Tumour-specific protein markers are usually present at elevated concentrations in patient biopsy tissue; therefore tumour tissue is an ideal biological material for studying cancer proteomics and biomarker discovery studies. To understand and elucidate cancer pathogenesis and its mechanisms at the molecular level, the collection and characterisation of a large number of individual patient tissue cohorts are required. Since most pathology institutes routinely preserve biopsy tissues by standardised methods of formalin fixation and paraffin embedment, these archived, FFPE tissues are important collections of pathology material, often accompanied by important metadata, such as patient medical history and treatments. FFPE tissue blocks are conveniently stored under ambient conditions for decades, while retaining cellular morphology due to the modifications induced by formalin.
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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.
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    Transforming RNA-Seq gene expression to track cancer progression in the multi-stage early to advanced-stage cancer development
    (Public Library of Science, 2023) Livesey, Michelle; Rossouw, Sophia Catherine; Blignaut, Renette
    Cancer progression can be tracked by gene expression changes that occur throughout early-stage to advanced-stage cancer development. The accumulated genetic changes can be detected when gene expression levels in advanced-stage are less variable but show high variability in early-stage. Normalizing advanced-stage expression samples with earlystage and clustering of the normalized expression samples can reveal cancers with similar or different progression and provide insight into clinical and phenotypic patterns of patient samples within the same cancer.