Browsing by Author "Livesey, Michelle"

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    Investigation of distinct gene expression profile patterns that can improve the classification of intermediate-risk prognosis in AML patients
    (Frontiers Media, 2023) Eshibona, Nasr; Livesey, Michelle; Christoffels, Alan
    Acute myeloid leukemia (AML) is a heterogeneous type of blood cancer that generally affects the elderly. AML patients are categorized with favorable-, intermediate-, and adverse-risks based on an individual’s genomic features and chromosomal abnormalities. Despite the risk stratification, the progression and outcome of the disease remain highly variable. To facilitate and improve the risk stratification of AML patients, the study focused on gene expression profiling of AML patients within various risk categories. Therefore, the study aims to establish gene signatures that can predict the prognosis of AML patients and find correlations in gene expression profile patterns that are associated with risk groups. Microarray data were obtained from Gene Expression Omnibus (GSE6891). The patients were stratified into four subgroups based on risk and overall survival. Limma was applied to screen for differentially expressed genes (DEGs) between short survival (SS) and long survival (LS). DEGs strongly related to general survival were discovered using Cox regression and LASSO analysis. To assess the model’s accuracy, Kaplan-Meier (K-M) and receiver operating characteristic (ROC) were used.
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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.