Evaluating a rapid method to quantify multiple gene expression in acute myeloid leukaemia

Acute myeloid leukaemia (AML) is a clonal disease characterized by the proliferation and accumulation of myeloid progenitor cells in the bone marrow and peripheral blood. Prognostic markers are needed to predict patient’s response to therapy and reduce under- or over-treatment. Age, white blood counts and cytogenetics are currently routine prognostic markers but are limited by the test and the number of patients who can benefit from it. Large scale gene profiling methods have further identified genetic aberrations such as microdeletions and mutations associated with pathogenesis of disease and outcome. Thus, further screening and confirmation using reliable and accurate high-throughput methods are required. A pool of genes with potential markers was isolated from good and poor prognosis patients in the previous study, where subtractive hybridisation was applied on randomly selected patient based on their prognosis. The expressions of these markers were then evaluated on leukaemia cell lines. This study, aims to further examine the expression of these markers on AML samples and confirm its prognostic properties. Thirty newly diagnosed acute myeloid leukaemia (AML) cases were included in the study. The diagnosis and treatment outcome of these cases were retrieved from haematological reports and clinical data from the hospital involved. Nineteen potential genes identified from earlier study and four housekeeping genes were selected and the level of expression was determined using the GeXP method and confirmed using the real-time PCR method. Results revealed that several genes including CALM2, CSTB and TMSB4X may be related to good prognosis while SON, PGK1 and SF3B1 may be related to poor prognostic in acute myeloid leukaemias. The GeXP method has several advantages over real-time PCR including the requirement for less samples, simultaneous optimization and examination of all target and reference genes in a reaction and no requirement for repeated standard curves which are all important considerations to the study of clinical samples. This study has provided much insight into reliable techniques for selection of biomarkers and identified potential prognostic markers for acute myeloid leukaemias. This will in turn lead towards more-directed therapy for patients.

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