Introduction: Quantitative polymerase chain reaction (qPCR) is commonly used in the investigation of acute myeloid leukaemias (AML). Stable reference genes (RG) are essential for accurate and reliable reporting but no standard method for selection has been endorsed. Materials and Methods: We evaluated simple statistics and published model-based approaches. Multiplex-qPCR was conducted to determine the expression of 24 candidate RG in AMLs (N=9). Singleplex-qPCR was carried out on selected RG (SRP14, B2M and ATP5B) and genes of interest in AML (N=15) and healthy controls, HC (N=12). Results: RG expression levels in AML samples were highly variable and coefficient of variance (CV) ranged from 0.37% to 10.17%. Analysis using GeNorm and Normfinder listed different orders of most stable genes but the top seven (ACTB, UBE2D2, B2M, NF45, RPL37A, GK, QARS) were the same. In singleplex-qPCR, SRP14 maintained the lowest CV in AML samples. B2M, one of most stable reference genes in AML, was expressed near significantly different in AML and HC. GeNorm selected ATP5B+SRP14 while Normfinder chose SRP14+B2M as the best two RG in combination. The median expressions of combined RG genes in AML compared to HC were less significantly different than individually implying smaller expression variation after combination. Genes of interest normalised with RG in combination or individually, displayed significantly different expression patterns. Conclusions: The selection of best reference gene in qPCR must consider all sample sets. Model-based approaches are important in large candidate gene analysis. This study showed combination of RG SRP14+B2M was the most suitable normalisation factor for qPCR analysis of AML and healthy individuals.

Introduction: Drug-resistance is a major hindrance to successful treatment of AML. Current predictive biomarkers are mainly genetic aberrations and insufficient in foretelling treatment outcome in all acute myeloid leukaemia (AML) due to its heterogeneous and aggressive nature. Proteins are stable and reliable. Secreted proteins in AML may have predictive or prognostic values for early intervention. Proteomic studies on AML are few and further investigations will benefit in selection of best markers. The aim of the study was to identify differentially expressed plasma proteins in AML with different treatment outcome. Methods: Two-dimensional electrophoresis (2-DE) technique was utilised to identify proteins differentially expressed in chemo-sensitive/chemo-resistant AML. Plasma and peripheral blood mononuclear cell (PBMC) lysate proteome analysis were performed on six chemo-resistant, four chemo-sensitive and six healthy controls and seven chemo-resistant, three chemo-sensitive and six healthy controls, respectively. Each experiment was conducted in duplicate or triplicate. Images were captured and protein spots detected by software. Differentially expressed protein spots were excised from gel and proteins were identified using LC/MS/MS. Proteins spots that were also detected in healthy controls were excluded. Results: Comparing mean % volume of each spot demonstrated significantly enhanced expression of apoliprotein-E (APO-E) and haptoglobin (HP) (p<0.05) in plasma and HNRNP H1 (p=0.049) in cell lysate of chemo-sensitive group. Serotransferrin (STF) from plasma and DNA-PK from cell lysate (p=0.01) were associated with chemo-resistance. Conclusion: This preliminary study identified several potential predictive biomarkers associated with chemo-resistance/chemo-sensitivity to treatment in AML. Further studies with a larger number of samples are required to validate the results.