Utilizing Computational Modeling to Discover the Patterns of DNA Methylation in Acute Myeloid Leukemia Among Various Racial Populations

Acute myeloid leukemia (AML) is a rapidly occurring disorder that is characterized by the clonal expansion of myeloid progenitors in the bone marrow and the proliferation of immature myeloid cells. Absolute monocyte count (AMC) and absolute lymphocyte count (ALC) have been used in the diagnostic and prognostic predictor of AML. Interestingly, dysregulation of DNA methylation levels is also observed in AML. Data on AML patients were downloaded from the GDC TCGA Acute Myeloid Leukemia (LAML) on xenabrowser.net website. Data exploration andfiltering based on selected genes and racial population groups was performed. DNMT3A, IDH1, IDH2, TET2, FLT3-ITD, NPM1, and WTl are selected as the potential methylation regulation genes in AML with low number of mutation cases. Univariate analysis was done using correlation test while the multivariate analysis was done using a forward step variable selection linear regression model. This study found that the univariate analysis showed a specific methylated gene locations have a role in AML. Moreover, it is also discovered that the highest correlated gene and CpG site for white and non-white populations are different. This study uncovered that monocyte is found to be inversely proportional while the lymphocyte is directly proportional to the methylation level found in certain genes. There may be therapeutic implication from these findings such as the use of demethylating agents.