In 2018 alone, an estimated 20,000 new acute myeloid leukemia (AML) patients were diagnosed, in the United States, and over 10,000 of them are expected to die from the disease. Although AML can occur in people of all ages, AML is primarily diagnosed among the elderly (median 68 years old at diagnosis) and its age-specific incidence and prevalence increases exponentially after 50 years of age. Prognoses have significantly improved for younger patients, but in patients older than 60 years old, prognoses remain grim: with current treatments, as much as 70% of patients will die within a year of diagnosis. Reassessment of early diagnosis and treatment approaches therefore should be considered, since relapse after complete remission is still the main obstacle. In this study, we conducted stratified computational meta-analysis of 2,213 AML patients compared to 548 healthy individuals, using curated publicly available data. We carried out analysis of variance of normalized batch corrected data, including considerations for disease, age, tissue and sex. We identified 964 differentially expressed unique genes genes and 4 associated significant pathways involved in AML. Additionally, we have identified 69 sex- and 372 age-related gene expression signatures relevant to AML. Finally, we used a machine learning model (KNN model) to classify AML patients compared to healthy individuals with > 90% achieved accuracy. Overall our findings provide a new reanalysis of public datasets, that enabled the identification of potential new gene sets relevant to AML that can potentially be used in future experiments and possible stratified disease diagnostics.
Read
