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Biography

Soo-Young Yoon has completed her MD, Ph.D course from Korea University. She is a professor of College of Medicine, Korea University and work with KUMC Guro Hospital, Seoul, Korea. She has published more than 75 papers in reputed journals and is serving as a board member of Korean Society of Laboratory Medicine.

Abstract

Acute myeloid leukemia (AML) is known to be very heterogeneous and for successful treatment, early diagnosis and classification into discrete risk categories is important. To identify miRNA expression signatures that can better predict survivals of AML patients, we analyzed the molecular genetic markers, especially focusing on the effect of the presence of IDH1 mutations on the prognosis of AML patients and their corresponding miRNA-seq expression profiles derived from The Cancer Genome Atlas (TCGA) AML dataset (n=200). We used cytogenetically normal-risk AML (CN-AML) patients as a training set to identify IDH1 mutation-specific miRNA signatures and for a testing set, cytogenetically poor-risk AML (CP-AML) patients with IDH1 wild-type were used. Using significance analysis of microarray (SAM) method on CN-AML dataset, we identified 12 significant miRNA signatures. As a result, it showed more favorable survival outcomes of CN-AML with IDH1 mutations than IDH1 wild-type (p = 0.046). For validation, a risk score was calculated based on the expression of the 12 miRNA signatures, the testing samples were divided into low-risk group and high-risk group. As a result, low-risk group showed higher expression of 5 protective miRNAs and also more favorable outcomes than the high-risk group with higher expression of 7 risky miRNAs. Therefore, our twelve IDH1 mutation-specific miRNA signatures have successfully predicted and identified a subgroup of testing samples, showing similarly favorable outcomes among the CP-AML with IDH1 wild-type genes (p =0.062). These findings may add prognostic or therapeutic implications for the future evaluation of those IDH1 mutation-specific miRNA signatures in AML patients.

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