Epigenetic landscape analysis of lncRNAs in acute myeloid leukemia with DNMT3A mutations

Yu-Jun Dai; Fang Hu; Si-Yuan He; Yue-Ying Wang

doi:10.21037/atm.2020.02.143

Epigenetic landscape analysis of lncRNAs in acute myeloid leukemia with DNMT3A mutations

Ann Transl Med. 2020 Mar;8(6):318. doi: 10.21037/atm.2020.02.143.

Authors

Yu-Jun Dai^{1

2

3}, Fang Hu^{1

2

3}, Si-Yuan He⁴, Yue-Ying Wang⁵

Affiliations

¹ Department of Hematologic Oncology, Sun Yat-sen University Cancer Center, Guangzhou 510000, China.
² Center State Key Laboratory of Oncology in South China, Guangzhou 510000, China.
³ Collaborative Innovation Center for Cancer Medicine, Guangzhou 510000, China.
⁴ The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA.
⁵ State Key Laboratory of Medical Genomics, Shanghai Institute of Hematology, RuiJin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.

Abstract

Background: Acute myeloid leukemia (AML) is a type of cancer that consists of a group of hematological malignancies with high heterogeneity. DNA methyltransferase 3A (DNMT3A)-mutated AML patients have a poor prognosis. Some long non-coding RNAs (lncRNAs) have been reported to enhance therapeutic sensitivity, and so could affect the overall survival rate of elderly cytogenetically normal acute myeloid leukemia (CN-AML) patients; however, studies on the lncRNA signature in DNMT3A-mutated AML are rare.

Method: The DNMT3A R878H conditional knock-in mouse model was constructed to explore the lncRNAs of DNMT3A mutation by using the Cuffcomparison method. Cis and trans regulation networks were used to predict candidate genes. The expression levels in leukemic cell lines and the prognostic index of these candidate genes were analyzed with the Broad Institute Cancer Cell Line Encyclopedia (CCLE) and OncoLnc databases. The data for each sample were statistically analyzed using GraphPad Prism.

Results: In this study, we applied the DNMT3A R878H conditional knock-in mouse model to explore the lncRNA epigenetic landscape of DNMT3A mutation by using the Cuffcomparison method. Twenty-three differentially expressed lncRNAs were identified in Dnmt3a^R878H/WTMx1-Cre⁺ mice. We next predicted the downstream targetable genes regulated by these lncRNAs through cis and trans regulation networks and found 124 candidate genes are related to these lncRNAs. In further analysis of 124 genes, we found that increased mRNA expression levels of interleukin 1 receptor type 2 (IL1R2), Krüppel-like factor 13 (KLF13), ATPase H+ transporting V1 subunit A (ATP6V1A), proteasome 26S Subunit, non-ATPase 3 (PSMD3), and pyrroline-5-carboxylate reductase 2 (PYCR2) were associated with poor prognosis in AML. Functional analysis of these genes demonstrated that the pathways involved in autophagy, cell cycle, and hematopoietic stem cell differentiation were more enriched in Dnmt3a^R878H/WTMx1-Cre⁺ mice.

Conclusion: Our study was the first to use DNMT3A R878H conditional knock-in mouse model to predict the specific lncRNAs regulated by the DNMT3A mutation in AML. Six candidate genes were found to be associated with DNMT3A mutation with poor prognosis. Our results provided a possible treatment strategy for this disease.

Keywords: DNMT3A R878H; LncRNA; RNA-seq; knock-in mice.