YDYu-Jun Dai

Yu-Jun Dai

*This is an algorithmically generated author record based on publicly available information.

Current Organizations / Institutes

The organizations/institutes from the scholar's latest article

Previous Organizations / Institutes

The organizations / institutes from the scholar's other articles in the past ten years


Yu-Jun Dai has a total of 14 co-authors. Here are the top co-authors by number of shared publications:

  • Bing Chen

    Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China

    1 shared publication
  • Fang Hu

    Department of Hematologic Oncology, Sun Yat-sen University Cancer Center, Guangzhou 510000, China

    1 shared publication
  • Han Yan

    Department of Hematology, Shanghai Institute of Hematology, RuiJin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, 197 RuiJin Road II, Shanghai, 200025, China

    1 shared publication
  • Jin-Yan Huang

    Department of Hematology, Shanghai Institute of Hematology, RuiJin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, 197 RuiJin Road II, Shanghai, 200025, China

    1 shared publication
  • Pan-Pan Wang

    Department of Hematology, Shanghai Institute of Hematology, RuiJin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, 197 RuiJin Road II, Shanghai, 200025, China

    1 shared publication
  • Sai-Juan Chen

    Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    1 shared publication

Latest Publications

  • Open Access

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

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

    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 Dnmt3aR878H/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 Dnmt3aR878H/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.


  • Open Access

    Gene mutational pattern and expression level in 560 acute myeloid leukemia patients and their clinical relevance

    by Yong-Mei Zhu, Pan-Pan Wang, Jin-Yan Huang, Yun-Shuo Chen, Bing Chen, Yu-Jun Dai, Han Yan, Yi Hu, Wen-Yan Cheng, Ting-Ting Ma, et al.

    Cytogenetic aberrations and gene mutations have long been regarded as independent prognostic markers in AML, both of which can lead to misexpression of some key genes related to hematopoiesis. It is believed that the expression level of the key genes is associated with the treatment outcome of AML. In this study, we analyzed the clinical features and molecular aberrations of 560 newly diagnosed non-M3 AML patients, including mutational status of CEBPA, NPM1, FLT3, C-KIT, NRAS, WT1, DNMT3A, MLL-PTD and IDH1/2, as well as expression levels of MECOM, ERG, GATA2, WT1, BAALC, MEIS1 and SPI1. Certain gene expression levels were associated with the cytogenetic aberration of the disease, especially for MECOM, MEIS1 and BAALC. FLT3, C-KIT and NRAS mutations contained conversed expression profile regarding MEIS1, WT1, GATA2 and BAALC expression, respectively. FLT3, DNMT3A, NPM1 and biallelic CEBPA represented the mutations associated with the prognosis of AML in our group. Higher MECOM and MEIS1 gene expression levels showed a significant impact on complete remission (CR) rate, disease free survival (DFS) and overall survival (OS) both in univariate and multivariate analysis, respectively; and an additive effect could be observed. By systematically integrating gene mutational status results and gene expression profile, we could establish a more refined system to precisely subdivide AML patients into distinct prognostic groups. Gene expression abnormalities contained important biological and clinical informations, and could be integrated into current AML stratification system.