RESUMO
BACKGROUND: Immediate early response 3 (IER3) plays a vital role in many tumors. This study aims to explore the function and mechanism of IER3 in Acute myeloid leukemia (AML). METHODS: The expression of IER3 in AML was performed by bioinformatics analysis. CCK-8 proliferation assay, flow cytometry cycle assay, clone formation assay, and tumorigenic ability were used to investigate the effect of IER3 on AML cells. Unbiased label-free quantitative proteomics and label-free quantitative phosphoproteomics analysis were performed. The regulatory relationship between SATB1(Special AT-rich sequence binding protein 1) and IER3 was investigated by Real time-PCR, Western blot, Chromatin immunoprecipitation (CHIP), and PCR. RESULTS: The result indicated that the prognosis of the high IER3 expression group was significantly worse than that of the low expression group. CCK-8 assay showed that IER3 enhanced the proliferation ability. Cell cycle analysis showed IER3 could promote HL60 cells to enter the S phase of DNA synthesis from the quiescent phase. IER3 could stimulate HEL cells to enter mitosis. Clone-formation experiments suggested that IER3 enhanced clonogenic ability.IER3 promoted the tumorigenesis of AML. Further experimental investigation revealed that IER3 promoted autophagy and induced the occurrence and development of AML by negatively regulating the phosphorylation activation of AKT/mTOR pathway. SATB1 was found to bind to the promoter region of IER3 gene and negatively regulate its transcription. CONCLUSION: IER3 could promote the development of AML and induce autophagy of AML cells by negatively regulating the phosphorylation and activation of AKT/mTOR. By the way, SATB1 may negatively target regulates IER3 transcription.
RESUMO
BACKGROUND: Runt-related transcription factor 1 (RUNX1) is one of the most frequently mutated genes in most of hematological malignancies, especially in acute myeloid leukemia. In the present study, we aimed to identify the key genes and microRNAs based on acute myeloid leukemia with RUNX1 mutation. The newly finding targeted genes and microRNA associated with RUNX1 may benefit to the clinical treatment in acute myeloid leukemia. MATERIAL/METHODS: The gene and miRNA expression data sets relating to RUNX1 mutation and wild-type adult acute myeloid leukemia (AML) patients were downloaded from The Cancer Genome Atlas database. Differentially expressed miRNAs and differentially expressed genes (DEGs) were identified by edgeR of R platform. Gene ontology and the Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed by Metascape and Gene set enrichment analysis. The protein-protein interaction network and miRNA-mRNA regulatory network were performed by Search Tool for the Retrieval of Interacting Genes database and Cytoscape software. RESULTS: A total of 27 differentially expressed miRNAs (25 upregulated and 2 downregulated) and 561 DEGs (429 upregulated and 132 downregulated) were identified. Five miRNAs (miR-151b, miR-151a-5p, let-7a-2-3p, miR-363-3p, miR-20b-5p) had prognostic significance in AML. The gene ontology analysis showed that upregulated DEGs suggested significant enrichment in MHC class II protein complex, extracellular structure organization, blood vessel development, cell morphogenesis involved in differentiation, embryonic morphogenesis, regulation of cell adhesion, and so on. Similarly, the downregulated DEGs were mainly enriched in secretory granule lumen, extracellular structure organization. In the gene set enrichment analysis of Kyoto Encyclopedia of Genes and Genomes pathways, the RUNX1 mutation was associated with adherent junction, WNT signaling pathway, JAK-STAT signaling pathway, pathways in cancer, cell adhesion molecules CAMs, MAPK signaling pathway. Eleven genes (PPBP, APP, CCR5, HLA-DRB1, GNAI1, APLNR, P2RY14, C3AR1, HTR1F, CXCL12, GNG11) were simultaneously identified by hub gene analysis and module analysis. MicroRNA-363-3p may promote the development of RUNX1 mutation AML, targeting SPRYD4 and FNDC3B. In addition, the RUNX1 mutation rates in patient were obviously correlated with age, white blood cell, FAB type, risk(cyto), and risk(molecular) (Pâ<â.05). CONCLUSION: Our findings have indicated that multiple genes and microRNAs may play a crucial role in RUNX1 mutation AML. MicroRNA-363-3p may promote the development of RUNX1 mutation AML by targeting SPRYD4 and FNDC3B.
Assuntos
Carcinogênese/genética , Fibronectinas/genética , Leucemia Mieloide Aguda/genética , MicroRNAs/metabolismo , Proteínas Nucleares/genética , Biologia Computacional , Subunidade alfa 2 de Fator de Ligação ao Core/genética , Conjuntos de Dados como Assunto , Feminino , Regulação Leucêmica da Expressão Gênica , Redes Reguladoras de Genes , Humanos , Leucemia Mieloide Aguda/patologia , Masculino , Pessoa de Meia-Idade , Mutação , Mapeamento de Interação de Proteínas , Mapas de Interação de Proteínas/genéticaRESUMO
BACKGROUND: DNA methyltransferase 3 alpha (DNMT3A) mutation was one of the most frequent genetic alterations in acute myeloid leukemia (AML), which was associated with poor prognosis and appeared to be a potential biomarker. Herein, we aimed to identify the key genes and pathways involved in adult AML with DNMT3A mutations and to find possible therapeutic targets for improving treatment. METHODS: The RNA sequencing datasets of 170 adult AML patients were obtained from The Cancer Genome Atlas (TCGA) database. EdgeR of the R platform was used to identify the differentially expressed genes (DEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed by Metascape and DAVID. And protein-protein interaction (PPI) network and clustering modules were analyzed with the STRING database and Cytoscape software. RESULTS: Mutated DNMT3A resulted in a shorter overall survival (OS) in AML patients and obviously associated with age, blast percentage in peripheral blood, and FLT3 mutation. A total of 283 DEGs were detected, of which 95 were upregulated and 188 were downregulated. GO term analysis showed that DEGs were significantly enriched in neutrophil degranulation, myeloid cell differentiation, stem cell proliferation, positive regulation of neurological system process, leukocyte migration, and tissue morphogenesis. KEGG pathway enrichment analysis indicated that the pathway of cancer, PI3K-Akt signaling pathway, and transcriptional misregulation in cancer may play a crucial role in DNMT3A mutation AML. Seven hub genes (BMP4, MPO, THBS1, APP, ELANE, HOXA7, and VWF) had a significant prognostic value. CONCLUSION: Bioinformatics analysis in the present study provided novel targets for early diagnosis and new strategies for treatment for AML with DNMT3A mutation.