Dataset Information

MicroRNAs and regulated interaction networks reveal differences between adult and pediatric acute myeloid leukemia.

ABSTRACT: OBJECTIVES:The purpose of this study was to identify featured microRNAs and their regulated network between adult and pediatric acute myeloid leukemia (AML) and find potential utility as biomarkers for diagnosis and treatment of pediatric AML. METHODS:We downloaded the microRNA expression dataset GSE35320 from Gene Expression Omnibus database and selected expression chips from bone marrow of 71 pediatric AML samples and 6 adulthood AML samples. Differentially expressed microRNAs were identified by Wilcox test. The target genes of these microRNAs were predicted using an integrative method and their functional enrichment analysis was performed using DAVID. Finally, STRING database and Cytoscape software was used to construct and analyze the interaction network. RESULTS:A total of 7 differentially expressed microRNAs were identified and the remarkably up-regulated and down-regulated microRNAs were miR-16 and miR-142-5p which included 323 and 22 predicted target genes, respectively. The target genes of 7 microRNAs were most associated with regulation of cell cycle, p53 signaling pathway, Wnt signaling pathway and neurotrophin signaling pathway. The interaction network of miR-16 target genes was constructed among 354 high confidence interaction pairs. The core genes of the network, such as TP53, BCL2, VEGFA, had a role in prognosis of children with AML. CONCLUSIONS:The featured microRNAs and their target genes are significant in the occurrence and development of pediatric AML, which is likely to be important for the identification of therapeutic targets and biomarkers for these patients.

SUBMITTER: Liu J

PROVIDER: S-EPMC6965806 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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MicroRNAs and regulated interaction networks reveal differences between adult and pediatric acute myeloid leukemia.

Liu Jing J Zhou Lei L Fu Xiaomin X Xu Chen C Huang Sai S Li Yan Y Gao Honghao H Guan Wei W Yang Lan L Lv Na N

International journal of clinical and experimental pathology 20171001 10

<h4>Objectives</h4>The purpose of this study was to identify featured microRNAs and their regulated network between adult and pediatric acute myeloid leukemia (AML) and find potential utility as biomarkers for diagnosis and treatment of pediatric AML.<h4>Methods</h4>We downloaded the microRNA expression dataset GSE35320 from Gene Expression Omnibus database and selected expression chips from bone marrow of 71 pediatric AML samples and 6 adulthood AML samples. Differentially expressed microRNAs w ...[more]

PMID: 31966399

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Project description:Background: Associated with poor prognosis, FMS-like tyrosine kinase 3 (FLT3) mutation appeared frequently in acute myeloid leukemia (AML). Herein, we aimed to identify the key genes and miRNAs involved in adult AML with FLT3 mutation and find possible therapeutic targets for improving treatment. Materials: Gene and miRNA expression data and survival profiles were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. EdgeR of R platform was applied to identify the differentially expressed genes and miRNAs (DEGs, DE-miRNAs). Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed by Metascape and DAVID. And protein-protein interaction network, miRNA-mRNA regulatory network and clustering modules analyses were performed by STRING database and Cytoscape software. Results: Survival analysis showed FLT3 mutation led to adverse outcome in AML. 24 DE-miRNAs (6 upregulated, 18 downregulated) and 250 DEGs (54 upregulated, 196 downregulated) were identified. Five miRNAs had prognostic value and the results matched their expression levels (miR-1-3p, miR-10a-3p, miR-10a-5p, miR-133a-3p and miR-99b-5p). GO analysis showed DEGs were enriched in skeletal system development, blood vessel development, cartilage development, tissue morphogenesis, cartilage morphogenesis, cell morphogenesis involved in differentiation, response to growth factor, cell-substrate adhesion and so on. The KEGG analysis showed DEGs were enriched in PI3K-Akt signaling pathway, ECM-receptor interaction and focal adhesion. Seven genes (LAMC1, COL3A1, APOB, COL1A2, APP, SPP1 and FSTL1) were simultaneously identified by hub gene analysis and module analysis. SLC14A1, ARHGAP5 and PIK3CA, the target genes of miR-10a-3p, resulted in poor prognosis. Conclusion: Our study successfully identified molecular markers, processes and pathways affected by FLT3 mutation in AML. Furthermore, miR-10a-3p, a novel oncogene, might involve in the development of FLT3 mutation adult AML by targeting SLC14A1, ARHGAP5 and PIK3CA.

Project description:BackgroundAlthough it is well-known that adult and pediatric acute myeloid leukemias (AMLs) are genetically distinct diseases, they still share certain gene expression profiles. The age-related genetic heterogeneities of AMLs have been well-studied, but the common prognostic signatures and molecular mechanisms of adult and pediatric AMLs are less investigated.AimTo identify genes and pathways that are associated with both pediatric and adult AMLs and discover a gene signature for overall survival (OS) prediction.MethodsThrough mining the transcriptome profiles of The Cancer Genome Atlas (TCGA) data sets of adult cancers and The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) data of pediatric cancers, we identified genes that are commonly dysregulated in both pediatric and adult AMLs, further discovered a common gene signature, and built two risk score models for TCGA and TARGET cohorts, respectively with L 0 regularized global AUC (area under the receiver operating characteristic curve) summary maximization.ResultsWe identified 57 genes that are differentially expressed and prognostically significant in both adult and childhood AMLs. The top 4 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enriched with those 57 genes include transcriptional misregulation, focal adhesion, PI3K-Akt signaling pathway, and signaling pathways regulating pluripotency of stem cells. We further identified a 6-gene signature including genes of ADAMTS3, DNMT3B, NYNRIN, SORT1, ZFHX3, and ZG16B for risk prediction. We constructed a risk score model with one dataset (either TCGA or TARGET) and evaluated its performance with the other. The test AUCs for the risk prediction of TCGA data with a 2-year and 5-year OS cutoffs are 0.762 (P = 2.33e-13, 95% CI: 0.69-0.83) and 0.759 (P = 7.26e-08, 95% CI: 0.66-0.85), respectively, while the test AUCs of TARGET data with the same cutoffs are 0.71 (P = 3.3e-07, 95% CI: 0.62-0.79) and 0.72 (P= 5.25e-09, 95% CI: 0.65-0.80), respectively. We further stratified patients into 3 equal sized prognostic subtypes with the 6-gene risk scores. The P-values of the tertile partitions are 1.74e-07 and 3.28e-08 for the TARGET and TCGA cohorts, respectively, which are significantly better than the standard cytogenetic risk stratification of both cohorts (TARGET: P = 1.64e-06; TCGA: P = 1.79e-05). When validated with two other independent cohorts, the 6-gene risk score models remain a significant predictor for OS. Investigating the common gene expression program is significant in that we may extrapolate the findings from adults to children and avoid unnecessary pediatric clinical trials.

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MicroRNAs and regulated interaction networks reveal differences between adult and pediatric acute myeloid leukemia.

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MicroRNAs and regulated interaction networks reveal differences between adult and pediatric acute myeloid leukemia.

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