Acute myeloid leukemia (AML) is a common myelogenous malignancy in adults that is often characterized by disease relapse. The pathophysiological mechanism of AML has not yet been elucidated. The present study aimed to identify the crucial microRNAs (miRNAs/miRs) and target genes in AML, and to uncover the potential oncogenic mechanism of AML. miRNA and mRNA expression-profiling microarray datasets were downloaded from the Gene Expression Omnibus database. Differential expression analysis was performed and a regulatory network between miRNAs and target genes was constructed. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were used to predict the biological functions of the differentially expressed genes. Reverse transcription-quantitative polymerase chain reaction analysis was employed to verify the expression levels of miRNAs and target genes in AML patient samples. A total of 86 differentially expressed miRNAs and 468 differentially expressed mRNAs between AML and healthy blood samples were identified. In total, 47 miRNAs and 401 mRNAs were found to be upregulated, and 39 miRNAs and 67 mRNAs were found to be downregulated in AML. A total of 223 miRNA-target genes pairs were subjected to the construction of a regulatory network. Differentially expressed target genes were significantly enriched in the Wnt signaling pathway (hsa04310), melanogenesis (hsa04916) and pathways in cancer (hsa05200). Significantly differentially expressed miRNAs and genes, including hsa-miR-155, hsa-miR-192, annexin A2 (ANXA2), frizzled class receptor 3 (FZD3), and pleomorphic adenoma gene 1 (PLAG1), may serve essential roles in AML oncogenesis. Overall, hsa-miR-155, hsa-miR-192, ANXA2, FZD3 and PLAG1 may be associated with the development of AML via the involvement of the Wnt signaling pathway, melanogenesis and other cancer-associated signaling pathways.
Leukemia is one of the 10 leading causes of cancer-associated mortality in China; in 2011 there were 27,907 mortalities in men and 19,708 mortalities in women from leukemia (1). The four types of Leukemia are acute lymphocytic leukemia, chronic lymphocytic leukemia, acute myeloid leukemia (AML) and chronic myeloid leukemia. AML accounts for ~80% of cases of acute leukemia in adults (2).
AML is a highly heterogeneous leukemia associated with excessive progenitor cell proliferation and a differentiation block for cell-cycle arrest. AML is often caused by karyotypic abnormalities, including chromosomal translocations, deletions and inversions (3,4). Etiological factors driving AML development remain unclear, but lifestyle and environmental exposures, including obesity and smoking, are reported to be associated with the disease (5).
The French-American-British (FAB) and World Health Organization (WHO) systems are the two main AML classification systems. The FAB system classifies AML into subtypes M0-M7 according to the cell type from which AML develops and the degree of maturation of the cells (6). According to the 2008 WHO Classification, AML are classified into six subgroups: AML with recurring genetic abnormalities, AML with myelodysplasia-related changes, therapy-related myeloid neoplasms, not otherwise specified AML, myeloid proliferations related to down syndrome and blastic plasmacytic dendritic cell neoplasms, with diagnosis performed according to morphology, cytochemistry, immunophenotype, genetics and clinical features (7).
Karyotypic abnormalities and genetic mutations are associated with AML progression and prognosis. Translocation of chromosomes 15 and 17 [t(15;17)], t(8;21) or inversion of chromosome 16 is predictive of a relatively good prognosis (8), whereas deletion of chromosome 7, deletion of 5q or >3 chromosomal abnormalities is predictive of a poor prognosis in AML patients (9,10). Fms-like tyrosine kinase 3-internal duplication (FLT3-ITD) and nucleophosmin (NPM1) are the two most commonly mutated genes in AML patients. Mutations to NPM1 occur in 50% of AML patients, whereas mutations to FLT3-ITD occur in 30%. FLT3-ITD, KIT proto-oncogene receptor tyrosine kinase and brain and acute leukemia, cytoplasmic gene mutations have a negative impact on AML prognosis (11,12), while NPM1 and CCAAT/enhancer binding protein-α have a positive impact on prognosis (12–14).
At present, the pathogenic mechanism of AML is unclear. Acute promyelocytic leukemia (APL) is an M3 subtype of AML according to the FAB classification system. Overexpression of microRNA (miRNA/miR)-125a decreases APL NB4 cell proliferation, the inhibition of cell cycle progression and the promotion of cell apoptosis by targeting the ErbB pathway in APL (15). miR-150 expression induces the myeloid differentiation of human acute leukemia cells and normal hematopoietic progenitors. In AML patient samples and cell lines, miR-150 expression is low or absent, which contributes to the blocking of myeloid differentiation in acute leukemia cells (16).
The aim of the present study was to identify featured target genes of significantly differentially expressed miRNAs in AML by comparing AML samples with healthy ones, and analyzing the correlation of miRNA-target genes. Candidate target genes identified by these approaches may provide the groundwork for the elucidation of the mechanism of AML. However, further investigation of the potential function of these genes in the treatment of AML is required.
Materials and methodsTranscriptomics datasets
In the Gene Expression Omnibus (GEO; http://ncbi.nlm.nih.gov/geo/) (17), only the studies comparing AML and healthy blood were assessed. A total of 6 studies were assessed in which the global profile of gene expression was measured in AML patients' blood samples, with accession numbers GSE48558, GSE35008, GSE35010, GSE24395, GSE17054 and GSE51908. The details of studies, including the platform, number of cases, controls, year and author, were extracted and assessed.
Data processing and identification of differentially expressed miRNAs and mRNAs
Raw expression datasets were downloaded from the GEO and the raw datasets were preprocessed by log2 transformation and Z-score normalization. Limma, which is a linear model for microarray data analysis, was utilized to analyze the differentially expressed miRNAs and mRNAs between the AML and healthy control samples (18). A false discovery rate (FDR) of <0.05 was set as the threshold of differentially expressed miRNAs and mRNAs.
miRNA target gene prediction
Targets genes for differentially expressed miRNAs were predicted via miRTarBase (http://mirtarbase.mbc.nctu.edu.tw/). Over 50,000 miRNA-target interactions in the miRTarBase database have been validated by experiments such as reporter assays, western blotting or microarray experiments with overexpression or knockdown of miRNAs (19,20).
Construction of regulatory miRNA-mRNA networks
The miRNA-mRNA interaction network of differentially expressed miRNA and mRNA was visualized using Cytoscape (http://cytoscape.org) (21). This software presents the regulation between miRNA and mRNA as two-dimensional network with nodes and edges, which represent miRNA-target gene associations.
Functional enrichment analysis of the differentially expressed target genes
To obtain the functions of differentially expressed targeted genes, Gene Ontology (GO) terms (22) and Kyoto Encyclopedia of Genes and Genomes (KEGG) (23) pathways were enriched using GOEAST (http://omicslab.genetics.ac.cn/GOEAST) (24) and GeneCodis (http://genecodis.cnb.csic.es/analysis), respectively (25). P<0.01 and FDR <0.05 were set as the thresholds of significance for GO terms and KEGG pathway analysis.
The blood samples were collected from 3 males with AML treated in Qilu Hospital of Shandong University (Shandong, China) in 2015, with a mean age of 45.6 years. In addition, 3 normal blood samples were also included with corresponding gender and age. Total RNA of fresh blood samples were extracted by TRIzol reagent (Invitrogen; Thermo Fisher Scientific, Inc., Waltham, MA, USA) according to the manufacturer's instructions. Use of these samples was approved by the Ethics Committee of Qilu Hospital of Shandong University (Jinan, China). The SuperScript III Reverse Transcription kit (Invitrogen; Thermo Fisher Scientific, Inc.) was used to synthesize the cDNA according to the manufacturer's instructions. RT-qPCR was performed using Power SYBR Green PCR Master mix (Applied Biosystems; Thermo Fisher Scientific, Inc.) on the Applied Biosystems 7500 (Applied Biosystems; Thermo Fisher Scientific, Inc.). The RT-qPCR cycling conditions were 1 cycle of 95°C for 10 min, followed by 45 cycles of 95°C for 15 sec and 60°C for 60 sec. The miRcute miRNA First-Strand cDNA kit (Tiangen Biotech Co., Ltd., Bejing, China) and the miRcute miRNA qPCR Detection kit (Tiangen Biotech Co., Ltd.) were used for miRNA expression level detection. The RT-qPCR cycling conditions for miRNA were 1 cycle of 94°C for 2 min, followed by 45 cycles of 94°C for 20 sec and 60°C for 34 sec. U6 small nuclear RNA and β-actin was used as internal controls for miRNA and mRNA detection, respectively. The relative expression of target genes was calculated using the 2−ΔΔCq method (26). At least three independent experiments were performed. The PCR primers used were as follows: hsa-miR-155 forward, 5′-TAATGCTAATCGTGATAGGGGT-3′ and reverse, GTGCAGGGTCCGAGGT; hsa-miR-192 forward, 5′-TGACCTATGAATTGACAGCC-3′ and reverse, GTGCAGGGTCCGAGGT; frizzled class receptor 3 (FZD3) forward, 5′-TCTCCTCTTAGCTGGCATTATATCC-3′ and reverse, 5′-GCAGCGTTCTTGTATCCACGTT-3′; and Annexin A2 (ANXA2) forward, 5′-AGAATCATGGTCTCCCGCAGTG-3′ and reverse, 5′-TCCACCACACAGGTACAGCAGC-3′.
Statistical analysis
RT-qPCR experimental data was expressed as the mean ± standard deviation. Statistical significance was evaluated using an unpaired Student's t-test. P<0.05 was considered to indicate a statistically significant difference.
ResultsDifferentially expressed miRNAs and mRNAs in AML
A total of 5 mRNA and 1 miRNA expression profiles datasets, including 137 AML and 84 healthy samples were downloaded from the GEO, normalized and processed (Table I) (27–31). Differentially expressed genes between AML and normal samples, including 86 miRNAs and 468 mRNAs, were screened with a threshold of FDR<0.05. Of the 86 miRNAs, 47 were upregulated and 39 were downregulated in AML samples compared with the normal samples; of the 468 mRNAs, 401 were upregulated and genes 67 were downregulated. The top 10 upregulated and downregulated miRNAs are shown in Table II (the full list of differentially expressed miRNAs and mRNAs is not shown).
Construction of miRNA-mRNA regulatory networks
The miRTarBase database was used to predict the target genes of the 47 upregulated and 39 downregulated miRNAs in AML; 223 miRNA-target gene pairs, including 31 differentially expressed miRNAs and 153 target genes, were visualized using Cytoscape software (Fig. 1). A total of 55 differentially expressed miRNAs, including hsa-miR-29b-1* and hsa-miR-194, were not displayed in the network, as the 55 differentially expressed miRNAs were not available in miRTarBase database (data not shown). hsa-miR-26b, hsa-miR-192, hsa-miR-21, hsa-miR-181a and hsa-miR-155 regulated 43, 25, 26, 15 and 11 targets, respectively, and displayed the highest connectivity. Pleomorphic adenoma gene 1 (PLAG1), high-mobility group AT-hook 2, RUN-domain-containing 3B, transmembrane protein 2, TNF-α induced protein 3 and family with sequence similarity 3 member C, which were regulated by 7, 5, 4, 4, 4 and 4 miRNAs, respectively, were the mRNAs with the highest connectivity (Fig. 1).
Functional analysis of miRNA target genes
GO classification and KEGG pathway analyses were used to obtain the biological functions of miRNA target genes, including biological process, cellular component, molecular function and signaling pathway. The threshold of GO classification was set as P<0.01. Negative regulation of blood coagulation (GO:0030195, P=1.83×10−24), negative regulation of hemostasis (GO:1900047, P=1.83×10−24) and negative regulation of coagulation (GO:0050819, P=2.65×10−23) were the most significantly enriched target genes of biological processes; sarcolemma (GO:0042383, P=1.85×10−29), Schmidt-Lanterman incisure (GO:0043220, P=1.80×10−25) and myelin sheath adaxonal region (GO:0035749, P=5.91×10−25) were the most significantly enriched target genes of the cellular component; and phospholipase inhibitor activity (GO:0004859, P=1.14×10−44), lipase inhibitor activity (GO:0055102, P=3.76×10−43) and calcium-dependent phospholipid binding (GO:0005544, P=5.77×10−41) were the most significantly enriched target genes of the molecular function (Table III).
In total, 148 of the 153 differentially expressed miRNA target genes were enriched in the KEGG database. The Wnt signaling pathway (FDR=8.70×10−4), melanogenesis (FDR=8.70×10−4) and pathways in cancer (FDR=1.60×10−3) were the most significantly enriched pathways in KEGG analysis, with the criteria of FDR<0.05 (Table IV).
RT-qPCR validation of differentially expressed miRNAs and target genes
To validate the microarray analysis data, the levels of significant differentially expressed miRNA and target genes were quantified by RT-qPCR in three AML blood samples and three normal blood samples. hsa-miR-155 was significantly (P<0.05) upregulated in AML compared with that in the normal samples, and the target gene ANXA2 was significantly downregulated in AML (Fig. 2A). FZD3 was significantly upregulated in the three AML samples compared with the normal samples (P<0.01; Fig. 2B). The present study identified hsa-miR-192 as a downregulated miRNA in AML, although the expression level was not found to be significantly different in AML by RT-qPCR validation (Fig. 2C).
Discussion
In the present study, hsa-miR-155 was one of the five miRNAs with the highest connectivity with target genes, targeting 11 differentially expressed mRNAs (Fig. 1), and was significantly upregulated in AML. In the present study, ANXA2 was predicted as a putative target gene of hsa-miR-155. RT-qPCR validated that hsa-miR-155 was significantly upregulated and ANXA2 was significantly downregulated in AML (Fig. 2A), which is in accordance with the bioinformatics analysis. The fact that hsa-miR-155 was upregulated in AML was consistent with the results of a previous study (32). Mounting evidence identifies hsa-miR-155 as having an oncogenic role, generating AML; overexpression of hsa-miR-155 causes myeloproliferation with cell cell-cycle arrest (33,34). High expression of hsa-miR-155 is associated with a poor outcome in AML patients, which has been observed in numerous AML patients via sequencing studies and miRNA expression analyses (35–37). Additionally, hsa-miR-155 is reported to contribute to the metastasis of various solid tumors, including colorectal carcinoma (38), oral squamous cell carcinoma (39) and renal cell carcinoma (40). ANXA2 is a target gene of hsa-miR-155 and its downregulation is associated with a poor AML patient prognosis, based on gene expression profile analysis (41). hsa-miR-155 upregulation and ANXA2 downregulation may be potential biomarkers for the clinical evaluation of AML prognosis.
Through KEGG analysis, FZD3 was found to be enriched in four signaling pathways, including the Wnt signaling pathway, melanogenesis, pathways in cancer and basal cell carcinoma. The Wnt signaling pathway was the most significantly enriched pathway in AML (Table IV). Higher expression of FZD3 was detected in three AML patients compared with that in the normal control, as determined by RT-qPCR (Fig. 2B), which was consistent with the bioinformatics analysis. FZD3 is a member of the frizzled gene family, which also includes FZD1 and FZD7, and functions as a receptor for the canonical Wnt/β-catenin signaling pathway. Overactivation of the Wnt signaling pathway contributes to tumorigenesis (42,43). According to the present study, the Wnt signaling pathway was essential for AML progression and oncogenicity. CXXC finger protein 5, which is frequently deleted in AML, inhibits the Wnt pathway and leukemic cell proliferation (44). Activation of the Wnt/β-catenin pathway mediates transformation of AML progenitor cells and results in impaired myelomonocytic differentiation (45,46). The FZD3/Wnt signaling pathway may therefore be important in AML pathogenesis.
In the present study, hsa-miR-192 was the most significantly downregulated miRNA and regulated 25 target genes in AML (Fig. 1). miR-192 downregulation is associated with cell cycle progression, cell growth, apoptosis and proliferation of solid tumors (47,48). Overexpression of miR-192 induces apoptotic death in bladder cancer cells, increases the proportion of cells in the G0/G1 phase and decreases the proportion of cells in the S phase compared with a control (47). Curcumin is a traditional Chinese medicine extracted from turmeric that inhibits non-small cell lung cancer cell (NSCLC) cell proliferation and induces NSCLC cell apoptosis through the upregulation of miR-192-5p and the suppression of the phosphoinositide-3 kinase/protein kinase B signaling pathway (47,48). In the present study, hsa-miR-192 was downregulated in AML (Fig. 2C), suggesting that it may also serve a key role in AML cell apoptosis and proliferation.
PLAG1 was targeted by 7 miRNAs, meaning it had the highest connectivity of the mRNAs in the miRNA-mRNA network (Fig. 1). The PLAG family consists of 3 members (PLAG1, PLAGL1 and PLAGL2), each with a highly conserved zinc finger structure that allows them to function as transcription factors to recognize DNA and/or RNA (49). PLAG1 serves an oncogenic role in AML, cooperating with CBF-SMMHC to induce AML tumorigenesis (50). The results of the present study revealed that PLAG1 was upregulated in AML.
In summary, a miRNA-mRNA regulatory network was constructed based on differentially expressed miRNAs and target genes in AML. In this network, a number of miRNAs and target genes that may play important roles in AML, such as hsa-miR-155, hsa-miR192, ANXA2, FZD3 and PLAG1, were identified. These results indicated that the Wnt signaling pathway, melanogenesis and pathways in cancer may be involved in the pathogenesis of AML. An miRNA-target gene regulatory network was constructed in AML using bioinformatic tools. A number of miRNAs and mRNAs that are potentially important for AML tumorigenesis were identified. However, the mechanism behind the associations between miRNA, mRNA and miRNA-mRNA involved in AML progression and development requires further investigation.
Acknowledgements
The present study was supported by a grant from the Program of Jining Science and Technology Development Plan (grant no, 2015-57-102).
ReferencesChenWZhengRZengHZhangSThe updated incidences and mortalities of major cancers in China, 2011Chin J Cancer34507507201510.1186/s40880-015-0042-6CripeLDAdult acute leukemiaCurr Probl Cancer21164199710.1016/S0147-0272(97)80006-29058027MarcucciGHaferlachTDöhnerHMolecular genetics of adult acute myeloid leukemia: Prognostic and therapeutic implicationsJ Clin Oncol29475486201110.1200/JCO.2010.30.255421220609DeguchiKGillilandDGCooperativity between mutations in tyrosine kinases and in hematopoietic transcription factors in AMLLeukemia16740744200210.1038/sj.leu.240250011960359FinnLSproatLHeckmanMGJiangLDiehlNNKetterlingRTibesRValdezRForanJEpidemiology of adult acute myeloid leukemia: Impact of exposures on clinical phenotypes and outcomes after therapyCancer Epidemiol3910841092201510.1016/j.canep.2015.09.00326365691BennettJMCatovskyDDanielMTFlandrinGGaltonDAGralnickHRSultanCProposals for the classification of the acute leukaemias. French-American-British (FAB) co-operative groupBr J Haematol33451458197610.1111/j.1365-2141.1976.tb03563.x188440VardimanJWThieleJArberDABrunningRDBorowitzMJPorwitAHarrisNLLe BeauMMHellström-LindbergETefferiABloomfieldCDThe 2008 revision of the World Health Organization (WHO) classification of myeloid neoplasms and acute leukemia: Rationale and important changesBlood114937951200910.1182/blood-2009-03-20926219357394MarcucciGMrózekKRuppertASMaharryKKolitzJEMooreJOMayerRJPettenatiMJPowellBLEdwardsCGPrognostic factors and outcome of core binding factor acute myeloid leukemia patients with t(8;21) differ from those of patients with inv(16): A Cancer and Leukemia Group B studyJ Clin Oncol2357055717200510.1200/JCO.2005.15.61016110030StoneRMPrognostic factors in AML in relation to (ab)normal karyotypeBest Pract Res Clin Haematol22523528200910.1016/j.beha.2009.07.00319959103SchanzJHaaseDCytogenetic features in myelodysplastic syndromes2014CareRSValkPJMGoodeveACAbu-DuhierFMGeertsma-KleinekoortWMWilsonGAGariMAPeakeIRLöwenbergBReillyJTIncidence and prognosis of c-KIT and FLT3 mutations in core binding factor (CBF) acute myeloid leukaemiasBr J Haematol121775777200310.1046/j.1365-2141.2003.04362.x12780793LangerCRadmacherMDRuppertASWhitmanSPPaschkaPMrózekKBaldusCDVukosavljevicTLiuCGRossMEHigh BAALC expression associates with other molecular prognostic markers, poor outcome, and a distinct gene-expression signature in cytogenetically normal patients younger than 60 years with acute myeloid leukemia: A Cancer and Leukemia Group B (CALGB) studyBlood11153715379200810.1182/blood-2007-11-12495818378853PreudhommeCSagotCBoisselNCayuelaJMTigaudIde BottonSThomasXRaffouxELamandinCCastaigneSFavorable prognostic significance of CEBPA mutations in patients with de novo acute myeloid leukemia: A study from the acute leukemia French association (ALFA)Blood10027172723200210.1182/blood-2002-03-099012351377DöhnerKTobisKUlrichRFröhlingSBennerASchlenkRFDöhnerHPrognostic significance of partial tandem duplications of the MLL gene in adult patients 16 to 60 years old with acute myeloid leukemia and normal cytogenetics: A study of the acute myeloid leukemia study group UlmJ Clin Oncol2032543261200210.1200/JCO.2002.09.08812149299UfkinMLPetersonSYangXDriscollHDuarteCSathyanarayanaPmiR-125a regulates cell cycle, proliferation, and apoptosis by targeting the ErbB pathway in acute myeloid leukemiaLeuk Res38402410201410.1016/j.leukres.2013.12.02124484870MorrisVAZhangAYangTStirewaltDLRamamurthyRMeshinchiSOehlerVGMicroRNA-150 expression induces myeloid differentiation of human acute leukemia cells and normal hematopoietic progenitorsPLoS One8e75815201310.1371/journal.pone.007581524086639EdgarRDomrachevMLashAEGene expression omnibus: NCBI gene expression and hybridization array data repositoryNucleic Acids Res30207210200210.1093/nar/30.1.20711752295DibounIWernischLOrengoCAKoltzenburgMMicroarray analysis after RNA amplification can detect pronounced differences in gene expression using limmaBMC Genomics7252200610.1186/1471-2164-7-25217029630HsuSDLinFMWuWYLiangCHuangWCChanWLTsaiWTChenGZLeeCJChiuCMmiRTarBase: A database curates experimentally validated microRNA-target interactionsNucleic Acids Res39(Database Issue)D163D169201110.1093/nar/gkq110721071411HsuSDTsengYTShresthaSLinYLKhaleelAChouCHChuCFHuangHYLinCMHoSYmiRTarBase update 2014: An information resource for experimentally validated miRNA-target interactionsNucleic Acids Res42(Database Issue)D78D85201410.1093/nar/gkt126624304892ShannonPMarkielAOzierOBaligaNSWangJTRamageDAminNSchwikowskiBIdekerTCytoscape: A software environment for integrated models of biomolecular interaction networksGenome Res1324982504200310.1101/gr.123930314597658ChengLLinHHuYWangJYangZGene function prediction based on the Gene Ontology hierarchical structurePLoS One9e107187201410.1371/journal.pone.010718725192339KanehisaMSatoYKawashimaMFurumichiMTanabeMKEGG as a reference resource for gene and protein annotationNucleic Acids Res44D457D462201610.1093/nar/gkv107026476454ZhengQWangXJGOEAST: A web-based software toolkit for Gene Ontology enrichment analysisNucleic Acids Res36W358W363200810.1093/nar/gkn27618487275Carmona-SaezPChagoyenMTiradoFCarazoJMPascual-MontanoAGENECODIS: A web-based tool for finding significant concurrent annotations in gene listsGenome Biol8R3200710.1186/gb-2007-8-1-r317204154LivakKJSchmittgenTDAnalysis of relative gene expression data using real-time quantitative PCR and the 2(−Delta Delta C(T)) methodMethods25402408200110.1006/meth.2001.126211846609Cramer-MoralesKNieborowska-SkorskaMScheibnerKPadgetMIrvineDASliwinskiTHaasKLeeJGengHRoyDPersonalized synthetic lethality induced by targeting RAD52 in leukemias identified by gene mutation and expression profileBlood12212931304201310.1182/blood-2013-05-50107223836560BarreyroLWillBBartholdyBZhouLTodorovaTIStanleyRFBen-NeriahSMontagnaCParekhSPellagattiAOverexpression of IL-1 receptor accessory protein in stem and progenitor cells and outcome correlation in AML and MDSBlood12012901298201210.1182/blood-2012-01-40469922723552KikushigeYShimaTTakayanagiSUrataSMiyamotoTIwasakiHTakenakaKTeshimaTTanakaTInagakiYAkashiKTIM-3 is a promising target to selectively kill acute myeloid leukemia stem cellsCell Stem Cell7708717201010.1016/j.stem.2010.11.01421112565MajetiRBeckerMWTianQLeeTLYanXLiuRChiangJHHoodLClarkeMFWeissmanILDysregulated gene expression networks in human acute myelogenous leukemia stem cellsProc Natl Acad Sci USA10633963401200910.1073/pnas.090008910619218430TanYSKimMKingsburyTJCivinCIChengWCRegulation of RAB5C is important for the growth inhibitory effects of MiR-509 in human precursor-B acute lymphoblastic leukemiaPLoS One9e111777201410.1371/journal.pone.011177725368993HavelangeVStaufferNHeaphyCCVoliniaSAndreeffMMarcucciGCroceCMGarzonRFunctional implications of microRNAs in acute myeloid leukemia by integrating microRNA and messenger RNA expression profilingCancer11746964706201110.1002/cncr.2609621455993ForrestARKanamori-KatayamaMTomaruYLassmannTNinomiyaNTakahashiYde HoonMJKubosakiAKaihoASuzukiMInduction of microRNAs, mir-155, mir-222, mir-424 and mir-503, promotes monocytic differentiation through combinatorial regulationLeukemia24460466201010.1038/leu.2009.24619956200O'ConnellRMRaoDSChaudhuriAABoldinMPTaganovKDNicollJPaquetteRLBaltimoreDSustained expression of microRNA-155 in hematopoietic stem cells causes a myeloproliferative disorderJ Exp Med205585594200810.1084/jem.2007210818299402MarcucciGMaharryKSMetzelerKHVoliniaSWuYZMrózekKNicoletDKohlschmidtJWhitmanSPMendlerJHClinical role of microRNAs in cytogenetically normal acute myeloid leukemia: miR-155 upregulation independently identifies high-risk patientsJ Clin Oncol3120862093201310.1200/JCO.2012.45.622823650424ChuangMKChiuYCChouWCHouHAChuangEYTienHFA 3-microRNA scoring system for prognostication in de novo acute myeloid leukemia patientsLeukemia2910511059201510.1038/leu.2014.33325428263ZhiFCaoXXieXWangBDongWGuWLingYWangRYangYLiuYIdentification of circulating microRNAs as potential biomarkers for detecting acute myeloid leukemiaPLoS One8e56718201310.1371/journal.pone.005671823437222QuYLWangHFSunZQTangYHanXNYuXBLiuKUp-regulated miR-155-5p promotes cell proliferation, invasion and metastasis in colorectal carcinomaInt J Clin Exp Pathol869886994201526261588BabaOHasegawaSNagaiHUchidaFYamatojiMKannoNIYamagataKSakaiSYanagawaTBukawaHMicroRNA-155-5p is associated with oral squamous cell carcinoma metastasis and poor prognosisJ Oral Pathol Med45248255201610.1111/jop.1235126307116MerhautovaJHezovaRPoprachAKovarikovaARadovaLSvobodaMVyzulaRDemlovaRSlabyOmiR-155 and miR-484 are associated with time to progression in metastatic renal cell carcinoma treated with sunitinibBiomed Res Int2015941980201510.1155/2015/94198026064968ParkMHChoSAYooKHYangMHAhnJYLeeHSLeeKEMunYCChoDHSeongCMParkJHGene expression profile related to prognosis of acute myeloid leukemiaOncol Rep1813951402200717982622Conacci-SorrellMZhurinskyJBen-Ze'evAThe cadherin-catenin adhesion system in signaling and cancerJ Clin Invest109987991200210.1172/JCI021542911956233GilesRHvan EsJHCleversHCaught up in a Wnt storm: Wnt signaling in cancerBiochim Biophys Acta1653124200312781368KühnlAValkPJSandersMAIveyAHillsRKMillsKIGaleREKaiserMFDillonRJoannidesMDownregulation of the Wnt inhibitor CXXC5 predicts a better prognosis in acute myeloid leukemiaBlood12529852994201510.1182/blood-2014-12-61370325805812WangYKrivtsovAVSinhaAUNorthTEGoesslingWFengZZonLIArmstrongSAThe Wnt/beta-catenin pathway is required for the development of leukemia stem cells in AMLScience32716501653201010.1126/science.118662420339075SimonMGrandageVLLinchDCKhwajaAConstitutive activation of the Wnt/beta-catenin signalling pathway in acute myeloid leukaemiaOncogene2424102420200510.1038/sj.onc.120843115735743YeMZhangJZhangJMiaoQYaoLZhangJCurcumin promotes apoptosis by activating the p53-miR-192-5p/215-XIAP pathway in non-small cell lung cancerCancer Lett357196205201510.1016/j.canlet.2014.11.02825444916JinHQiaoFWangYXuYShangYCurcumin inhibits cell proliferation and induces apoptosis of human non-small cell lung cancer cells through the upregulation of miR-192-5p and suppression of PI3K/Akt signaling pathwayOncol Rep3427822789201510.3892/or.2015.425826351877KasKVozMLHensenKMeyenEVan de VenWJTranscriptional activation capacity of the novel PLAG family of zinc finger proteinsJ Biol Chem2732302623032199810.1074/jbc.273.36.230269722527LandretteSFKuoYHHensenKvan Waalwijk van Doorn-KhosrovaniBarjesteh SPerratPNVan de VenWJDelwelRCastillaLHPlag1 and Plagl2 are oncogenes that induce acute myeloid leukemia in cooperation with Cbfb-MYH11Blood10529002907200510.1182/blood-2004-09-363015585652
miRNA-target gene regulatory network of acute myeloid leukemia. Circular nodes represent target genes and diamond nodes represent miRNAs. Green nodes represent downregulation, red nodes represent upregulation. Solid lines indicate regulatory associations between the miRNAs and target genes. miRNA/miR, microRNA.
Verification of miRNA and target gene expression levels in AML and normal controls, as determined by reverse transcription-quantitative polymerase chain reaction. (A) hsa-miR-155 and ANXA2 expression levels in AML patients and healthy controls. (B) FZD3 expression levels in AML patients and healthy controls. (C) hsa-miR-192 expression levels in AML patients and healthy controls. *P<0.05, **P<0.01. miRNA/miR, microRNA; AML, acute myeloid leukemia; ANXA2, annexin A2; FZD3, frizzled class receptor 3; CON, healthy control patient blood samples; LEU, AML patient blood samples.
Characteristics of mRNA and miRNA expression profiling of the acute myeloid leukemia.
A, mRNA expression profiling
Author, year
Gene expression omnibus ID
Platform
Samples, H:P
(Refs.)
Civin et al, 2013
GSE48558
GPL6244 [HuGene-1_0-st] Affymetrix Human Gene 1.0 ST Array [transcript (gene) version]
49:18
(27)
Barreyro et al, 2012
GSE35008
GPL6244 [HuGene-1_0-st] Affymetrix Human Gene 1.0 ST Array
16:12
(28)
Barreyro et al, 2012
GSE35010
GPL6244 [HuGene-1_0-st] Affymetrix Human Gene 1.0 ST Array
16:15
(28)
Kikushige et al, 2010
GSE24395
GPL6106 Sentrix Human-6 v2 Expression BeadChip
5:12
(29)
Majeti R et al, 2009
GSE17054
GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array
4:9
(30)
B, miRNA expression profiling
Author, year
Gene expression omnibus ID
Platform
Samples, H:P
(Refs.)
Tan YS et al, 2013
GSE51908
GPL8786 [miRNA-1_0] Affymetrix miRNA Array
47:18
(31)
H, healthy subject; P, AML patient; miRNA, microRNA.