Literature DB >> 30935386

High expression of miR-363 predicts poor prognosis and guides treatment selection in acute myeloid leukemia.

Huihui Zhang1,2, Ninghan Zhang1,2, Rong Wang1,2, Tingting Shao1, Yuan Feng1, Yao Yao1,2, Qingyun Wu1,2, Shengyun Zhu1,2, Jiang Cao2, Huanxin Zhang2, Zhenyu Li1,2, Xuejiao Liu3, Mingshan Niu4,5, Kailin Xu6,7,8.   

Abstract

BACKGROUND: Acute myeloid leukemia (AML) is a highly heterogeneous malignancy with various outcomes, and therefore needs better risk stratification tools to help select optimal therapeutic options.
METHODS: In this study, we identify miRNAs that could predict clinical outcome in a heterogeneous AML population using TCGA dataset.
RESULTS: We found that MiR-363 is a novel prognostic factor in AML patients undergoing chemotherapy. In multivariable analyses, high miR-363 remained predictive for shorter OS (HR = 2.349, P = 0.012) and EFS (HR = 2.082, P = 0.001) independent of other well-known prognostic factors. More importantly, allogeneic hematopoietic stem cell transplantation (allo-HSCT) overcame the adverse outcomes related to high miR-363 expression. In gene expression profiling, high miR-363 expression was positively correlated with the amounts of leukemogenic transcription factors, including Myb, RUNX3, GATA3, IKZF3, ETS1 and MLLT3. Notably, we found that the in silico predicted target genes (EZH2, KLF6 and PTEN) of miR-363 were downregulated in association with high miR-363 expression.
CONCLUSIONS: In summary, miR-363 expression may help identify patients in need of strategies to select the optimal therapy between chemotherapeutic and allo-HCST regimens. AML patients with high miR-363 expression may be highly recommended for early allo-HSCT regimen.

Entities:  

Keywords:  Acute myeloid leukemia; Allo-HSCT; Chemotherapy; Clinical outcome; Mir-363

Mesh:

Substances:

Year:  2019        PMID: 30935386      PMCID: PMC6444823          DOI: 10.1186/s12967-019-1858-7

Source DB:  PubMed          Journal:  J Transl Med        ISSN: 1479-5876            Impact factor:   5.531


Background

Acute myeloid leukemia (AML), the most frequent form of acute leukemia in adults, is caused by a rapid clonal proliferation of neoplastic myeloblasts [1]. Patients with AML manifest complex and heterogeneous outcomes after receiving different treatments. Conventional cytotoxic treatment with chemotherapeutics is the first-line therapy in AML [2]. High-risk patients could receive effectively antileukemic action and potential cure after accepting allogeneic hematopoietic stem cell transplantation (allo-HSCT). The variation in AML patient prognosis is related to various inherent factors, including cytogenetics and genetic alterations. Somatic mutations in NPM1, CEBPA, FLT3, IDH1, IDH2 and TET2 are associated with outcomes of patients and served as prognostic markers in AML [3]. Despite the molecular mechanisms of leukemogenesis are well known, most AML patients are not cured. Notably, the currently available risk stratification systems are not completely accurate. Therefore, novel prognostic markers are needed to improve AML risk classification and select optimal therapeutic schedule. MicroRNAs (miRNAs) represent short noncoding RNAs, which hybridize to target mRNAs with high specificity and decrease protein levels through translation inhibition [4]. Dysregulation of miRNAs expression in AML can affect cell proliferation, survival and hematopoietic differentiation [5]. More importantly, abnormal expression of miRNAs is related to clinical outcomes of AML patients. For instance, high miR-181a level has been confirmed to predict favorable survival in cytogenetically normal AML cases [6]. Patients with high miR-212 level tend to have better outcome independently of cytogenetic subtype [7]. Moreover, high miR-3151 expression is associated with worse overall survival and disease-free survival in patients cytogenetically normal AML [8]. However, the majority of previous studies did not distinguish the various effects of chemotherapy and allo-HSCT treatment on the therapeutic outcome. As is well known, the prognostic impact of a marker is treatment-dependent in AML. Consequently, miRNAs may have different prognostic effects in chemotherapy and allo-HSCT treatment, respectively. In this study, we identify miR-363 that could predict clinical outcome in a heterogeneous AML population using genome-wide screening. The prognostic role of miR-363 is independent of known potent clinical and molecular predictors. The miR-363 expression contributed to risk classification in AML patients undergoing chemotherapy. We also evaluated whether allo-HCST could overcome the poor prognostic effects of high miR-363 level in the same cohort. In order to evaluate biological insights of miR-363, we performed genome-wide gene and miRNA expression analyses.

Materials and methods

Patients

We studied a total of 162 patients with newly diagnosed AML according to the WHO classification. The RNA-Seq expression data of these AML patients were provided by The Cancer Genome Atlas (TCGA) [9]. This study has been approved by Human Studies Committee of the Washington University. Patients with AML were included in a single center’s tissue protocol and followed NCCN guidelines to receive treatment. Patients with unfavorable risk underwent allo-HSCT if they were medically fit for the risks of transplantation, and if a suitably matched donor was available. In this cohort, 90 patients were only treated by chemotherapy and another 72 patients accepted both chemotherapy and allo-HSCT. All clinical data are available on the TCGA website.

Gene-expression profiling

Of the 162 patients, only 155 had both microRNA and mRNA expression data. For mRNA-seq data, genes expressed at or below a noise threshold of RPKM (Reads per kilobase per million mapped reads) ≤ 0.2 in at least 75% of samples were removed. For miRNA-seq data, read counts were normalized to RPM (Reads per million reads). The expression data were log2 transformed before analysis. The gene/microRNA expression signatures were derived by Spearman correlation analysis (Benjamini–Hochberg adjusted P value < 0.01). Finally, gene rows were reordered using hierarchical clustering analysis. The miRBase Targets Version 7 and Targetscan Release 7.1 were employed to predict the targets of miR-363. Gene Ontology enrichment assessment of genes in miR-363 related signature was conducted with the Database for Annotation, Visualization, and Integrated Discovery (DAVID).

Statistical analysis

A comparison of baseline characteristics between patients with high and low miRNA expression patients was conducted. The median miR-363 level was used to identify patients with low and high miRNA expression, respectively. Mann–Whitney U test was performed to test relations between two continuous variables. Fisher’s exact and Chi square tests were determined for categorical variables. Overall survival (OS) was the time from patient diagnosis to death at the final follow-up. Event-free survival (EFS) was the time from patient diagnosis to adverse events, including relapse and death. Kaplan–Meier method was performed to evaluate OS and EFS distributions and the log-rank test was employed to compare survival curves. Univariable Cox proportional hazards models were constructed for assessing correlations of miR-363 expressions with OS and EFS, respectively. We establish multivariable Cox proportional hazards models to identify factors affecting OS and EFS. The factors included in the evaluation model contained miR-363 expression levels, FLT3–ITD, NPM1, DNMT3A, RUNX1, TP53, TET2, MLL–PTD, IDH1/IDH2 and NRAS/KRAS mutation statuses, and WBC involvement. Factors showing significance with α = 0.20 in univariable analysis were entered into limited backward selection to generate multivariable models. Variables remaining in the final models were significant at α = 0.05. The R software 3.1.5, GraphPad Prism and SPSS were used for statistical analysis, with P < 0.05 indicating statistical significance.

Results

Association of miR-363 level with clinico-molecular properties

The patients were divided into two groups, chemotherapy and allo-HSCT groups. Subsequently, each group was subdivided into two groups in accordance with the median of miR-363. The relationship between clinical-genetic characteristics and miR-363 expression is shown in Table 1. In patients who underwent chemotherapy, cases with high miR-363 levels showed higher relapse rate (P = 0.001), and lower WBC count (P = 0.001) and circulating blast amounts (P = 0.007) at initial diagnosis in comparison with those expressing low miR-363 amounts. Patients with elevated miR-363 expression comprised less cases with favorable risk (P = 0.002), but more with poor risk of AML (P = 0.018). Furthermore, patients with high miR-363 expression included 92% of all cases with complex karyotypes and all cases with TP53 gene mutation. Meanwhile, Low miR-363 expressers encompassed all cases with the CBFβ-MYH11 fusion gene and 69% of all cases with NPM1 gene mutation.
Table 1

Comparison of clinical and molecular characteristics with miR-363 expression in AML patients

CharacteristicChemotherapy groupAllo-HSCT group
High miR-363 (n = 45)Low miR-363 (n = 45) P High miR-363 (n = 36)Low miR-363 (n = 36) P
Age/years, median68 (33–88)62 (22–77)0.00552 (18–65)51 (21–72)0.585
Age group, n (%), years0.0060.793
 < 608 (17.8)21 (46.7)25 (69.4)27 (75.0)
 ≥ 6037 (82.2)24 (53.3)11 (30.6)9 (25.0)
Gender, n (%)1.0000.634
 Male25 (55.6)25 (55.6)22 (55.6)19 (52.8)
 Female20 (44.4)20 (44.4)14 (38.9)17 (47.2)
WBC, × 109/L, median8.3 (0.7–171.9)39.8 (2.5–298.4)0.00111.4 (0.6–77.3)35.8 (1.2–223.8)0.001
BM blast (%), median71 (30–98)73 (32–99)0.58067.5 (30–95)71.5 (39–100)0.156
PB blast (%), median16 (0–91)52 (0–98)0.00733.5 (0–90)56 (8–96)0.008
FAB subtypes, n (%)
 M05 (11.1)3 (6.7)0.7147 (19.4)2 (5.6)0.151
 M19 (20)11 (24.4)0.80010 (37.8)13 (36.1)0.614
 M210 (22.2)11 (24.4)1.0009 (25.0)10 (27.8)1.000
 M410 (22.2)14 (31.1)0.4755 (13.9)9 (25.0)0.372
 M58 (17.8)5 (11.1)0.5503 (8.3)1 (2.8)0.614
 M62 (4.4)1 (2.2)1.0001 (2.8)01.000
 M71 (2.2)0 (0)1.0001 (2.8)01.000
 Others1 (2.2)0 (0)1.00001 (2.8)1.000
Karyotype, n (%)
 Normal20 (44.4)24 (53.3)0.52716 (44.4)18 (50.0)0.814
 Complex11 (24.4)1 (2.2)0.00411 (30.6)1 (2.8)0.003
 8 Trisomy02 (4.4)0.4942 (5.6)5 (13.9)0.429
 CBFβ–MYH1107 (15.6)0.01205 (13.9)0.054
 11q23/MLL4 (8.9)1 (2.2)0.3612 (5.6)1 (2.8)1.000
 −7/7q−2 (4.4)1 (2.2)1.0001 (2.8)1 (2.8)1.000
 BCR–ABL11 (2.2)01.0001 (2.8)1 (2.8)1.000
 RUNX1–RUNX1T1 (2.2)5 (11.1)0.20301 (2.8)1.000
 Others6 (13.3)4 (8.9)0.7393 (8.3)3 (8.3)1.000
Risk, n (%)
 Good1 (2.2)12 (26.7)0.00206 (16.7)0.025
 Intermediate25 (55.6)25 (55.6)1.00019 (52.8)22 (61.1)0.634
 Poor18 (40.0)7 (15.6)0.01817 (47.2)7 (19.4)0.023
 Others1 (2.2)1 (2.2)1.00001 (2.8)1.000
FLT3–ITD, n (%)0.7840.045
 Presence7 (15.6)9 (20.0)4 (11.1)12 (33.3)
 Absence38 (84.4)36 (80.0)32 (88.9)24 (66.7)
NPM1, n (%)0.0230.430
 Mutation9 (20.0)20 (44.4)8 (22.2)12 (33.3)
 Wild type36 (80.0)25 (55.6)28 (77.8)24 (66.7)
CEBPA, n (%)
 Single mutation1 (2.2)2 (4.4)1.00005 (13.9)0.054
 Double mutation001 (2.8)2 (5.6)1.000
 Wild type44 (97.8)43 (95.6)1.00035 (97.2)29 (80.6)0.055
DNMT3A, n (%)10000.786
 Mutation13 (28.9)12 (26.7)10 (27.8)8 (22.2)
 Wild type32 (71.1)33 (73.3)26 (72.2)28 (7.8)
IDH1/IDH2, n (%)0.1670.415
 Mutation5 (11.1)11 (24.4)11 (30.6)7 (19.4)
 Wild type40 (88.9)34 (75.6)25 (69.4)29 (80.6)
RUNX1, n (%)0.7140.260
 Mutation5 (11.1)3 (6.7)6 (16.7)2 (5.6)
 Wild type40 (88.9)42 (93.3)30 (83.3)34 (94.4)
MLL–PTD, n (%)1.0000.614
 Presence2 (4.4)3 (6.7)3 (8.3)1 (2.8)
 Absence43 (95.6)42 (93.7)33 (91.7)35 (97.2)
NRAS/KRAS, n (%)1.0001.000
 Mutation6 (13.3)7 (15.6)4 (11.1)3 (8.3)
 Wild type39 (86.7)38 (84.4)32 (88.9)33 (91.7)
TET2, n (%)0.1180.614
 Mutation9 (20.0)3 (6.7)1 (2.8)3 (8.3)
 Wild type36 (80.0)42 (93.7)35 (97.2)33 (91.7)
TP53, n (%)0.0000.115
 Mutation11 (24.4)04 (11.1)0
 Wild type34 (75.6)45 (100.0)32 (88.9)36 (100.0)
Relapse, n (%)0.0010.474
 Yes42 (93.3)28 (62.2)23 (63.9)19 (47.2)
 No3 (6.7)17 (37.8)13 (36.1)17 (52.8)

Mann–Whitney test was used for continuous variables. Chi square tests were used for categorical variables

WBC white blood cell, BM bone marrow, PB peripheral blood, FAB French–American–British classification

Comparison of clinical and molecular characteristics with miR-363 expression in AML patients Mann–Whitney test was used for continuous variables. Chi square tests were used for categorical variables WBC white blood cell, BM bone marrow, PB peripheral blood, FAB French–American–British classification

Prognostic value of miR-363 expression in patients treated with chemotherapy or allo-HSCT

We performed genome-wide screening of miRNAs in AML cases in order to acquire prognostic marker to improve the classification of AML. MiR-363 was identified as a new prognostic marker for chemotherapy in AML patients. In order to evaluate survival of patients, we employed the Kaplan–Meier method and log-rank test. The expression level distribution of miR-363 was shown in Fig. 1a. In the chemotherapy group, cases highly expressing miR-363 showed reduced OS (HR = 2.28, P = 0.0004) and EFS (HR = 2.14, P = 0.0012) in comparison with low expressers (Fig. 1b). We further performed a survival analysis in the good/intermediate group, patients with high miR-363 expression had significantly shorter OS (P = 0.0009) and EFS (P = 0.0019) compared with patients with low miR-363 expression (Fig. 1c). However, miR-363 expression level was not associated with outcome in AML patients treated with allo-HCST (Fig. 1d). These data suggested that high expression of miR-363 was a poor prognostic factor in AML patients treated with chemotherapy.
Fig. 1

Kaplan–Meier survival curves based on miR-363 expression. a The expression level distribution of miR-363. b Cases highly expressing miR-363 showed markedly shorter OS and EFS in the chemotherapy group (n = 90). c Patients with high miR-363 expression had poor OS and EFS in the chemotherapy group. d Effect of miR-363 levels on OS and EFS in cases administered allo-HSCT (n = 72)

Kaplan–Meier survival curves based on miR-363 expression. a The expression level distribution of miR-363. b Cases highly expressing miR-363 showed markedly shorter OS and EFS in the chemotherapy group (n = 90). c Patients with high miR-363 expression had poor OS and EFS in the chemotherapy group. d Effect of miR-363 levels on OS and EFS in cases administered allo-HSCT (n = 72)

MiR-363 is associated with clinical outcome in AML

Univariate and multivariate cox analyses were performed to assess whether miR-363 level is an independent predictor of survival in AML. Univariate analysis (Table 2) showed that high miR-363 had an adverse prognostic value for predicting OS (HR = 2.389, P < 0.001) and EFS (HR = 2.224, P = 0.001) in cases administered chemotherapy. In multivariable analysis, miR-363 and multiple demonstrated prognostic factors were assessed (Table 2). High miR-363 remained an independent predictor of shorter OS (HR = 2.349, 95% CI 1.305–4.229, P = 0.012) and EFS (HR = 2.082, 95% CI 1.172–3.699, P = 0.001).
Table 2

Univariate and multivariate analyses in patients treated with chemotherapy

VariablesEFSOS
HR (95% CI)P-valueHR (95% CI)P-value
Univariate analyses
 MiR-363 (high vs. low)2.224 (1.369–3.612)0.0012.389 (1.468–3.887)0.000
 WBC (≥ 20 vs. < 20 × 109/L)1.015 (0.633–1.627)0.9520.980 (0.611–1.571)0.932
 FLT3–ITD (positive vs. negative)1.095 (0.587–2.040)0.7761.049 (0.563–1.956)0.880
 NPM1 (mutated vs. wild)1.050 (0.633–1.741)0.8500.965 (0.582–1.599)0.890
 DNMT3A (mutated vs. wild)1.301 (0.774–2.185)0.3201.299 (0.775–2.179)0.321
 RUNX1 (mutated vs. wild)1.502 (0.717–3.147)0.2811.591 (0.759–3.335)0.219
 TP53 (mutated vs. wild)3.011 (1.539–5.892)0.0012.898 (1.487–5.649)0.002
 TET2 (mutated vs. wild)0.778 (0.372–1.625)0.5040.686 (0.328–1.434)0.316
 MLL–PTD (mutated vs. wild)0.891 (0.324–2.445)0.8220.945 (0.344–2.596)0.913
 IDH1/IDH2 (mutated vs. wild)0.973 (0.271–1.273)0.9260.988 (0.550–1.777)0.969
 NRAS/KRAS (mutated vs. wild)1.214 (0.637–2.314)0.5561.228 (0.644–2.340)0.532
Multivariate analyses
 MiR-363 (high vs. low)2.362 (1.346–4.145)0.0032.683 (1.507–4.779)0.001
 WBC (≥ 20 vs. < 20 × 109/L)1.806 (1.036–3.151)0.0371.861 (1.056–3.280)0.032
 RUNX1 (mutated vs. wild)1.706 (0.797–3.654)0.1691.819 (0.850–3.892)0.123
 TP53 (mutated vs. wild)2.786 (1.312–5.915)0.0082.566 (1.221–5.395)0.013

EFS event-free survival, OS overall survival, WBC white blood cell

Univariate and multivariate analyses in patients treated with chemotherapy EFS event-free survival, OS overall survival, WBC white blood cell In patients receiving allo-HSCT, univariate analysis showed that adverse OS in patients with TP53-mutant. However, miR-363 expression status was not associated with OS and EFS in the allo-HSCT group (Table 3). Multivariable analysis revealed that TP53 and FLT3–ITD mutations independently predict adverse OS (P = 0.002 and P = 0.049, respectively). The miR-363 expression status did not persist as OS and EFS predictors in multivariable analysis.
Table 3

Univariate and multivariate analyses in patients treated with allo-HSCT

VariablesEFSOS
HR (95% CI)P-valueHR (95% CI)P-value
Univariate analyses
 MiR-363 (high vs. low)1.182 (0.643–2.175)0.5901.424 (0.775–2.619)0.255
 WBC (≥ 20 vs. < 20 × 109/L)1.089 (0.594–1.999)0.7820.826 (0.450–1.516)0.537
 FLT3–ITD (positive vs. negative)1.876 (0.914–3.851)0.0861.973 (0.953–4.084)0.067
 NPM1 (mutated vs. wild)1.007 (0.515–1.970)0.9831.023 (0.523–1.998)0.948
 DNMT3A (mutated vs. wild)1.285 (0.655–2.520)0.4661.387 (0.704–2.731)0.344
 RUNX1 (mutated vs. wild)1.145 (0.449–2.290)0.7771.579 (0.613–4.067)0.344
 TP53 (mutated vs. wild)2.034 (0.718–5.760)0.1814.334 (1.453–12.925)0.009
 TET2 (mutated vs. wild)0.526 (0.127–2.186)0.3770.670 (0.162–2.776)0.581
 MLL–PTD (mutated vs. wild)5.775 (1.664–20.042)0.0062.728 (0.832–8.944)0.098
 IDH1/IDH2 (mutated vs. wild)0.587 (0.271–1.273)0.1770.633 (0.293–1.368)0.245
 NRAS/KRAS (mutated vs. wild)0.796 (0.245–2.586)0.7050.488 (0.150–1.587)0.233
Multivariate analyses
 MLL–PTD (mutated vs. wild)5.180 (1.449–18.511)0.0113.136 (0.943–10.429)0.062
 FLT3–ITD (positive vs. negative)1.837 (0.868–3.888)0.1122.301 (1.090–4.860)0.029
 TP53 (mutated vs. wild)2.493 (0.860–7.226)0.0925.848 (1.885–18.142)0.002

EFS event-free survival, OS overall survival, WBC white blood cell

Univariate and multivariate analyses in patients treated with allo-HSCT EFS event-free survival, OS overall survival, WBC white blood cell

Allo-HSCT overcomes the adverse prognostic role of miR-363 expression

Next, we investigated whether allo-HSCT could overcome the adverse outcomes of miR-363 expression. The 162 patients were divided into 2 groups according to the median level of miR-363. In the high miR-363 group, cases administered allo-HSCT showed markedly improved OS (HR = 0.361, 95% CI 0.225–0.588, P < 0.0001) and EFS (HR = 0.447, 95% CI 0.287–0.751, P = 0.002) in comparison with cases administered chemotherapy (Fig. 2a). In patients with lower expression of miR-363, no marked differences in OS (P = 0.127) and EFS (P = 0.226) were found between the chemotherapy and allo-HSCT groups (Fig. 2b). These results suggested that miR-363 may be considered as a prognostic marker for the detection of patients requiring optimal therapeutic schedules.
Fig. 2

Allo-HSCT overcomes the adverse prognostic influence of high miR-363 expression in AML. a The 162 cases were divided into two groups according to median miR-363 levels. Kaplan–Meier survival curves for cases administered chemotherapy (n = 53) and allo-HSCT (n = 28), respectively, in the high miR-363 group. b Kaplan–Meier survival curves for cases administered chemotherapy (n = 37) and allo-HSCT (n = 44), respectively, in the low miR-363 group

Allo-HSCT overcomes the adverse prognostic influence of high miR-363 expression in AML. a The 162 cases were divided into two groups according to median miR-363 levels. Kaplan–Meier survival curves for cases administered chemotherapy (n = 53) and allo-HSCT (n = 28), respectively, in the high miR-363 group. b Kaplan–Meier survival curves for cases administered chemotherapy (n = 37) and allo-HSCT (n = 44), respectively, in the low miR-363 group

Biological insight of miR-363 expression in AML

To further investigate the biological function of miR-363, gene expression signature associated with miRNA expression was determined in AML cases. We observed that the levels of 178 genes were strongly associated with miR-363 expression, including 130 and 48 with positive and negative correlations, respectively (Fig. 3). Differentially upregulated genes in patients with high miR-363 expression included leukemogenic transcription factors (Myb, RUNX3, GATA3, IKZF3, HMGA2 and ETS1) [10, 11]. Notably, MLLT3/AF9 was up-regulated in the high miR-363 group, as a frequent fusion partner of the MLL gene in translocations t(9;11)(p22;q23) related to AML [12]. Among downregulated genes, we found that miR-363 expression showed negative correlations with the levels of tumor suppressor genes (EZH2, KLF6 and PTEN). Interestingly, these three genes were predicted miR-363 targets according to in silico analysis. Gene Ontology showed that genes associated with cell migration, T cell activation, system development, cell differentiation, response to chemicals and immune response were significantly correlated with miR-363 expression (Table 4). Thus, the miR-363 associated gene-expression profiling signature supported clinical finding in AML obtained by miRNA analysis.
Fig. 3

Heat map of the gene expression signature related to miR-363 expression in AML. Cases (columns) were ordered from left to right by increasing miR-363 levels. Genes (rows) were ordered by hierarchical cluster analysis. Blue and red reflect expression levels below and above median values for respective genes, respectively; miR-363 associated genes are indicated

Table 4

Gene ontology terms of biological processes in the miR-363 associated expression profile

GO IDGO termsPercentage of members of the GO term present in the miR-363 profileP-value
GO:0050789Regulation of biological process69.5< 0.001
GO:0050794Regulation of cellular process64.4< 0.001
GO:0051716Cellular response to stimulus44.10.009
GO:0007154Cell communication40.70.009
GO:0032502Developmental process39.00.002
GO:0007275Multicellular organism development37.3< 0.001
GO:0048731System development33.9< 0.001
GO:0048869Cellular developmental process29.40.003
GO:0030154Cell differentiation28.20.001
GO:0042221Response to chemical27.70.025
GO:0010033Response to organic substance20.30.026
GO:0070887Cellular response to chemical stimulus19.80.018
GO:0009605Response to external stimulus16.90.009
GO:0045595Regulation of cell differentiation15.30.001
GO:0006955Immune response13.60.012

GO gene ontology

Heat map of the gene expression signature related to miR-363 expression in AML. Cases (columns) were ordered from left to right by increasing miR-363 levels. Genes (rows) were ordered by hierarchical cluster analysis. Blue and red reflect expression levels below and above median values for respective genes, respectively; miR-363 associated genes are indicated Gene ontology terms of biological processes in the miR-363 associated expression profile GO gene ontology

Discussion

As current molecular stratification schemes do not fully grasp the heterogeneity of prognosis in patients with AML, the identification of novel prognostic markers is urgent [13]. In heterogeneous cohorts of AML patients, the correlation of miRNAs as predictive molecular markers remains largely unknown. In this study, miR-363 was determined as an independent prognostic factor in AML cases undergoing chemotherapy. Meanwhile, the miR-363 expression provides a powerful tool for risk stratification of AML patients. More importantly, allo-HSCT can overcome miR-363 expression-associated adverse outcomes. We showed that miR-363 expression levels constitute independent prognostic marker of AML in a heterogeneous cohort administered chemotherapy. High miR-363 levels could still predict adverse outcome after consideration of other molecular prognostic factors in multivariable analysis. Thus, miR-363 could increase the prognostic value of previously defined molecular factors in a highly heterogeneous cohort of AML cases. Strikingly, patients with high miR-363 expression levels showed markedly poor OS and EFS. These findings suggest that miR-363 independently influences treatment outcomes and may synergistically drive leukemogenesis. More importantly, miR-363 expression levels could be useful to the identification of patients with adverse outcome in AML patients administrated chemotherapy. Conventional chemotherapy and allo-HCST constitute the standard post-remission treatment strategies for AML [14]. However, there is a lack of efficient prognostic markers for guiding rational treatment options. We found that high miR-363 expressers administered allo-HSCT showed markedly improved OS and EFS in comparison with cases administered chemotherapy. In cases lowly expressing miR-363, there was no advantage for those administered allo-HSCT in comparison with the chemotherapy group. These findings indicate that patients with low miR-363 expression may not benefit from allo-HSCT as first-line therapy. Therefore, the expression of miR-363 may contribute to identify patients in need of strategies to select the optimal treatment regimen between chemotherapy and allo-HCST. The AML patients with high miR-363 expression may be preferably recommended for early allo-HSCT. The possible oncogenic function of miR-363 has been reported previously in T-cell acute lymphoblastic leukemia, multiple myeloma and solid tumors [15, 16]. MiR-363 promotes growth and chemo-resistance in gastric adenocarcinoma by downregulating FBW7 [17]. It was shown that miR-363 is a prognostic marker for hepatocellular carcinoma [18]. However, the function and prognostic role of miR-363 in AML remains unclear. To derive biological insights from AML cases characterized by high miR-363 expression, we identified genes associated with miR-363 expression in AML patients. Interestingly, miR-363 expression was positively correlated with the amounts of leukemogenic transcription factors, including Myb, RUNX3, GATA3, IKZF3, ETS1 and MLLT3. The Myb oncogene, a driver of leukemogenesis, is widely expressed in AML and important for continued proliferation and differentiation blocking activity in AML cells [19]. ETS1 is critical in cell proliferation and differentiation in AML [20]. MLLT3 represents a commonly encountered fusion partner of MLL in translocations t(9;11)(p22;q23), which are related to AML [21]. Notably, we found that the direct target genes (EZH2, KLF6 and PTEN) of miR-363 were downregulated in association with high miR-363 expression. It was shown that loss-of-function mutations of the tumor suppressor gene EZH2 are found in AML [22]. Meanwhile, PTEN plays an essential role in the prevention of leukemogenesis [23, 24]. Indeed, PTEN deletion in hematopoietic cells can induce a myeloproliferative disease within days and transplantable leukemias within weeks. These miR-363 associated genes may participate in the adverse response to chemotherapy in cases highly expressing miR-363. Therefore, the miR-363 related gene-expression profiling signature may support the clinical observation that AML is characterized by the expression of miRNA. However, the mechanisms concerning the regulation of miR-363 expression and subsequent influence of AML treatment outcome remain to be elucidated.

Conclusions

In conclusion, miR-363 levels independently correlate with clinical outcome in a highly heterogeneous cohort of AML cases. MiR-363 expression could greatly contribute to the identification of patients with poor outcome in AML. Expression analysis of miR-363 may be useful to improve the risk stratification of AML patients. Furthermore, allo-HSCT may overcome the unfavorable consequences of high miR-363 expression in AML. Therefore, the expression analysis of miR-363 may help identify cases in need of strategies to select the optimal treatment regimen between chemotherapy and allo-HCST.
  24 in total

1.  PTEN maintains haematopoietic stem cells and acts in lineage choice and leukaemia prevention.

Authors:  Jiwang Zhang; Justin C Grindley; Tong Yin; Sachintha Jayasinghe; Xi C He; Jason T Ross; Jeffrey S Haug; Dawn Rupp; Kimberly S Porter-Westpfahl; Leanne M Wiedemann; Hong Wu; Linheng Li
Journal:  Nature       Date:  2006-04-23       Impact factor: 49.962

2.  Genomic Classification and Prognosis in Acute Myeloid Leukemia.

Authors:  Elli Papaemmanuil; Moritz Gerstung; Hartmut Döhner; Peter J Campbell; Lars Bullinger; Verena I Gaidzik; Peter Paschka; Nicola D Roberts; Nicola E Potter; Michael Heuser; Felicitas Thol; Niccolo Bolli; Gunes Gundem; Peter Van Loo; Inigo Martincorena; Peter Ganly; Laura Mudie; Stuart McLaren; Sarah O'Meara; Keiran Raine; David R Jones; Jon W Teague; Adam P Butler; Mel F Greaves; Arnold Ganser; Konstanze Döhner; Richard F Schlenk
Journal:  N Engl J Med       Date:  2016-06-09       Impact factor: 91.245

3.  Molecular studies reveal a MLL-MLLT3 gene fusion displaced in a case of childhood acute lymphoblastic leukemia with complex karyotype.

Authors:  Daniela Ribeiro Ney Garcia; Thomas Liehr; Mariana Emerenciano; Claus Meyer; Rolf Marschalek; Maria do Socorro Pombo-de-Oliveira; Raul C Ribeiro; Marcelo Gerardin Poirot Land; Maria Luiza Macedo Silva
Journal:  Cancer Genet       Date:  2015-02-19

4.  miR-3151 interplays with its host gene BAALC and independently affects outcome of patients with cytogenetically normal acute myeloid leukemia.

Authors:  Ann-Kathrin Eisfeld; Guido Marcucci; Kati Maharry; Sebastian Schwind; Michael D Radmacher; Deedra Nicolet; Heiko Becker; Krzysztof Mrózek; Susan P Whitman; Klaus H Metzeler; Jason H Mendler; Yue-Zhong Wu; Sandya Liyanarachchi; Ravi Patel; Maria R Baer; Bayard L Powell; Thomas H Carter; Joseph O Moore; Jonathan E Kolitz; Meir Wetzler; Michael A Caligiuri; Richard A Larson; Stephan M Tanner; Albert de la Chapelle; Clara D Bloomfield
Journal:  Blood       Date:  2012-04-23       Impact factor: 22.113

5.  The prognostic relevance of miR-212 expression with survival in cytogenetically and molecularly heterogeneous AML.

Authors:  S M Sun; V Rockova; L Bullinger; M K Dijkstra; H Döhner; B Löwenberg; M Jongen-Lavrencic
Journal:  Leukemia       Date:  2012-06-13       Impact factor: 11.528

6.  Oncogenic potential of the miR-106-363 cluster and its implication in human T-cell leukemia.

Authors:  Séverine Landais; Sébastien Landry; Philippe Legault; Eric Rassart
Journal:  Cancer Res       Date:  2007-06-15       Impact factor: 12.701

7.  Interaction of c-Myb with p300 is required for the induction of acute myeloid leukemia (AML) by human AML oncogenes.

Authors:  Diwakar R Pattabiraman; Crystal McGirr; Konstantin Shakhbazov; Valerie Barbier; Keerthana Krishnan; Pamela Mukhopadhyay; Paula Hawthorne; Ann Trezise; Jianmin Ding; Sean M Grimmond; Peter Papathanasiou; Warren S Alexander; Andrew C Perkins; Jean-Pierre Levesque; Ingrid G Winkler; Thomas J Gonda
Journal:  Blood       Date:  2014-03-04       Impact factor: 22.113

8.  Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia.

Authors:  Timothy J Ley; Christopher Miller; Li Ding; Benjamin J Raphael; Andrew J Mungall; A Gordon Robertson; Katherine Hoadley; Timothy J Triche; Peter W Laird; Jack D Baty; Lucinda L Fulton; Robert Fulton; Sharon E Heath; Joelle Kalicki-Veizer; Cyriac Kandoth; Jeffery M Klco; Daniel C Koboldt; Krishna-Latha Kanchi; Shashikant Kulkarni; Tamara L Lamprecht; David E Larson; Ling Lin; Charles Lu; Michael D McLellan; Joshua F McMichael; Jacqueline Payton; Heather Schmidt; David H Spencer; Michael H Tomasson; John W Wallis; Lukas D Wartman; Mark A Watson; John Welch; Michael C Wendl; Adrian Ally; Miruna Balasundaram; Inanc Birol; Yaron Butterfield; Readman Chiu; Andy Chu; Eric Chuah; Hye-Jung Chun; Richard Corbett; Noreen Dhalla; Ranabir Guin; An He; Carrie Hirst; Martin Hirst; Robert A Holt; Steven Jones; Aly Karsan; Darlene Lee; Haiyan I Li; Marco A Marra; Michael Mayo; Richard A Moore; Karen Mungall; Jeremy Parker; Erin Pleasance; Patrick Plettner; Jacquie Schein; Dominik Stoll; Lucas Swanson; Angela Tam; Nina Thiessen; Richard Varhol; Natasja Wye; Yongjun Zhao; Stacey Gabriel; Gad Getz; Carrie Sougnez; Lihua Zou; Mark D M Leiserson; Fabio Vandin; Hsin-Ta Wu; Frederick Applebaum; Stephen B Baylin; Rehan Akbani; Bradley M Broom; Ken Chen; Thomas C Motter; Khanh Nguyen; John N Weinstein; Nianziang Zhang; Martin L Ferguson; Christopher Adams; Aaron Black; Jay Bowen; Julie Gastier-Foster; Thomas Grossman; Tara Lichtenberg; Lisa Wise; Tanja Davidsen; John A Demchok; Kenna R Mills Shaw; Margi Sheth; Heidi J Sofia; Liming Yang; James R Downing; Greg Eley
Journal:  N Engl J Med       Date:  2013-05-01       Impact factor: 91.245

Review 9.  Clinical significance of microRNAs in chronic and acute human leukemia.

Authors:  Chien-Hung Yeh; Ramona Moles; Christophe Nicot
Journal:  Mol Cancer       Date:  2016-05-14       Impact factor: 27.401

10.  MiR-425 expression profiling in acute myeloid leukemia might guide the treatment choice between allogeneic transplantation and chemotherapy.

Authors:  Chen Yang; Tingting Shao; Huihui Zhang; Ninghan Zhang; Xiaoying Shi; Xuejiao Liu; Yao Yao; Linyan Xu; Shengyun Zhu; Jiang Cao; Hai Cheng; Zhiling Yan; Zhenyu Li; Mingshan Niu; Kailin Xu
Journal:  J Transl Med       Date:  2018-10-01       Impact factor: 5.531

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  5 in total

Review 1.  Implication of microRNAs in Carcinogenesis with Emphasis on Hematological Malignancies and Clinical Translation.

Authors:  Zsuzsanna Gaál
Journal:  Int J Mol Sci       Date:  2022-05-23       Impact factor: 6.208

2.  High expression of miR-25 predicts favorable chemotherapy outcome in patients with acute myeloid leukemia.

Authors:  Mingshan Niu; Yuan Feng; Ninghan Zhang; Tingting Shao; Huihui Zhang; Rong Wang; Yao Yao; Ruosi Yao; Qingyun Wu; Jiang Cao; Xuejiao Liu; Yubo Liu; Kailin Xu
Journal:  Cancer Cell Int       Date:  2019-05-07       Impact factor: 5.722

3.  MiR-340 Is a Biomarker for Selecting Treatment Between Chemotherapy and Allogeneic Transplantation in Acute Myeloid Leukemia.

Authors:  Mingshan Niu; Ninghan Zhang; Rong Wang; Tingting Shao; Yuan Feng; Yangling Shen; Xuejiao Liu; Kai Zhao; Shengyun Zhu; Linyan Xu; Yao Yao; Kailin Xu
Journal:  Front Oncol       Date:  2019-10-11       Impact factor: 6.244

4.  miR-143-3p represses leukemia cell proliferation by inhibiting KAT6A expression.

Authors:  Dan Xu; Jinlong Jiang; Guangsheng He; Haixia Zhou; Chengfu Ji
Journal:  Anticancer Drugs       Date:  2022-01-01       Impact factor: 2.248

5.  Identification of Acute Myeloid Leukemia Bone Marrow Circulating MicroRNAs.

Authors:  Douâa Moussa Agha; Redouane Rouas; Mehdi Najar; Fatima Bouhtit; Najib Naamane; Hussein Fayyad-Kazan; Dominique Bron; Nathalie Meuleman; Philippe Lewalle; Makram Merimi
Journal:  Int J Mol Sci       Date:  2020-09-25       Impact factor: 5.923

  5 in total

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