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  • Primary research
  • Open Access

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

  • 1, 2,
  • 1,
  • 1,
  • 1,
  • 1,
  • 1,
  • 1, 2,
  • 1, 2,
  • 1, 2,
  • 2,
  • 1, 3,
  • 4Email author and
  • 1, 2Email author
Contributed equally
Cancer Cell International201919:122

https://doi.org/10.1186/s12935-019-0843-9

  • Received: 17 March 2019
  • Accepted: 29 April 2019
  • Published:

Abstract

Background

Acute myeloid leukemia (AML) pertains to a hematologic malignancy with heterogeneous therapeutic responses. Improvements in risk stratification in AML patients are warranted. MicroRNAs have been associated with the pathogenesis of AML.

Methods

To examine the prognostic value of miR-25, 162 cases with de novo AML were classified into two groups according to different treatment regimens.

Results

In the chemotherapy group, cases with upregulated miR-25 expression showed relatively longer overall survival (OS; P = 0.0086) and event-free survival (EFS; P = 0.019). Multivariable analyses revealed that miR-25 upregulation is an independent predictor for extended OS (HR = 0.556, P = 0.015) and EFS (HR = 0.598, P = 0.03). In addition, allogeneic hematopoietic stem cell transplantation (allo-HSCT) circumvented the poor prognosis that was related to miR-25 downregulation with chemotherapy. The expression level pattern of miR-25 coincided with AML differentiation and proliferation, which included HOXA and HOXB cluster members, as well as the HOX cofactor MEIS1. The MYH9 gene was identified as a direct target of miR-25.

Conclusions

The miR-25 levels are correlated with prognosis in AML independently of other powerful molecular markers. The expression of miR-25 may contribute to the selection of the optimal treatment regimen between chemotherapy and allo-HCST for AML patients.

Keywords

  • Acute myeloid leukemia
  • miR-25
  • Clinical outcome
  • Chemotherapy
  • Allo-HSCT

Background

Acute myeloid leukemia (AML) is a group of clonal malignant diseases that derive from the hematopoietic stem cells. AML is characterized by a large group of germinal cells, which leads to a loss of normal hematopoietic function [1]. The clinical prognosis of patients with AML is various. The differences in outcomes among AML patients depend on multiple intrinsic factors [2, 3]. With the development of methodologies of massive sequencing, it has been demonstrated that somatic mutations in NPM1, FLT3, CEBPA, IDH1 and IDH2 are connected to prognosis in AML [4]. To be specific, patients with mutated FLT3 have a dismal outcome, while mutations in NPM1 and CEBPA are related with favorable prognosis. The advent of chemotherapy and allogeneic hematopoietic stem cell transplantation (allo-HSCT) has significantly improved AML treatment outcomes [5]. Relapse and refractory of leukemia remain the most disturbing problems in AML patients [6]. Thus, it is urge to explore more reliable and effective prognostic biomarkers to enhance the capacity of prediction and thus improve the outcome of AML by choosing optimal therapeutic approach.

MicroRNAs are short non-coding RNAs, which are implicated in a diverse group of critical cellular mechanisms, such as apoptosis, differentiation, cell cycle progression, and immune responses [7]. Recently, more and more attentions have been focused on the prognostic role of microRNAs in AML. A recent study has shown that the upregulation of miR-181a facilitates better survival of AML patients who are cytogenetically normal [8]. However, AML patients who are cytogenetically normal and upregulated miR-212 and miR-3151 have shorter overall and disease-free survival [9, 10]. However, most microRNA analyses did not differentiate the AML patients treated with chemotherapy and allo-HSCT. Thus, microRNAs may have varied prognostic roles in chemotherapy and allo-HSCT treatment group, respectively.

MiR-25, a member of miR-106b-25 cluster, is located on human chromosome 7q22.1 [11]. Previous studies revealed that miR-25 was involved in many kinds of cancers [12]. It has been identified that miR-25 is a potential biomarker for pediatric AML based on Pipeline of Outlier MicroRNA Analysis (POMA) model [13]. More important, Garzon et al. [14] reported that miR-25 is significantly down-regulated in 122 newly diagnosed AML samples compared with CD34+ normal cells. However, clinical and prognostic role of miR-25 in AML are still unclear. A total of 162 recently diagnosed de novo AML patients were enrolled in this evaluation. The cases were placed into two groups based on the treatment that they received. The present study suggested that miR-25 is a solitary AML prognostic biomarker. Furthermore, our study revealed that allo-HSCT would be more beneficial to patients showing downregulated miR-25.

Patients and methods

Patients

Approximately 162 patients with a diagnosis of de novo AML were included in this study. The data sets used in this investigation were acquired from The Cancer Genome Atlas (TCGA). A single-institution tissue banking strategy endorsed by the human studies committee of Washington University was used in this study. All of the patients provided their written informed consent. AML diagnosis and classification were made according to the French–American–British (FAB) and the World Health Organization (WHO) criteria. The cases were placed into two groups according to the clinical treatment received. Ninety patients accepted chemotherapy, and the rest accepted allo-HSCT.

Gene-expression profiling

The samples from 155 patients both had been obtained mRNA and microRNA expression data. These data were applied to identify the mRNA-expression signature associated with miR-25 expression. The sequencing read count for each miRNA was normalized to Reads per million reads (RPM). The mRNA expression values were logged (base 2) prior to analysis [15]. Spearman correlation was used to correlate the mRNA-expression profile with miR-25 expression. Hierarchical clustering analysis was used to reorder the gene rows. To screen for target genes of miR-25, Targetscan, miRNApath and miRDB website tools were implemented. Gene Ontology enrichment assessment of genes in miR-340 related signature was performed with the Database for Annotation, Visualization, and Integrated Discovery (DAVID).

Statistical analysis

The clinical endpoints of this investigation on treatment outcomes included overall survival (OS) and event-free survival (EFS). OS pertains to the time interval from diagnosis to death or last follow-up of the patient. EFS is described as the time interval from diagnosis to disease progression, relapse, or death attributed to any cause. The patients were assigned to the high or low expression groups based on the median miR-25 expression. Descriptive statistics (median and/or range) were used to summarize patients’ clinical and molecular characteristics. To elucidate the role of miR-25 expression in AML clinical and molecular features, the Pearson Chi-square and Fisher’s exact tests were used to screen for significant differences between two categorical variables. In addition, the Mann–Whitney’s U test was used for continuous variables. For univariable and multivariable analysis, a Cox proportional hazards model was employed to determine the effect of various risk factors on patient OS and EFS. The limited backward elimination procedure was applied to assess hazard ratios (HRs) and P values. Kaplan–Meier analysis was performed to determine the impact of miR-25 expression on OS and EFS. Statistical analysis was conducted with SPSS and GraphPad Prism. Differences among variables were determined to be statistically significant when the P value was < 0.05.

Results

Correlation analysis of miR-25 expression and clinical characteristics

To establish the correlation among miR-25 expression and various clinical profiles, we assigned the patients who underwent chemotherapy and allo-HSCT to one of two groups according to median miR-25 expression levels, respectively. The associations of the clinical features with miR-25 expression levels are summarized in Table 1. In the chemotherapy group, subjects who exhibited upregulated miR-25 had a higher percentage of RUNX1-RUNX1T1 compared to those with downregulated expression (P = 0.026). In addition, high miR-25 expresser involved in more good risk cases of AML (P = 0.002). However, no significant differences were observed in gender, age, WBC count, BM blast, PB blast, FAB subtypes, FLT3-ITD, NPM1, DNMT3A, RUNX1, MLL-PTD, TP53, IDH1 and IDH2 among the high and low miR-25 expression group. In the allo-HSCT group, study participants with upregulated miR-25 exhibited a lower frequency for FLT3-ITD mutations (P = 0.045) compared to those with downregulated miR-25. No significant differences in as far as gender, age, WBC count, BM blast, PB blast, and mutations in the NPM1, RUNX1, DNMT3A, MLL-PTD, IDH1, IDH2, and TP53 genes were observed among the upregulated and downregulated miR-25 groups.
Table 1

Comparison of clinical and molecular characteristics with miR-25 expression in patients with AML

Characteristic

Chemotherapy group

Allo-HSCT group

High miR-25

(n = 45)

Low miR-25

(n = 45)

P

High miR-25

(n = 36)

Low miR-25

(n = 36)

P

Age/years, median

61.4 (22–82)

64.4 (31–88)

0.49

47.3 (22–72)

49.4 (18–69)

0.535

Age group/n (%) (years)

  

0.495

  

0.793

 < 60

16 (35.6)

12 (26.7)

 

27 (75)

25 (69.4)

 

 ≥ 60

29 (64.4)

33 (73.3)

 

9 (25)

11 (30.6)

 

Gender/n (%)

  

0.289

  

0.634

 Male

22 (48.9)

28 (62.2)

 

22 (61.1)

19 (52.8)

 

 Female

23 (51.1)

17 (37.8)

 

14 (38.9)

17 (47.2)

 

WBC/× 109/L, median

32.4 (0.7–297.4)

51.8 (1.5–298.4)

0.059

36.0 (0.6–223.8)

39.8 (1.2–118.8)

0.248

BM blast/%, median

69 (32–99)

67.2 (30–92)

0.955

66.2 (34–99)

70.2 (30–100)

0.277

PB blast/%, median

39.5 (0–98)

35.1 (0–97)

0.320

46.1 (0–96)

48.9 (0–94)

0.752

FAB subtypes/n (%)

 M0

4 (8.9)

4 (8.9)

1.000

6 (16.7)

3 (8.3)

0.478

 M1

13 (28.9)

7 (15.6)

0.204

7 (19.4)

16 (44.4)

0.042

 M2

12 (26.7)

9 (20)

0.619

12 (33.3)

7 (19.4)

0.285

 M4

11 (24.4)

13 (28.9)

0.812

8 (22.2)

6 (16.7)

0.767

 M5

4 (8.9)

9 (20)

0.230

2 (5.6)

2 (5.6)

1.000

 M6

1 (2.2)

0 (0.0)

1.000

0 (0.0)

1 (2.8)

1.000

 M7

0 (0.0)

2 (4.4)

0.494

1 (2.8)

0 (0.0)

1.000

 No date

0 (0.0)

1 (2.2)

1.000

0 (0.0)

1 (2.8)

1.000

Karyotype/n (%)

 Normal

18 (40)

26 (57.8)

0.140

15 (41.7)

19 (52.7)

0.479

 Complex

5 (11.1)

7 (15.6)

0.758

6 (16.6)

6 (16.6)

1.000

 Poor

0 (0.0)

5 (11.1)

0.056

1 (2.8)

4 (11.1)

0.357

 Intermediate

8 (17.8)

2 (4.4)

0.090

6 (16.7)

3 (8.4)

0.478

 MLL

1 (2.2)

2 (4.4)

1.000

3 (8.3)

0 (0.0)

0.239

 CBFβ-MYH11

6 (13.3)

1 (2.2)

0.110

4 (11.1)

1 (2.8)

0.357

 BCR-ABL1

0 (0.0)

1 (2.2)

1.000

0 (0.0)

2 (5.6)

0.493

 RUNX1-RUNX1T1

6 (13.3)

0 (0.0)

0.026

1 (2.8)

0 (0.0)

1.000

 N.D.

1 (2.2)

1 (2.2)

1.000

0 (0.0)

1 (2.8)

1.000

Risk(cyto)/n (%)

 Good

12 (26.7)

1 (2.2)

0.002

5 (13.9)

1 (2.8)

0.199

 Intermediate

26 (57.8)

29 (64.4)

0.665

21 (58.3)

20 (55.5)

1.000

 Poor

6 (13.3)

14 (31.1)

0.074

10 (27.8)

14 (38.9)

0.454

 Other

1 (2.2)

1 (2.2)

1.000

0 (0.0)

1 (2.8)

1.000

FLT3-ITD/n (%)

  

0.784

  

0.045

 Presence

9 (20.0)

7 (15.6)

 

4 (11.1)

12 (33.3)

 

 Absence

36 (80.0)

38 (84.4)

 

32 (88.9)

24 (66.7)

 

NPM1/n (%)

  

0.175

  

0.064

 Presence

11 (24.4)

18 (40)

 

6 (16.7)

14 (38.9)

 

 Absence

34 (75.6)

27 (60)

 

36 (83.3)

22 (61.1)

 

DNMT3A/n (%)

  

0.157

  

1.000

 Presence

9 (20)

16 (35.6)

 

9 (25)

9 (25)

 

 Absence

36 (80)

29 (64.6)

 

27 (75)

27 (75)

 

RUNX1/n (%)

  

0.714

  

0.710

 Presence

5 (11.1)

3 (6.7)

 

5 (13.9)

3 (8.3)

 

 Absence

40 (89.9)

42 (93.3)

 

31 (86.1)

33 (91.7)

 

MLL-PTD/n (%)

  

1.000

   

 Presence

2 (4.4)

3 (6.7)

 

2 (5.6)

2 (5.6)

 

 Absence

43 (95.6)

42 (93.3)

 

34 (94.4)

34 (94.4)

 

TP53/n (%)

  

0.522

  

1.000

 Mutation

4 (8.9)

7 (15.6)

 

2 (5.6)

2 (5.6)

 

 Wild type

41 (91.1)

38 (84.4)

 

34 (94.4)

34 (94.4)

 

CEBPA/n (%)

  

1.000

  

0.055

 Mutation

1 (2.2)

2 (4.4)

 

7 (19.4)

1 (2.8)

 

 Wild type

44 (97.8)

43 (95.6)

 

29 (80.6)

35 (97.2)

 

IDH1/n (%)

  

1.000

  

0.514

 Mutation

3 (6.7)

4 (8.9)

 

4 (11.1)

7 (19.4)

 

 Wild type

42 (93.3)

41 (91.1)

 

32 (88.9)

29 (80.6)

 

IDH2/n (%)

  

1.000

  

0.260

 Mutation

5 (11.1)

4 (8.9)

 

2 (5.6)

6 (16.7)

 

 Wild type

40 (88.9)

41 (91.1)

 

34 (94.4)

30 (83.3)

 

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-25 profiles in AML patients

We performed Kaplan–Meier analysis and log-rank test to assess the prognostic value of miR-25 profiles in AML patients. The chemotherapy group showed that AML patients with upregulated miR-25 were connected to better EFS (P = 0.019) and OS (P = 0.0086) relative to those with downregulated miR-25 (Fig. 1a, b). However, AML patients who received allo-HSCT did not exhibit any connection among prognosis and miR-25 expression (Fig. 1c, d). These findings revealed that miR-25 may be utilized as a chemotherapy-specific prognostic marker for AML.
Fig. 1
Fig. 1

Kaplan–Meier survival curves of AML patients stratified based on miR-25 expression. a, b In the chemotherapy group, the high miR-25 expressers had significantly prolonged OS and EFS (n = 90) compared with low miR-25 expressers. c, d There were no significant differences in patients undergoing allo-HSCT between high and low miR-25 groups (n = 72)

High level of miR-25 is independently associated with favorable prognosis

To determine whether miR-25 expression could be used as an independent predictor for AML patient survival, we conducted univariate and multivariate Cox analyses. For the chemotherapy group, univariate analysis revealed that the upregulation of miR-25 was connected with longer EFS (HR = 0.598, 95% CI 0.376–0.951, P = 0.030) and OS (HR = 0.556, 95% CI 0.347–0.890, P = 0.015). Furthermore, multivariate cox analysis indicated that miR-25 upregulation was independently connected with longer EFS (HR = 0.561, 95% CI 0.333–0.943, P = 0.029) and OS (HR = 0.502, 95% CI 0.296–0.851, P = 0.011) after adjustment of mutation status for the FLT3-ITD, NPM1, DNMT3A, RUNX1, IDH1, and IDH2 genes and WBC count (Table 2).
Table 2

Univariate and multivariate analyses in patients treated with chemotherapy

Variables

EFS

OS

HR (95% CI)

P-value

HR (95% CI)

P-value

Univariate analyses

 MiR-25 (high vs low)

0.598 (0.376–0.951)

0.030

0.556 (0.347–0.890)

0.015

 WBC (< 20 vs ≥ 20 × 109/L)

0.939 (0.594–1.484)

0.786

0.936 (0.591–1.484)

0.779

 FLT3-ITD (positive vs negative)

1.242 (0.693–2.224)

0.467

1.192 (0.665–2.136)

0.555

 NPM1 (mutated vs wild)

1.168 (0.721–1.893)

0.527

1.044 (0.640–1.704)

0.862

 DNMT3A (mutated vs wild)

1.491 (0.909–2.446)

0.114

1.432 (0.868–2.362)

0.160

 RUNX1 (mutated vs wild)

1.464 (0.700–3.064)

0.312

1.591 (0.759–3.335)

0.219

 ITDH1 (mutated vs wild)

1.043 (0.452–2.405)

0.922

0.908 (0.366–2.254)

0.836

 ITDH2 (mutated vs wild)

0.981 (0.487–1.977)

0.956

0.991 (0.492–1.995)

0.979

Multivariate analyses

 MiR-25 (high vs low)

0.561 (0.333–0.943)

0.029

0.502 (0.296–0.851)

0.011

 WBC (< 20 vs ≥ 20 × 109/L)

0.884 (0.537–1.456)

0.629

0.927 (0.563–1.527)

0.766

 FLT3-ITD (positive vs negative)

1.489 (0.778–2.848)

0.229

1.578 (0.815–3.054)

0.176

 NPM1 (mutated vs wild)

0.877 (0.476–1.615)

0.674

0.760 (0.411–1.405)

0.382

 DNMT3A (mutated vs wild)

1.421 (0.787–2.568)

0.244

1.416 (0.787–2.550)

0.246

 RUNX1 (mutated vs wild)

1.730 (0.768–3.897)

0.186

1.805 (0.805–4.050)

0.152

 ITDH1 (mutated vs wild)

1.141 (0.448–2.904)

0.782

1.074 (0.397–2.906)

0.889

 ITDH2 (mutated vs wild)

1.039 (0.480–2.251)

0.922

1.042 (0.483–2.248)

0.916

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

Univariate analysis of the allo-HSCT group suggested that AML cases harboring FLT3-ITD mutations had shorter EFS (HR = 1.873, 95% CI 1.020–3.437, P = 0.043) and OS (HR = 1.998, 95% CI 1.053–3.788, P = 0.034). Patients with mutations only in the RUNX1 gene exhibited shorter OS (HR = 2.253, 95% CI 1.046–4.849, P = 0.038). Multivariate analysis indicated that FLT3-ITD and RUNX1 remained independent outcome predictors after adjusting for all other prognostic factors (Table 3). However, allo-HSCT patients did not show any significant differences between upregulated and downregulated miR-25 expression.
Table 3

Univariate and multivariate analyses in patients treated with allo-HSCT

Variables

EFS

OS

HR (95% CI)

P-value

HR (95% CI)

P-value

Univariate analyses

 MiR-25 (high vs low)

0.886 (0.553–1.473)

0.641

0.625 (0.364–1.073)

0.088

 WBC (< 20 vs ≥ 20 × 109/L)

1.530 (0.910–2.571)

0.108

0.949 (0.554–1.628)

0.851

 FLT3-ITD (positive vs negative)

1.873 (1.020–3.437)

0.043

1.998 (1.053–3.788)

0.034

 NPM1 (mutated vs wild)

0.913 (0.515–1.619)

0.755

0.879 (0.478–1.617)

0.678

 DNMT3A (mutated vs wild)

1.106 (0.615–1.989)

0.737

1.269 (0.686–2.347)

0.447

 RUNX1 (mutated vs wild)

1.375 (0.650–2.907)

0.404

2.253 (1.046–4.849)

0.038

 ITDH1 (mutated vs wild)

0.985 (0.498–1.949)

0.966

0.810 (0.382–1.718)

0.582

 ITDH2 (mutated vs wild)

0.569 (0.227–1.425)

0.229

0.931 (0.368–2.357)

0.880

Multivariate analyses

 MiR-25 (high vs low)

0.788 (0.421–1.476)

0.457

0.510 (0.266–0.978)

0.043

 WBC (< 20 vs ≥ 20 × 109/L)

1.343 (0.756–2.386)

0.314

0.827 (0.450–1.519)

0.540

 FLT3-ITD (positive vs negative)

2.222 (1.044–4.729)

0.038

2.201 (0.951–5.096)

0.065

 NPM1 (mutated vs wild)

0.586 (0.280–1.227)

0.156

0.560 (0.249–1.259)

0.161

 DNMT3A (mutated vs wild)

1.058 (0.549–2.037)

0.867

1.514 (0.774–2.963)

0.226

 RUNX1 (mutated vs wild)

1.483 (0.620–3.545)

0.376

2.671 (1.114–6.402)

0.028

 ITDH1 (mutated vs wild)

1.265 (0.535–2.944)

0.592

0.781 (0.305–1.999)

0.606

 ITDH2 (mutated vs wild)

0.524 (0.183–1.498)

0.228

0.499 (0.175–1.424)

0.194

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

Allo-HSCT may circumvent poor patient outcomes that are related to downregulated miR-25 expression

To determine whether allo-HSCT therapy could circumvent the severe prognosis that was associated with downregulated miR-25, the whole cohort of 162 cases was split into two groups according to the median miR-25 expression levels. In the downregulated miR-25 group, the AML cases who received allo-HSCT showed significantly longer EFS (HR = 0.515, 95% CI 0.327–0.831, P = 0.0069) and OS (HR = 0.405, 95% CI 0.250–0.639, P = 0.0002) relative to cases who underwent standard chemotherapy alone (Fig. 2a, b). For the upregulated miR-25 group, no obvious differences in EFS (P = 0.969) and OS (P =0.364) were observed among the allo-HSCT and chemotherapy regimens. Thus, the AML patients showing downregulated miR-25 may benefit from treatment with allo-HSCT.
Fig. 2
Fig. 2

Allo-HSCT treatment circumvents the unfavorable outcomes of AML patients showing downregulated miR-25 expression. a, b A total of 162 cases were placed into two groups according to the median miR-25 expression levels. In the downregulated miR-25 group, the Kaplan–Meier survival curves of AML patients classified based on chemotherapy (n = 52) and allo-HSCT (n = 29) treatment. c, d In the upregulated miR-25 group, the Kaplan–Meier survival curves of AML patients classified based on chemotherapy (n = 38) and allo-HSCT (n = 43) treatment

Biological insights into miR-25 profiles in AML

To generate insights into the molecular mechanism of miR-25, we analyzed a gene expression signature that was connected with miR-25 expression among AML cases. An association between the expression of 205 genes and miR-25 was observed. Among these genes, 145 were negatively correlated and 60 were positively correlated with the expression of miR-25 (Fig. 3). MiR-25 expression was inversely correlated with the expression of HOXA and HOXB, as well as the HOX cofactor MEIS1. Notably, these genes are crucial for the leukemogenesis and self-renewal capacities of AML [8, 16, 17]. Furthermore, we discovered that the expression of miR-25 was negatively connected with the levels of the PRDM16, Which involved in AML translocation [18]; CD97, an EGF-TM7 receptor [19]; IRAK1, which activates NF-κB pathways by the interaction with TRAF6 [20]; NFKB2, a pro-inflammatory response gene [21]; MYH9, which predicts unfavorable outcome of AML [22]; HDAC11, a epigenetic regulator. Notably, MYH9 was a in silico predicted target of miR-25.
Fig. 3
Fig. 3

Heat map of miR-25-associated gene-expression signature in patients with AML. The columns represent patients and the rows represent genes. The columns are ordered from left to right according to increasing expression levels of miR-25. The hierarchical cluster analysis was performed to order rows. The expression levels of various genes are represented by nodes of different colors, ranging from the lowest (green) to the highest (red)

Gene Ontology showed that genes involved in cellular metabolic process, system development, immune system process, transcription, hematopoietic or lymphoid organ development, hemopoiesis and myeloid cell differentiation were markedly overrepresented among differentially expressed genes associated with miR-25 expression (Table 4).
Table 4

Gene ontology terms of biological processes in the miR-25 associated expression profile

GO ID

GO terms

Percentage of members of the GO term present in the miR-25 profile

P-value FDR

GO:0031323

Regulation of cellular metabolic process

46.9

0.029

GO:0048522

Regulation of cellular process

40.3

0.029

GO:0048731

System development

37.2

0.039

GO:0010604

Regulation of macromolecule metabolic process

28.5

0.013

GO:0002376

Immune system process

26.5

0.004

GO:0045893

Regulation of transcription

16.8

0.037

GO:0048534

Hematopoietic or lymphoid organ development

14.7

< 0.001

GO:0002520

Immune system development

14.7

< 0.001

GO:0030097

Hemopoiesis

13.7

< 0.001

GO:0001501

Skeletal system development

9.6

0.012

GO:0030099

Myeloid cell differentiation

8.1

0.009

GO Gene Ontology

Discussion

AML has been considered to occur as the result of genetic abnormalities, including chromosomal rearrangements, gene deregulations and mutations [23]. The deregulated expression of microRNAs in AML can influence cell proliferation, survival and hematopoietic differentiation [24]. The association of microRNAs with prognosis in heterogeneous patients with AML is still largely unclear. In this evaluation, the upregulated of miR-25 was determined to be an independently favorable prognosticator of AML cases who were administered chemotherapy. Furthermore, allo-HSCT may overcome the poor prognosis of AML cases with low miR-25 expression.

A correlation between aberrant miRNA expression and AML prognosis has been established [25, 26]. However, most of previous microRNA markers is restricted to AML without cytogenetic abnormalities. In our study, univariate and multivariate analyses demonstrated that miR-25 is an independently biomarker for cases administered chemotherapy. High miR-25 expression can predict favorable outcome. The prognostic role of miR-25 is different with previously established prognostic factors in a heterogeneous population of AML. MiR-25, as an independent outcome predictor, may improve the current clinical risk-based classification of patients with AML.

To further understand the biological insight into the molecular mechanism underlying miR-25, we identified genes significantly correlated with miR-25 expression. We discovered that the expression of miR-25 negatively connected with the levels of PRDM16, HOXAs, HOXBs, MEIS1, CD97, IRAK1, NFKB2 and MYH9. HOXA and HOXB gene clusters are the common characters of AML [27, 28]. Of these genes, HOXB4 is positively involved in the renewal of hematopoietic stem cell [29, 30]. A previous study has shown that HOXA9 contributes to the proliferation, apoptosis, and differentiation processes of leukemia [31]. In addition, HOXA9 has been correlated with poor AML prognosis [32]. Prior evaluations have revealed that IRAK1 may be utilized as a therapeutic target for AML, and TRAF6 may be used to activate pathways such as NFKB, MAPK, and AKT [20, 33]. PRDM16, also known as MEL1, is highly homologous to MDS1/EVI1. High expression of PRDM16 can predict the adverse outcome of AML [18]. Moreover, MYH9 has also been predicted as a direct target of miR-25. High expression of MYH9 can induce resistant to chemotherapy and predict poor clinical outcome in AML [22]. Taken together, the miR-25-associated gene-expression profiling analyses provide insights into the leukemogenic role of genes that are either direct or indirect targets of miR-25. Therefore, the miR-25-associated gene-expression signature analysis give novel insights into the oncogenic role of these genes. These miR-25-related genes could contribute to the chemotherapeutic responses of AML patients.

The FMS-like tyrosine kinase 3 (FLT3) gene is pivotal to hematopoietic stem cell proliferation and differentiation [34]. FLT3 mutations take a great account of most frequent genetic aberrations in AML [35]. FLT3-ITD mutation is one of FLT3 mutations, which can keep the tyrosine kinase persistently active, and result in the abnormal proliferation of leukemic cells. Mutations in the FLT-ITD gene have been associated with higher risk for relapse and poor OS and EFS [36]. Consistent with the conclusion, our data suggested that FLT3-ITD mutation is a poor outcome marker in patients undergoing allo-HSCT. These analysis results indicate that allo-HSCT cannot overcome all adverse prognosis of molecular markers. The findings of this study have revealed that allo-HSCT circumvents the poor chemotherapy outcomes that are related to downregulated miR-25 expression. Thus, low miR-25 expression may be employed as a predictor of adverse prognoses among patients who received chemotherapy, as well as identify patients who require strategies in selecting the best treatment regimen, i.e., chemotherapy and/or allo-HCST.

Conclusion

In conclusion, high expression of miR-25 was identified to independently predict favorable survival in a highly heterogeneous population of patients with AML. Our findings may offer more information for the therapeutic strategies and the prediction of patients with AML, which may improve the survival and reduce the relapse of them. More importantly, allo-HSCT circumvents poor chemotherapeutic outcomes in cases with downregulated miR-25. The expression levels of miR-25 may thus be utilized in determining whether chemotherapy or allo-HSCT is the optimal treatment regimen for a specific AML patient.

Notes

Abbreviations

AML: 

acute myeloid leukemia

TCGA: 

The Cancer Genome Atlas

WHO: 

World Health Organization

Allo-HSCT: 

allogeneic hematopoietic stem cell transplantation

OS: 

overall survival

EFS: 

event-free survival

Declarations

Acknowledgements

Not applicable.

Funding

The research was supported by National Natural Science Foundation of China (81670142, 81772658, 81870163); Jiangsu Provincial Key Research and Development Program (BE2017638, BE2017636); Natural Science Foundation of Jiangsu Province (BK20180104); The Foundation of Jiangsu Province Six Talents Peak (2017-WSN-120); Jiangsu Qing Lan Project for Mingshan Niu; Postgraduate Research & Practice Innovation Program of Jiangsu Province (SJCX18_0704, SJKY19_2110).

Authors’ contributions

MN, YF and NZ designed and performed the computational analyses. TS, HZ, RW, YY, RY, QW and JC contributed to statistical analyses. XL, YL and KX designed and wrote the manuscript. All authors read and approved the final manuscript.

Ethics approval and consent to participate

All data sets were derived from The Cancer Genome Atlas (TCGA) data portal and are publicly accessible from the TCGA website. The patients were enrolled in a single-institution tissue banking protocol approved by the human studies committee at Washington University. Written informed consent was obtained from all patients.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Blood Diseases Institute, Affiliated Hospital of Xuzhou Medical University, Xuzhou Medical University, Xuzhou, Jiangsu, China
(2)
Department of Hematology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
(3)
Institute of Nervous System Diseases, Xuzhou Medical University, Xuzhou, Jiangsu, China
(4)
School of Life Science & Medicine, Dalian University of Technology, Panjin, China

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© The Author(s) 2019

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