Abstract

As high expression level of growth arrest-specific 6 (GAS6) had an adverse effect on prognosis in acute myeloid leukemia (AML) patients, it is interesting to reveal the relationship between GAS6-mRNA level and the survival condition of AML patients undergoing allogeneic hematopoietic stem cell transplantation (HSCT). We screened The Cancer Genome Atlas database and found 71 AML patients with GAS6-mRNA expression and received allo-HSCT treatments. We divided them into two groups based on the median expression of GAS6-mRNA. Patients with GAS6-mRNAhigh (n=36) seemed to have lower bone marrow (BM) blast (P=0.022), lower percentage of type M5 (P=0.034), lower percentage of inv(16)/CBFβ-MYH11 karyotype (P=0.020), and lower rate of good risk classification (P=0.005) than the group GAS6-mRNAlow (n= 35). Higher expression level of GAS6-mRNA also brought higher RUNX1 mutations (P=0.003), MLL-PTD mutations (P=0.042), TP53 mutations (P=0.042), and lower NRAS/KRAS mutations (P=0.042). Univariate analyses showed that GAS6-mRNA was unfavorable for overall survival (OS) (P=0.044), as RUNX1 and WT1 also gave negative influences. Multivariate analyses confirmed that GAS6-mRNA cut down the event-free servival (EFS) and OS of AML patients with HSCT (P=0.029, P=0.025). Our study indicated that higher expression of GAS6-mRNA related with adverse effects in AML patients with HSCT treatment.

Introduction

Characterized by clonal expansion of stem cells or progenitor cells in blood tissues without differentiation, acute myeloid leukemia (AML) is considered to be a highly heterogeneous disease [1]. As next generation sequencing was used to discover the pathogenesis of AML at the level of genes, many biomarkers for the prognosis of AML have been found. Mutations in NPM1, IDH2, and biallelic CEBPA mutations always bring longer EFS and overall survival (OS); while FLT3-ITD positive, DNMT3A, IDH1, TET2, KRAS, KIT, TP53, PTPN11, and MLL-PTD are predictors for poor outcomes [2].

Growth arrest-specific 6 (GAS6) is a gene that encodes the GAS6 protein and plays an important role in cell proliferation, survival, and migration. Since Manfioletti et al. considered GAS6 a new member of vitamin K-dependent proteins and may be involved in cell growth regulation, many studies have been done to uncover its biological function [3]. Binding with Tyro3, Axl and Mer (TAM) receptors, GAS6 gives activation for its downstream pathways like phosphatidylinositol 3-kinase (PI3K), extracellular signal-regulated kinase, and nuclear factor κ-light-chain-enhancer of activated B cells (NF-κβ) [4]. There were quite a little studies confirmed that up-regulation of GAS6 will disturb those pathways and lead to incontrollable growth of body cell and finally lead to cancer [5–8]. The prognostic value of GAS6 has been found in breast cancer, lung cancer, and some other common tumors like glioblastoma and renal cell carcinoma [9]. GAS6 expression was detected in many cell lines of leukemia [10,11]. In recent years, Whitman et al. found that GAS6 expression also produced an adverse effect on the outcome of AML patients. Expressing of GAS6 predicted CR failure, shorter DFS, and OS in patients only received chemotherapy [12]. Allo-hematopoietic stem cell transplantation (HSCT) was served as a helpful method in the recovery of AML and overcame the harmful effect of some high risk molecular biomarkers [13]. However, the prognostic significance of GAS6 expression level in AML patients undergoing allo-HSCT was still unknown. In the present study, we compared AML patients with different levels of GAS6 expression to find out whether GAS6 a poor prognosis factor in AML patients undergoing allo-HSCT.

Materials and methods

Patients

We screened The Cancer Genome Atlas (https://cancergenome.nih.gov/) and 71 diagnosed AML patients were enrolled in the study. Expression levels of GAS6-mRNA and clinical and molecular information of those AML patients were downloaded. We selected patients according to two standards. First, patients who do not have information about their GAS6-mRNA levels were excluded. Second, patients who did not undergo the treatment of allo-HSCT were excluded. Finally, 71 AML patients were included in our study.

Event-free survival (EFS) and OS were considered as two endpoints. EFS is the time from the date of diagnosis to removal from the study due to the absence of complete remission, relapse, or death. OS is the time from the date of diagnosis to death due to any cause. Written informed consent was obtained from all patients, which was approved by the Human Research Ethics Committee of Washington University.

Statistical analysis

We compared the different biological and clinical characters using descriptive statistics. The Mann–Whitney U test was applied to two group comparisons, and chi-square test was used to compare the rate between two groups. Survival analysis about EFS and OS rates were calculated using the Kaplan–Meier method and compared using the log-rank test. Cox proportional hazard model was used to assess the hazard ratios (HRs) associated with the prognosis. A two-sided P-value <0.05 was considered statistically significant for all statistical analyses. All statistical analyses were performed by SPSS Version 20.0 software.

Results

Comparison of clinical and molecular characteristics between different GAS6-mRNA expression levels

Based on the median expression level, we divided 71 AML patients into two groups (GAS6-mRNAhigh, n= 36; GAS-mRNAlow, n= 35). Clinical and molecular characteristics of two groups are summarized in Table 1 with the results of statistic analyses. No significant differences were found in age, gender, WBC count, and peripheral blood blasts proportion between two groups, while GAS6-mRNAlow group seemed to have higher BM blast percentage (P=0.022). GAS6-mRNAlow group was more commonly seen in type M5 when considered FAB classification (P=0.034). Karyotype and risk distribution showed that patients with GAS6-mRNAhigh always have lower proportion of inv(16)/CBFβ-MYH11 karyotype (P=0.020) and the rate of Good Risk classification (P=0.005). When comparing some of the frequent AML mutations, no significant differences were observed in FLT3-ITD, NPM1, CEBPA, DNMT3A, IDH1, IDH2, WT1, TET2, KIT, PTPN11, and PHF6 between two groups, but there were obvious distinctions between those two groups as higher level of GAS6-mRNA brought higher RUNX1 mutations (P=0.003), MLL-PTD mutations (P=0.042), TP53 mutations (P=0.042), and lower NRAS/KRAS mutations (P=0.042). Relapse rate and HSCT types distribution did not show significant differences.

Table 1
Clinical and molecular characteristics of GAS6-mRNAhigh and GAS6-mRNAlow patients
Characteristics GAS6-mRNAhigh (n= 36) GAS6-mRNAlow (n= 35) U/χ2 P-value 
Age/years, median (range) 53.5 (18–69) 48 (22–72) 542.5* 0.314 
Age group/n (%)   1.610§ 0.205 
  <60 years 24 (66.7) 28 (80.0)   
  ≥60 years 12 (33.3) 7 (20.0)   
Gender/n (%)   0.010§ 0.919 
  Male 21 (58.3) 20 (57.1)   
  Female 15 (41.7) 15 (42.9)   
WBC count/×109/l, median (range) 23.35 (0.6–223.8) 30.9 (2.3–118.8) 544.0* 0.323 
BM blasts/%, median (range) 62 (30–100) 77 (34–99) 431.0* 0.022 
PB blasts/%, median (range) 60 (0–96) 45 (4–94) 554.5* 0.499 
FAB subtypes/n (%)     
  M0 5 (13.9) 4 (11.8) 0.070§ 0.791 
  M1 13 (36.1) 10 (29.4) 0.356§ 0.551 
  M2 10 (27.8) 8 (23.5) 0.165§ 0.684 
  M3 0 (0.0) 1 (2.9) 1.074§ 0.300 
  M4 6 (16.7) 7 (20.6) 0.178§ 0.673 
  M5 0 (0.0) 4 (11.8) 4.492§ 0.034 
  M6 1 (2.8) 0 (0.0) 0.958§ 0.328 
  M7 1 (2.8) 0 (0.0) 0.958§ 0.328 
Karyotype/n (%)     
  Normal 18 (51.4) 14 (40.0) 0.921§ 0.337 
  Complex 7 (20.0) 4 (11.4) 0.971§ 0.324 
  8 Trisomy 5 (14.3) 1 (2.9) 2.917§ 0.088 
  inv(16)/CBFβ-MYH11 0 (0.0) 5 (14.3) 5.385§ 0.020 
  11q23/MLL 1 (2.9) 2 (5.7) 0.348§ 0.555 
  -7/7q- 1 (2.9) 2 (5.7) 0.348§ 0.555 
  t(15;17)/PML-RARA 0 (0.0) 1 (2.9) 1.014§ 0.314 
  t(9;22)/BCR-ABL1 1 (2.9) 1 (2.9) 0.000§ 1.000 
  t(8;21)/RUNX1-RUNX1T1 0 (0.0) 1 (2.9) 1.014§ 0.314 
  Others 2 (5.7) 4 (11.4) 0.729§ 0.393 
Risk/n (%)     
  Good 0 (0.0) 7 (20.0) 7.778§ 0.005 
  Intermediate 23 (65.7) 17 (48.6) 2.100§ 0.147 
  Poor 12 (34.3) 11 (31.4) 0.065§ 0.799 
FLT3-ITD   0.045§ 0.832 
  Presence 9 (25.0) 8 (22.9)   
  Absence 27 (75.0) 27 (77.1)   
NPM1   2.911§ 0.088 
  Mutation 6 (16.7) 12 (34.3)   
  Wild type 30 (83.3) 23 (65.7)   
CEBPA     
  Single mutation 2 (5.6) 3 (8.6) 0.247§ 0.620 
  Double mutation 2 (5.6) 1 (2.9) 0.319§ 0.572 
  Wild type 32 (88.9) 31 (88.6) 0.002§ 0.966 
DNMT3A   0.119§ 0.730 
  Mutation 8 (22.2) 9 (25.7)   
  Wild type 28 (77.8) 26 (74.3)   
IDH1   0.002§ 0.962 
  Mutation 5 (13.9) 5 (14.3)   
  Wild type 31 (86.1) 30 (85.7)   
IDH2   0.002§ 0.966 
  Mutation 4 (11.1) 4 (11.4)   
  Wild type 32 (88.9) 31 (88.6)   
WT1   0.002§ 0.966 
  Mutation 4 (11.1) 4 (11.4)   
  Wild type 32 (88.9) 31 (88.6)   
RUNX1   8.765§ 0.003 
  Mutation 8 (22.2) 0 (0.0)   
  Wild type 28 (77.8) 35 (100.0)   
MLL-PTD   4.121§ 0.042 
  Presence 4 (11.1) 0 (0.0)   
  Absence 32 (88.9) 35 (100)   
NRAS/KRAS   4.121§ 0.042 
  Mutation 1 (2.8) 6 (17.1)   
  Wild type 35 (97.2) 29 (82.9)   
TET2   1.120§ 0.290 
  Mutation 1 (2.8) 3 (8.6)   
  Wild type 35 (97.2) 32 (91.4)   
TP53   4.121§ 0.042 
  Mutation 4 (11.1) 0 (0.0)   
  Wild type 32 (88.9) 35 (100.0)   
KIT   3.222§ 0.073 
  Mutation 0 (0.0) 3 (8.6)   
  Wild type 36 (100) 32 (91.4)   
PTPN11   0.247§ 0.620 
  Mutation 2 (5.6) 3 (8.6)   
  Wild type 34 (94.4) 32 (91.4)   
PHF6   1.001§ 0.317 
  Mutation 3 (8.3) 1 (2.9)   
  Wild type 33 (91.7) 34 (97.1)   
Relapse   0.065§ 0.799 
  Yes 24 (68.6) 23 (65.7)   
  No 11 (31.4) 12 (34.3)   
HSCT     
  Haplo 1 (2.8) 1 (2.9) 0.000§ 0.984 
  Sib allo 16 (44.4) 14 (40.0) 0.144§ 0.705 
  MUD 19 (52.8) 20 (57.1) 0.137§ 0.712 
Characteristics GAS6-mRNAhigh (n= 36) GAS6-mRNAlow (n= 35) U/χ2 P-value 
Age/years, median (range) 53.5 (18–69) 48 (22–72) 542.5* 0.314 
Age group/n (%)   1.610§ 0.205 
  <60 years 24 (66.7) 28 (80.0)   
  ≥60 years 12 (33.3) 7 (20.0)   
Gender/n (%)   0.010§ 0.919 
  Male 21 (58.3) 20 (57.1)   
  Female 15 (41.7) 15 (42.9)   
WBC count/×109/l, median (range) 23.35 (0.6–223.8) 30.9 (2.3–118.8) 544.0* 0.323 
BM blasts/%, median (range) 62 (30–100) 77 (34–99) 431.0* 0.022 
PB blasts/%, median (range) 60 (0–96) 45 (4–94) 554.5* 0.499 
FAB subtypes/n (%)     
  M0 5 (13.9) 4 (11.8) 0.070§ 0.791 
  M1 13 (36.1) 10 (29.4) 0.356§ 0.551 
  M2 10 (27.8) 8 (23.5) 0.165§ 0.684 
  M3 0 (0.0) 1 (2.9) 1.074§ 0.300 
  M4 6 (16.7) 7 (20.6) 0.178§ 0.673 
  M5 0 (0.0) 4 (11.8) 4.492§ 0.034 
  M6 1 (2.8) 0 (0.0) 0.958§ 0.328 
  M7 1 (2.8) 0 (0.0) 0.958§ 0.328 
Karyotype/n (%)     
  Normal 18 (51.4) 14 (40.0) 0.921§ 0.337 
  Complex 7 (20.0) 4 (11.4) 0.971§ 0.324 
  8 Trisomy 5 (14.3) 1 (2.9) 2.917§ 0.088 
  inv(16)/CBFβ-MYH11 0 (0.0) 5 (14.3) 5.385§ 0.020 
  11q23/MLL 1 (2.9) 2 (5.7) 0.348§ 0.555 
  -7/7q- 1 (2.9) 2 (5.7) 0.348§ 0.555 
  t(15;17)/PML-RARA 0 (0.0) 1 (2.9) 1.014§ 0.314 
  t(9;22)/BCR-ABL1 1 (2.9) 1 (2.9) 0.000§ 1.000 
  t(8;21)/RUNX1-RUNX1T1 0 (0.0) 1 (2.9) 1.014§ 0.314 
  Others 2 (5.7) 4 (11.4) 0.729§ 0.393 
Risk/n (%)     
  Good 0 (0.0) 7 (20.0) 7.778§ 0.005 
  Intermediate 23 (65.7) 17 (48.6) 2.100§ 0.147 
  Poor 12 (34.3) 11 (31.4) 0.065§ 0.799 
FLT3-ITD   0.045§ 0.832 
  Presence 9 (25.0) 8 (22.9)   
  Absence 27 (75.0) 27 (77.1)   
NPM1   2.911§ 0.088 
  Mutation 6 (16.7) 12 (34.3)   
  Wild type 30 (83.3) 23 (65.7)   
CEBPA     
  Single mutation 2 (5.6) 3 (8.6) 0.247§ 0.620 
  Double mutation 2 (5.6) 1 (2.9) 0.319§ 0.572 
  Wild type 32 (88.9) 31 (88.6) 0.002§ 0.966 
DNMT3A   0.119§ 0.730 
  Mutation 8 (22.2) 9 (25.7)   
  Wild type 28 (77.8) 26 (74.3)   
IDH1   0.002§ 0.962 
  Mutation 5 (13.9) 5 (14.3)   
  Wild type 31 (86.1) 30 (85.7)   
IDH2   0.002§ 0.966 
  Mutation 4 (11.1) 4 (11.4)   
  Wild type 32 (88.9) 31 (88.6)   
WT1   0.002§ 0.966 
  Mutation 4 (11.1) 4 (11.4)   
  Wild type 32 (88.9) 31 (88.6)   
RUNX1   8.765§ 0.003 
  Mutation 8 (22.2) 0 (0.0)   
  Wild type 28 (77.8) 35 (100.0)   
MLL-PTD   4.121§ 0.042 
  Presence 4 (11.1) 0 (0.0)   
  Absence 32 (88.9) 35 (100)   
NRAS/KRAS   4.121§ 0.042 
  Mutation 1 (2.8) 6 (17.1)   
  Wild type 35 (97.2) 29 (82.9)   
TET2   1.120§ 0.290 
  Mutation 1 (2.8) 3 (8.6)   
  Wild type 35 (97.2) 32 (91.4)   
TP53   4.121§ 0.042 
  Mutation 4 (11.1) 0 (0.0)   
  Wild type 32 (88.9) 35 (100.0)   
KIT   3.222§ 0.073 
  Mutation 0 (0.0) 3 (8.6)   
  Wild type 36 (100) 32 (91.4)   
PTPN11   0.247§ 0.620 
  Mutation 2 (5.6) 3 (8.6)   
  Wild type 34 (94.4) 32 (91.4)   
PHF6   1.001§ 0.317 
  Mutation 3 (8.3) 1 (2.9)   
  Wild type 33 (91.7) 34 (97.1)   
Relapse   0.065§ 0.799 
  Yes 24 (68.6) 23 (65.7)   
  No 11 (31.4) 12 (34.3)   
HSCT     
  Haplo 1 (2.8) 1 (2.9) 0.000§ 0.984 
  Sib allo 16 (44.4) 14 (40.0) 0.144§ 0.705 
  MUD 19 (52.8) 20 (57.1) 0.137§ 0.712 

Allo, allogeneic; BM, bone marrow; FAB, French American British; Haplo, haploidentical; HSCT, hematopoietic stem cell transplantation; PB, peripheral blood; MUD, matched unrelated donor; WBC, white blood cell.

*Mann–Whitney U test.

§Chi-square test.

The group of patients with GAS6-mRNAhigh has shorter EFS and OS than patients in the GAS6-mRNAlow group through underwent allo-HSCT treatment (P=0.050 for EFS, P=0.041 for OS, Figure 1A,B).

The influence of GAS6-mRNA expression on EFS and OS

Figure 1
The influence of GAS6-mRNA expression on EFS and OS

(A,B) The prognostic difference between GAS6-mRNAhigh and GAS6-mRNAlow group. The group of patients with GAS6-mRNAhigh has shorter EFS and OS than patients in the GAS6-mRNAlow group through underwent allo-HSCT treatment.

Figure 1
The influence of GAS6-mRNA expression on EFS and OS

(A,B) The prognostic difference between GAS6-mRNAhigh and GAS6-mRNAlow group. The group of patients with GAS6-mRNAhigh has shorter EFS and OS than patients in the GAS6-mRNAlow group through underwent allo-HSCT treatment.

Univatiate and multivariate analyses for prognostic factors

We assessed the prognostic factors of clinical and molecular characteristics by choosing expression level of GAS6-mRNA (high vs low), age (<60 vs ≥60 years), WBC count (<30 × 109/l vs ≥30 × 109/l), risk classification (poor vs non-poor), and genes with more than five mutation cases (FLT3-ITD; positive vs negative; NPM1, DNMT3A, IDH2, IDH1, RUNX1, CEBPA, WT1, PTPN11, and NRAS/KRAS; mutated vs wild) to do survival analysis. Results were shown in Table 2.

Table 2
Univariate analysis for EFS and OS
Variables EFS OS 
 OR (95% CI) P-value OR (95% CI) P-value 
GAS6-mRNA (high vs low) 1.727 (0.993–3.002) 0.053 1.764 (1.016–3.063) 0.044 
Age (≥60 vs <60 years) 0.995 (0.545–1.816) 0.987 1.406 (0.769–2.571) 0.268 
WBC (≥30 vs <30 × 109/l) 1.342 (0.776–2.319) 0.293 0.986 (0.571–1.702) 0.959 
Risk (poor vs non-poor) 1.081 (0.602–1.939) 0.795 1.290 (0.719–2.313) 0.393 
FLT3-ITD (positive vs negative) 1.798 (0.951–3.398) 0.071 1.666 (0.884–3.139) 0.114 
NPM1 (mutated vs wild) 0.799 (0.419–1.523) 0.495 0.805 (0.422–1.536) 0.510 
DNMT3A (mutated vs wild) 1.120 (0.596–2.105) 0.726 1.259 (0.668–2.374) 0.477 
IDH2 (mutated vs wild) 0.678 (0.269–1.172) 0.411 0.995 (0.392–2.525) 0.992 
IDH1 (mutated vs wild) 0.780 (0.351–1.736) 0.543 0.756 (0.340–1.678) 0.491 
RUNX1 (mutated vs wild) 1.648 (0.771–3.519) 0.197 2.437 (1.127–5.270) 0.024 
CEBPA (mutated vs wild) 0.822 (0.326–2.075) 0.679 0.695 (0.276–1.749) 0.439 
WT1 (mutated vs wild) 2.298 (1.021–5.173) 0.045 1.587 (0.709–3.554) 0.261 
PTPN11 (mutated vs wild) 0.695 (0.275–1.756) 0.442 0.496 (0.195–1.258) 0.140 
NRAS/KRAS (mutated vs wild) 0.878 (0.347–2.219) 0.783 1.412 (0.560–3.559) 0.465 
Variables EFS OS 
 OR (95% CI) P-value OR (95% CI) P-value 
GAS6-mRNA (high vs low) 1.727 (0.993–3.002) 0.053 1.764 (1.016–3.063) 0.044 
Age (≥60 vs <60 years) 0.995 (0.545–1.816) 0.987 1.406 (0.769–2.571) 0.268 
WBC (≥30 vs <30 × 109/l) 1.342 (0.776–2.319) 0.293 0.986 (0.571–1.702) 0.959 
Risk (poor vs non-poor) 1.081 (0.602–1.939) 0.795 1.290 (0.719–2.313) 0.393 
FLT3-ITD (positive vs negative) 1.798 (0.951–3.398) 0.071 1.666 (0.884–3.139) 0.114 
NPM1 (mutated vs wild) 0.799 (0.419–1.523) 0.495 0.805 (0.422–1.536) 0.510 
DNMT3A (mutated vs wild) 1.120 (0.596–2.105) 0.726 1.259 (0.668–2.374) 0.477 
IDH2 (mutated vs wild) 0.678 (0.269–1.172) 0.411 0.995 (0.392–2.525) 0.992 
IDH1 (mutated vs wild) 0.780 (0.351–1.736) 0.543 0.756 (0.340–1.678) 0.491 
RUNX1 (mutated vs wild) 1.648 (0.771–3.519) 0.197 2.437 (1.127–5.270) 0.024 
CEBPA (mutated vs wild) 0.822 (0.326–2.075) 0.679 0.695 (0.276–1.749) 0.439 
WT1 (mutated vs wild) 2.298 (1.021–5.173) 0.045 1.587 (0.709–3.554) 0.261 
PTPN11 (mutated vs wild) 0.695 (0.275–1.756) 0.442 0.496 (0.195–1.258) 0.140 
NRAS/KRAS (mutated vs wild) 0.878 (0.347–2.219) 0.783 1.412 (0.560–3.559) 0.465 

Univariate analyses suggested that high expression of GAS6-mRNA was unfavorable for OS (P=0.044). Referring to common genes which always present in AML patients, RUNX1 (P=0.024 for OS) and WT1 (P=0.045 for EFS) also gave negative influences.

Then we selected above-mentioned factors that had statistical significance in univariate analyses and genes confirmed to be associated with prognosis to do the multivariate COX regression analyses (Table 3). The results indicated that high expression of GAS6-mRNA was an independent factor for poor EFS and OS (P=0.029, P=0.025) as FLT3-ITD positive (P=0.029, P=0.030).Mutations in WT1 contributed to shorter EFS (P=0.014), PTPN11 mutations led to shorter OS (P=0.007) while NPM1 mutations made longer OS (P=0.019). Other factors had no association with EFS and OS.

Table 3
Multivariate analysis for EFS and OS
Variables EFS OS 
 OR (95% CI) P-value OR (95% CI) P-value 
GAS6-mRNA (high vs low) 1.890 (1.066–3.353) 0.029 1.934 (1.086–3.441) 0.025 
FLT3-ITD (positive vs negative) 2.382 (1.095–5.181) 0.029 2.366 (1.089–5.140) 0.030 
NPM1 (mutated vs wild) 0.479 (0.196–1.167) 0.105 0.320 (0.124–0.827) 0.019 
DNMT3A (mutated vs wild) 1.276 (0.652–2.499) 0.476 1.379 (0.694–2.740) 0.359 
IDH2 (mutated vs wild) 0.615 (0.235–1.608) 0.321 0.996 (0.380–2.607) 0.993 
IDH1 (mutated vs wild) 1.025 (0.402–2.611) 0.959 1.170 (0.457–2.996) 0.744 
CEBPA (mutated vs wild) 0.606 (0.224–1.636) 0.323 0.622 (0.239–1.618) 0.330 
WT1 (mutated vs wild) 3.107 (1.258–7.675) 0.014 1.959 (0.811–4.733) 0.135 
PTPN11 (mutated vs wild) 2.168 (0.695–6.764) 0.183 5.053 (1.546–16.513) 0.007 
NRAS/KRAS (mutated vs wild) 1.311 (0.492–3.489) 0.588 0.953 (0.358–2.541) 0.924 
Variables EFS OS 
 OR (95% CI) P-value OR (95% CI) P-value 
GAS6-mRNA (high vs low) 1.890 (1.066–3.353) 0.029 1.934 (1.086–3.441) 0.025 
FLT3-ITD (positive vs negative) 2.382 (1.095–5.181) 0.029 2.366 (1.089–5.140) 0.030 
NPM1 (mutated vs wild) 0.479 (0.196–1.167) 0.105 0.320 (0.124–0.827) 0.019 
DNMT3A (mutated vs wild) 1.276 (0.652–2.499) 0.476 1.379 (0.694–2.740) 0.359 
IDH2 (mutated vs wild) 0.615 (0.235–1.608) 0.321 0.996 (0.380–2.607) 0.993 
IDH1 (mutated vs wild) 1.025 (0.402–2.611) 0.959 1.170 (0.457–2.996) 0.744 
CEBPA (mutated vs wild) 0.606 (0.224–1.636) 0.323 0.622 (0.239–1.618) 0.330 
WT1 (mutated vs wild) 3.107 (1.258–7.675) 0.014 1.959 (0.811–4.733) 0.135 
PTPN11 (mutated vs wild) 2.168 (0.695–6.764) 0.183 5.053 (1.546–16.513) 0.007 
NRAS/KRAS (mutated vs wild) 1.311 (0.492–3.489) 0.588 0.953 (0.358–2.541) 0.924 

Discussion

Our study showed that high expression level of GAS6-mRNA has a negative effect on EFS and OS in AML patients underwent allo-HSCT treatments. Multivariate analyses also suggested that GAS6-mRNA expression level of a valuable biomarker relates to prognosis. This conclusion was in accordance with Whitman et al. whose study found that GAS6 expression caused an adverse effect on the outcome of AML patients [12]. For further thought, it indicated that allo-HSCT cannot overcome the harmful effect of GAS6-mRNA expression as well.

Mutations in NPM1 is a favorable risk factor, while FLT3-ITD positive and DNMT3A mutations are predictors for poor outcomes in AML patients [2]. In our study, univariate analyses showed that NPM1, FLT3-ITD, and DNMT3A mutations had nothing to do with EFS and OS of those patients. Multivariate analyses reached the conclusion that only the expression level of GAS6-mRNA and FLT3-ITD positive made a difference in both EFS and OS. With the ideas above, it would be reliable for us to speculate that allo-HSCT can only neutralize part of the bad effects of those traditional molecular biomarkers, but the adverse prognostic impact of GAS6-mRNA expression level still could not be reversed. Thus, the expression level of GAS6-mRNA could be a better prognostic factor for AML patients undergoing allo-HSCT compared with traditional prognostic factors.

Whitman et al. did a GAS6-associated gene expression signature and found that the overexpression of genes that relevant to cell cycle and activating of IL-8 signaling pathway were most likely to be the decisive reasons that GAS6-mRNA could have its influence on the AML patients [12]. Recent studies have found that GAS6/TAM interaction plays an important part not only in tumor cells for its biological functions, but also have a marked impact on tumor microenvironment and cancer metastasis [14]. GAS6 could promote cellular survival and down-regulate apoptotic factors [15,16], induce cell proliferation [17–19], and enhance the migration of cancer cells [20–22]. GAS6 even exerts an autocrine activity and associates with self-sustaining [23,24]. GAS6/TAM also changes the biological behavior of immune cells and vascular smooth muscle cells [25,26].

The biological role of GAS6 suggested a possibility for targetted treatments. Several studies considered that specific therapy targets for Axl R428 and non-specific therapy targets for Mer shRNA might be of use in AML patients [27,28]. These would cast new light on the treatments for AML patients with GAS6-mRNA expression.

Conclusion

In conclusion, our study indicated that high expression of GAS6-mRNA correlates with shorter EFS and OS in AML patients with allo-HSCT treatment and it could serve as a biomarker for poor prognosis. There were several limitations in our study. The limitation of case number reduced the accuracy of our statistic process. Our study is a retrospective study, whose effectiveness is not better than a prospective study. Further studies with a larger cases number shall be done to validate our findings.

Ethics committee approval and patient consent

Written informed consent was obtained from all patients, which was approved by the Human Research Ethics Committee of Washington University.

Competing Interests

The authors declare that there are no competing interests associated with the manuscript.

Funding

This work was supported by grants from the National Natural Science Foundation of China [grant number 81500118 and 61501519], the China Postdoctoral Science Foundation funded project [grant number 2016M600443], and PLAGH project of Medical Big Data [grant number 2016MBD-025].

Author Contribution

X.K. and L.F. designed the study. X.Y. wrote the manuscript. X.Y., J.S., X.Z., G.Z., J.Z., S.Y., and J.W. performed statistical analyses and analyzed the data. X.K. and L.F. coordinated the study over the entire experimental period. All authors contributed toward data analysis, drafting, and revising the paper and agreed to be accountable for all aspects of the work.

Abbreviations

     
  • AML

    acute myeloid leukemia

  •  
  • BM

    bone marrow

  •  
  • EFS

    event-free servival

  •  
  • GAS6

    growth arrest-specific 6

  •  
  • HSCT

    hematopoietic stem cell transplantation

  •  
  • OS

    overall survival

References

References
1.
Dohner
H.
,
Weisdorf
D.J.
and
Bloomfield
C.D.
(
2015
)
Acute myeloid leukemia
.
N. Engl. J. Med.
373
,
1136
1152
[PubMed]
2.
Yohe
S.
(
2015
)
Molecular genetic markers in acute myeloid leukemia
.
J. Clin. Med.
4
,
460
478
[PubMed]
3.
Manfioletti
G.
,
Brancolini
C.
,
Avanzi
G.
and
Schneider
C.
(
1993
)
The protein encoded by a growth arrest-specific gene (gas6) is a new member of the vitamin K-dependent proteins related to protein S, a negative coregulator in the blood coagulation cascade
.
Mol. Cell. Biol.
13
,
4976
4985
4.
Wu
G.
,
Ma
Z.
,
Hu
W.
,
Wang
D.
,
Gong
B.
,
Fan
C.
et al.
(
2017
)
Molecular insights of Gas6/TAM in cancer development and therapy
.
Cell Death Dis.
8
,
e2700
[PubMed]
5.
Ammoun
S.
,
Provenzano
L.
,
Zhou
L.
,
Barczyk
M.
,
Evans
K.
,
Hilton
D.A.
et al.
(
2014
)
Axl/Gas6/NFkappaB signalling in schwannoma pathological proliferation, adhesion and survival
.
Oncogene
33
,
336
346
[PubMed]
6.
Song
X.
,
Wang
H.
,
Logsdon
C.D.
,
Rashid
A.
,
Fleming
J.B.
,
Abbruzzese
J.L.
et al.
(
2011
)
Overexpression of receptor tyrosine kinase Axl promotes tumor cell invasion and survival in pancreatic ductal adenocarcinoma
.
Cancer
117
,
734
743
7.
Demarest
S.J.
,
Gardner
J.
,
Vendel
M.C.
,
Ailor
E.
,
Szak
S.
,
Huang
F.
et al.
(
2013
)
Evaluation of Tyro3 expression, Gas6-mediated Akt phosphorylation, and the impact of anti-Tyro3 antibodies in melanoma cell lines
.
Biochemistry
52
,
3102
3118
8.
Hutterer
M.
,
Knyazev
P.
,
Abate
A.
,
Reschke
M.
,
Maier
H.
,
Stefanova
N.
et al.
(
2008
)
Axl and growth arrest-specific gene 6 are frequently overexpressed in human gliomas and predict poor prognosis in patients with glioblastoma multiforme
.
Clin. Cancer Res.
14
,
130
138
[PubMed]
9.
Zhang
S.
,
Xu
X.S.
,
Yang
J.X.
,
Guo
J.H.
,
Chao
T.F.
and
Tong
Y.
(
2018
)
The prognostic role of Gas6/Axl axis in solid malignancies: a meta-analysis and literature review
.
Onco. Targets Ther.
11
,
509
519
[PubMed]
10.
Dirks
W.
,
Rome
D.
,
Ringel
F.
,
Jager
K.
,
MacLeod
R.A.
and
Drexler
H.G.
(
1999
)
Expression of the growth arrest-specific gene 6 (GAS6) in leukemia and lymphoma cell lines
.
Leuk. Res.
23
,
643
651
[PubMed]
11.
Ben-Batalla
I.
,
Schultze
A.
,
Wroblewski
M.
,
Erdmann
R.
,
Heuser
M.
,
Waizenegger
J.S.
et al.
(
2013
)
Axl, a prognostic and therapeutic target in acute myeloid leukemia mediates paracrine crosstalk of leukemia cells with bone marrow stroma
.
Blood
122
,
2443
2452
[PubMed]
12.
Whitman
S.P.
,
Kohlschmidt
J.
,
Maharry
K.
,
Volinia
S.
,
Mrozek
K.
,
Nicolet
D.
et al.
(
2014
)
GAS6 expression identifies high-risk adult AML patients: potential implications for therapy
.
Leukemia
28
,
1252
1258
[PubMed]
13.
Zhang
J.
,
Shi
J.
,
Zhang
G.
,
Zhang
X.
,
Yang
X.
,
Yang
S.
et al.
(
2018
)
BAALC and ERG expression levels at diagnosis have no prognosis impact on acute myeloid leukemia patients undergoing allogeneic hematopoietic stem cell transplantation
.
Ann. Hematol.
14.
Wu
G.
,
Ma
Z.
,
Cheng
Y.
,
Hu
W.
,
Deng
C.
,
Jiang
S.
et al.
(
2018
)
Targeting Gas6/TAM in cancer cells and tumor microenvironment
.
Mol. Cancer
17
,
20
[PubMed]
15.
Braunger
J.
,
Schleithoff
L.
,
Schulz
A.S.
,
Kessler
H.
,
Lammers
R.
,
Ullrich
A.
et al.
(
1997
)
Intracellular signaling of the Ufo/Axl receptor tyrosine kinase is mediated mainly by a multi-substrate docking-site
.
Oncogene
14
,
2619
2631
[PubMed]
16.
Goruppi
S.
,
Ruaro
E.
,
Varnum
B.
and
Schneider
C.
(
1999
)
Gas6-mediated survival in NIH3T3 cells activates stress signalling cascade and is independent of Ras
.
Oncogene
18
,
4224
4236
[PubMed]
17.
Han
J.
,
Tian
R.
,
Yong
B.
,
Luo
C.
,
Tan
P.
,
Shen
J.
et al.
(
2013
)
Gas6/Axl mediates tumor cell apoptosis, migration and invasion and predicts the clinical outcome of osteosarcoma patients
.
Biochem. Biophys. Res. Commun.
435
,
493
500
[PubMed]
18.
Sainaghi
P.P.
,
Castello
L.
,
Bergamasco
L.
,
Galletti
M.
,
Bellosta
P.
and
Avanzi
G.C.
(
2005
)
Gas6 induces proliferation in prostate carcinoma cell lines expressing the Axl receptor
.
J. Cell. Physiol.
204
,
36
44
[PubMed]
19.
Jin
Y.
,
Nie
D.
,
Li
J.
,
Du
X.
,
Lu
Y.
,
Li
Y.
et al.
(
2017
)
Gas6/AXL signaling regulates self-renewal of chronic myelogenous leukemia stem cells by stabilizing beta-catenin
.
Clin. Cancer Res.
23
,
2842
2855
[PubMed]
20.
Lee
Y.
,
Lee
M.
and
Kim
S.
(
2013
)
Gas6 induces cancer cell migration and epithelial-mesenchymal transition through upregulation of MAPK and Slug
.
Biochem. Biophys. Res. Commun.
434
,
8
14
[PubMed]
21.
Lee
H.J.
,
Jeng
Y.M.
,
Chen
Y.L.
,
Chung
L.
and
Yuan
R.H.
(
2014
)
Gas6/Axl pathway promotes tumor invasion through the transcriptional activation of Slug in hepatocellular carcinoma
.
Carcinogenesis
35
,
769
775
[PubMed]
22.
Kanzaki
R.
,
Naito
H.
,
Kise
K.
,
Takara
K.
,
Eino
D.
,
Minami
M.
et al.
(
2017
)
Gas6 derived from cancer-associated fibroblasts promotes migration of Axl-expressing lung cancer cells during chemotherapy
.
Sci. Rep.
7
,
10613
[PubMed]
23.
El
S.H.
,
Pissaloux
D.
,
Alberti
L.
,
Tabone-Eglinger
S.
,
Ranchere
D.
,
Decouvelaere
A.V.
et al.
(
2013
)
Autocrine role for Gas6 with Tyro3 and Axl in leiomyosarcomas
.
Target Oncol.
8
,
261
269
[PubMed]
24.
Baumann
C.
,
Ullrich
A.
and
Torka
R.
(
2017
)
GAS6-expressing and self-sustaining cancer cells in 3D spheroids activate the PDK-RSK-mTOR pathway for survival and drug resistance
.
Mol. Oncol.
11
,
1430
1447
[PubMed]
25.
Loges
S.
,
Schmidt
T.
,
Tjwa
M.
,
van Geyte
K.
,
Lievens
D.
,
Lutgens
E.
et al.
(
2010
)
Malignant cells fuel tumor growth by educating infiltrating leukocytes to produce the mitogen Gas6
.
Blood
115
,
2264
2273
[PubMed]
26.
Park
I.K.
,
Trotta
R.
,
Yu
J.
and
Caligiuri
M.A.
(
2013
)
Axl/Gas6 pathway positively regulates FLT3 activation in human natural killer cell development
.
Eur. J. Immunol.
43
,
2750
2755
[PubMed]
27.
Janning
M.
,
Ben-Batalla
I.
and
Loges
S.
(
2015
)
Axl inhibition: a potential road to a novel acute myeloid leukemia therapy
.
Expert Rev. Hematol.
8
,
135
138
[PubMed]
28.
Lee-Sherick
A.B.
,
Eisenman
K.M.
,
Sather
S.
,
McGranahan
A.
,
Armistead
P.M.
,
McGary
C.S.
et al.
(
2013
)
Aberrant Mer receptor tyrosine kinase expression contributes to leukemogenesis in acute myeloid leukemia
.
Oncogene
32
,
5359
5368
[PubMed]
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