J Cancer 2020; 11(15):4581-4588. doi:10.7150/jca.44766

Research Paper

Prognostic significance of Spinster homolog gene family in acute myeloid leukemia

Wenhui Huang1,2,3#, Tingting Qian1,2,3#, Zhiheng Cheng4, Tiansheng Zeng1,2,3, Chaozeng Si5, Chaojun Liu6, Cong Deng7, Xu Ye1, Yan Liu8 Corresponding address, Longzhen Cui8,9 Corresponding address, Lin Fu1,2,3,8,9 Corresponding address

1. Department of Hematology, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510260, China
2. Translational Medicine Center, State Key Laboratory of Respiratory Disease, The Second Affiliated Hospital, Guangzhou Medical University, Guangzhou 510260, China
3. Guangdong Provincial Education Department Key Laboratory of Nano-Immunoregulation Tumor Microenvironment, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510260, China
4. Department of Pathology and Medical Biology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
5. Information Center, China-Japan Friendship Hospital, Beijing, 100029, China.
6. Yinfeng Gene Technology Co., Ltd.; No.1109, Gangxing 3 Rd,New and High-tech Zone, Jinan City, Shandong Province, 250102, China
7. Department of Clinical laboratory, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510260, China
8. Translational Medicine Center, Huaihe Hospital of Henan University, Kaifeng, 475000, China.
9. Department of Hematology, Huaihe Hospital of Henan University, Kaifeng, 475000, China
#These authors contributed equally to this work.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions.
Citation:
Huang W, Qian T, Cheng Z, Zeng T, Si C, Liu C, Deng C, Ye X, Liu Y, Cui L, Fu L. Prognostic significance of Spinster homolog gene family in acute myeloid leukemia. J Cancer 2020; 11(15):4581-4588. doi:10.7150/jca.44766. Available from http://www.jcancer.org/v11p4581.htm

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Abstract

Acute myeloid leukemia (AML) is a clonal and heterogeneous disease characterized by proliferation of immature myeloid cells, with impaired differentiation and maturation. Spinster homolog (SPNS) is a widely distributed transmembrane transporter, which assists sphingolipids in playing their roles through the cell membrane. However, the expression and clinical implication of the SPNS family has not been investigated in AML. From the Cancer Genome Atlas database, a total of 155 AML patients with complete clinical characteristics and SPNS1-3 expression data were contained in our study. In patients who received chemotherapy only, high expressions of SPNS2 and SPNS3 had adverse effects on event-free survival (EFS) and overall survival (OS) (all P<0.05). However, in the allogeneic hematopoietic stem cell transplantation (allo-HSCT) group, we only found a significant difference in OS between the high and low SPNS3 expression groups (P=0.001), while other SPNS members showed no effect on survival. Multivariate analysis indicated that high SPNS2 expression was an independent risk factor for both EFS and OS in chemotherapy patients. The results confirmed that high expression of SPNS2 and SPNS3 were poor prognostic factors, and the effect of SPNS2 can be neutralized by allo-HSCT.

Keywords: Acute myeloid leukemia, Prognosis, SPNS1, SPNS2, SPNS3

Introduction

Acute myeloid leukemia (AML) is a malignancy with malignant breeding of bone marrow precursor cells, and the function and production of the normal cells are restrained [1]. AML always accompanies with specific gene variations, which can be served as the basis of its onset and effective treatment [2, 3]. Some gene abnormalities have been identified as independent prognostic factors. For example, high expressions of FUT3/6/7, PDK2/3, PAK3/7 and NCALD were proved as poor prognosis factors in AML [4-7]. While high FUT4 and PAK2 expressers have longer EFS and OS after chemotherapy [4, 6]. On account of the previous studies, there should be more research to explore the effect of gene expression on prognosis.

Spinster homolog (SPNS) is a protein stretching across cell membrane, with the function of transmembrane transporter. According to amino acid sequence homology analysis, SPNS members belong to major facilitator superfamily (MFS) [8, 9]. Previous study reported that SPNS1 and the vacuolar-type H+-ATPase (v-ATPase) could regulate proper autolysosomal biogenesis with optimal acidification, which is closely associated to developmental senescence and survival [10]. In addition, Yanagisawa et. al identified that SPNS1 was a favorable factor in Niemann-Pick type C disease (NPC) (-/-) cells [11]. SPNS2 is notarized to be the physiologically functional Sphingosine 1-phosphate (S1P) transporters, S1P is an effective and biologically active signaling molecules, which can promote the development of cancer by regulating cell proliferation, survival, migration, vascularization and lymphoangiogenesis [12, 13]. The relation between SPNS2 and S1P were previously found in animals, such as zebrafishand and mouse. Then Hisano et. al demonstrated that human SPNS2 can also transport S1P and its analogue, indicating SPNS2 may participate in the progress of cancer adjustment [14]. There were studies indicated that SPNS3 involved in sphingolipid pathways to mediate airway hyperresponsiveness and mast cell activation in asthma patients [15]. People have probed some fundamental effects of SPNS3, nevertheless still don't understand most of the roles that SPNS3 play in human disorders. But the prognosis of SPNSs in AML has never been investigated.

Here we conducted a prognosis study to investigate the impact of the SPNS genes in AML patients. Our study disclosed the guiding significance of SPNS expression in prognosis of AML, high expression of SPNS2 and SPNS3 were poor prognosis in chemotherapy patients, and SPNS3 was a poor indicator for OS in allo-HSCT patients.

Subjects and Methods

Patients

A total of 155 AML patients with complete clinical data and SPNS expression from The Cancer Genome Atlas (TCGA) database were included in this study (https://cancergenome.nih.gov/). Eighty-four patients underwent chemotherapy only, and 71 also received allogeneic hematopoietic stem cell transplantation (allo-HSCT). Clinical characteristics of AML were expounded, the end points of this study were event-free survival (EFS) and overall survival (OS). OS referred to the time from diagnosis to death for any reason or the last follow-up time. EFS refers to the time from diagnosis to the first event, such as relapse, death, etc. Clinical and molecular characteristics were expounded, including peripheral blood (PB), white blood cell (WBC) counts, PB blasts, bone marrow (BM) blasts, French-American-British (FAB) subtypes, and the frequencies of known recurrent genetic mutations. The informed consent of patients was obtained, and the study protocol was approved by the Washington University Human Studies Committee.

Statistical analysis

The clinical and molecular characteristics of the patients were summarized using descriptive statistical methods. Data sets were described by median and/or range. The Mann-Whitney U-test was used as appropriate to compare numerical comparison and χ2 test for comparison of categorical and numerical data between two groups. Survival rates were estimated using the Kaplan-Meier method and the log-rank test. The univariate and multivariate Cox proportional hazard models of EFS and OS were established using a limited backward elimination process. The statistical significance level was 0.05 for a two tailed test. All statistical analyses were performed using SPSS software 25.0, and GraphPad Prism software 7.0.

Bioinformatic Analysis

The median expression of SPNS2 or SPNS3 was demanded in 84 patients with chemotherapy-only group. The patients were divided into two group according to the median expression of SPNS2 or SPNS3, then take the expression of SPNS2 and SPNS3 minus the median expression of SPNS2 and SPNS3 respectively. Gglot2 was used to map SPNS2 or SPNS3 gene expression profiles in these 84 AML patients. The gene expression above the median level is high expression. The Rice Hmisc package rcorr function was employed to investigate the Pearson correlation coefficient of the gene expression matrix, then genes related to SPNS2 or SPNS3 expression were extracted (p<0.01, absolute correlation coefficient >0.3). And genes associated with SPNS2 or SPNS3 expression were performed by the KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis. An unsupervised clustering heat map was generated for the first enriched significant pathway gene expression of SPNS2 or SPNS3 using the R-package ComplexHeatmap.

Results

Prognostic significance of SPNS family in AML

All patients were divided into two groups according to median expression levels of the three SPNS members. The differences of EFS and OS between high and low expression subgroups were presented in Table 1. Kaplan-Meier analysis revealed that the chemotherapy-only patients with high SPNS2 or SPNS3 expression had an adverse effect on EFS and OS (all P < 0.05, Table 1, Fig. 1a-d). In the allo-HSCT group, high SPNS3 expressers had a shorter OS than patients with low SPNS3 expression (Table 1, Fig. 2).

Clinical and molecular characteristics of the patients

As shown in Table 2, the clinical and molecular characteristics of high and low SPNS2 and SPNS3 expression subgroups in chemotherapy group were compared. In the SPNS2high group, the group had more FAB-M1 (P < 0.001), fewer FAB-M4 (P = 0.004) and FAB-M5 patients (P = 0.003). No significant differences were observed in age and gender, WBC count, BM blasts, other FAB subtypes, risk stratification, frequencies of other genetic mutations (FLT3-ITD, NPM1, DNMT3A, IDH1/IDH2, RUNX1, NRAS/KRAS, TET2, and TP53) and relapse rates between the SPNS2high and SPNS2low groups. Compared with the SPNS3low subgroup, SPNS3high group had fewer patients with RUNX1-RUNX1T1 karyotype (P = 0.026), and fewer patients with good-risk (P = 0.026). There are no remarkable differences were found in age and gender, WBC count, BM blasts, and PB blasts, FAB subtypes, other risk stratification, frequencies of other genetic mutations (FLT3-ITD, NPM1, DNMT3A, IDH1/IDH2, RUNX1, NRAS/KRAS, TET2, and TP53) and relapse rates between the two subgroups.

 Table 1 

Comparison of EFS and OS between different expression levels of SPNS1-3

VariablesEFSOS
χ2P-valueχ2P-value
Chemotherapy-only group
SPNS1 (high vs. low)0.6350.4250.1310.718
SPNS2 (high vs. low)11.4650.0014.7840.029
SPNS3 (high vs. low)7.6180.0064.5990.023
Allo-HSCT group
SPNS1 (high vs. low)0.1370.7110.0340.854
SPNS2 (high vs. low)0.0330.8561.760O.185
SPNS3 (high vs. low)0.1350.71410.2070.001

Allo-HSCT allogeneic hematopoietic stem cell transplantation, EFS event-free survival, OS overall survival

 Fig 1 

Kaplan-Meier curves of event-free survival (EFS) and overall survival (OS) in patients who received chemotherapy-only. a, b High SPNS2 expressers had shorter EFS and OS than the low expressers. c, d High SPNS3 expressers had shorter EFS and OS than the low expressers.

J Cancer Image (Click on the image to enlarge.)
 Fig 2 

Kaplan-Meier curves of overall survival (OS) in patients who received transplantation treatment. High SPNS3 expressers had shorter OS than the low expressers in allo-HSCT group.

J Cancer Image (Click on the image to enlarge.)

The clinical and molecular characteristics of high and low SPNS3 expression in transplanted subgroup were shown in Table 3. Median age was 51 (range 18-72) years, with 19 cases older than 60 years. Thirty cases were man. The median WBC count, BM blasts, and PB blasts at diagnosis were 29.4 × 109/L, 71%, and 48.5%, respectively. The primary FAB subtypes were M1, M2, and M4 (71.6%). Thirty-two patients had abnormal karyotypes. The proportion of good, intermediate, and poor-risk patients were 9.9, 59.2, and 29.6%, respectively. NPM1 had the highest mutation frequency (n = 18, 25.4%), followed by DNMT3A (n = 17, 23.9%), FLT3 (n = 17, 23.9%), IDH1/2 (n = 17, 23.9%), RUNX1 (n = 8, 11.3%), NRAS/KRAS (n =7, 9.9%), TET2 (n = 4, 5.6%), TP53 (n = 4, 5.6%).Forty-eight patients had AML relapse. In regard to SPNS3 expression, SPNS3high group had more patients with normal karyotype (P = 0.017), more intermediate-risk (P = 0.016). No significant differences were observed in age and gender, WBC count, BM blasts and PB blasts, FAB subtypes, risk stratification, frequencies of other genetic mutations (FLT3-ITD, NPM1, DNMT3A, IDH1/IDH2, RUNX1, NRAS/KRAS, TET2, and TP53) and relapse rates between the SPNS3high and SPNS3low groups.

Multivariate analysis of possible prognostic factors in the chemotherapy-only group and allo-HSCT group

In order to evaluating the prognostic effects of SPNS2 and SPNS3, expression levels of SPNS2/3 (high vs. low), age (≥60 vs. <60 years), PB blast count (≥20% vs. <20%), FLT3-ITD (positive vs. negative), and other common genetic mutations (NPM1, DNMT3A, IDH1/IDH2, RUNX1 and TET2; mutated vs. wild) were selected for multivariate analysis (Table 4). In the chemotherapy-only group, we can conclude that high SPNS2 expression was an independent poor factor for EFS and OS (P = 0.006, P = 0.048, respectively). And in the allo-HSCT group, high SPNS3 expression (P = 0.002) and FLT3-ITD mutation (P = 0.035) were independent risk factors for OS.

Bioinformatic analysis of SPNS2 and SPNS3 in chemotherapy-only group

In order to explore the role of SPNS2 and SPNS3 in AML patients, we performed KEGG pathway enrichment analysis and mapped unsupervised clustering heat maps. The expressions of SPNS2 and SPNS3 were shown in Figure S1 and S2. There are 3100 positive and 1158 negative co-expression genes with SPNS2 (Table S1). The results of the KEGG pathway enrichment analysis revealed that Neurotrophin, North, Adipocytokine and Sphingolipid signaling pathway were enriched in high SPNS2 expressers (Fig 3A). And SPNS2 was positive correlated with AKT and TP53, and negative associated with PIK3R2, all these genes belong to Sphingolipid signaling pathway (Fig S2, Table S2). According to the Table S3, 1941 positive and 440 negative co-expression genes with SPNS3. Different from SPNS2, patients who have high expression of SPNS3 co-express with Sphingolipid, North, Neurotrophin, mTOR and ErbB signaling pathway (Fig 3B). Otherwise, the unsupervised clustering heat maps found that SPNS3 was associated with RPL and RPS family, which expressed in Sphingolipid signaling pathway (Fig S4, Table S4).

Discussion

In this study, we found high expression of SPNS2 and SPNS3 were poor prognostic factors in the patients who underwent chemotherapy only. Moreover, high expression of SPNS3 was a negative prognosis factor for OS in allo-HSCT patients. SPNS2 and SPNS3 were independent dismal prognosis factors in chemotherapy and all-HSCT group respectively.

SPNS2, as a functional transporter of S1P, has been identified to be associated with many cancers. For example, previous study found that knockout SPNS2 gene can worsen non-small cell lung cancer [16], another research illustrated SPNS2 may play a role in inhibiting the development and progression of gastric cancer [17]. However, another previous paper revealed that lacking of SPNS2 can reduce the regulation ability of S1P on lymphocyte transport, leading to the reduced lymphocyte circulation in tissues, the proportion of T cells and NK cells then increased to kill the tumor cells more effectively [18]. And SPNS2 can also promote the tumor growth via transporting S1P to extracellular environment [19].

 Table 2 

Comparison of clinical and molecular characteristics in different SPNS2/3 expression groups among chemotherapy-only group.

CharacteristicsSPNS2PSPNS3P
High (n=42)Low (n=42)High (n=42)Low (n=42)
Age (years), median (range)67.5 (25-88)66 (22-81)0.975a66 (33-88)67 (22-81)0.982a
Age group, n (%)0.814b0.814b
<60 years12 (28.6)14 (33.3)14 (33.3)12 (28.6)
≥60 years30 (71.4)28 (66.7)28 (66.7)30 (71.4)
Gender, n (%)0.662b0.382b
Male21 (50.0)24 (57.1)25 (59.5)20 (47.6)
Female21 (50.0)18 (42.9)17 (40.5)22 (52.4)
WBC (×109/L),
median (range)
14.8
(0.7-297.4)
14.6
(1.9-131.5)
0.862a16.5
(0.7-134.4)
13.3
(1.0-297.4)
0.943a
BM blasts (%), median (range)73.5
(32-99)
69.5
(30-95)
0.455a74
(30-98)
68
(32-99)
0.785a
PB blasts (%), median (range)49.5
(0-98)
7.5
(0-90)
0a38
(0-97)
17.5
(0-98)
0.149a
FAB subtypes, n (%)
M06 (14.3)1 (2.4)0.109b3 (7.1)4 (9.5)1.000b
M118 (42.9)2 (4.8)0b13 (31.0)7 (16.7)0.200b
M213 (31.0)8 (19.0)0.314b9 (21.4)12 (28.6)0.615b
M44 (9.5)16 (38.1)0.004b12 (28.6)8 (19.0)0.443b
M51 (2.4)11 (26.2)0.003b4 (9.5)8 (19.0)0.350b
M60 (0.0)1 (2.4)1.000b1 (2.4)0 (0.0)1.000b
M70 (0.0)2 (4.8)0.494b0 (0.0)2 (4.8)0.494b
Cytogenetics, n (%)
Normal19 (45.2)21 (50.0)0.827b22 (52.4)18 (42.9)0.512b
t(9;22)/BCR-ABL10 (0.0)1 (2.44)1.000b0 (0.0)1 (2.4)1.000b
inv(16)/CBFβ-MYH111 (2.4)5 (11.9)0.202b2 (4.8)4 (9.5)0.676b
Complex7 (16.7)5 (11.9)0.520b7 (16.7)4 (9.5)0.520b
11q23/MLL1 (2.4)2 (4.8)1.000b2 (4.8)1 (2.4)1.000b
t(8;12)/RUNX1-RUNX1T13 (7.1)3 (7.1)1.000b0 (0.0)6 (14.3)0.026b
Others11 (26.2)6 (14.3)0.277b9 (21.4)8 (19.0)1.000b
Risk, n (%)
Good4 (9.5)8 (19.0)0.350b2 (4.8)10 (23.8)0.026b
Intermediate24 (57.1)27 (64.3)0.655b27 (64.3)24 (57.1)0.655b
Poor12 (28.6)7 (16.7)0.297b11 (26.2)8 (19.0)0.603b
FLT3, n (%)0.668b0.447b
FLT3-ITD6 (4.3)9 (21.4)8 (19.0)7 (16.7)
FLT3-TKD9 (9.5)3 (7.1)5 (11.9)2 (4.8)
Wildtype32 (76.2)30 (71.4)29 (69.0)33 (78.6)
NPM1, n (%)0.641b0.160b
Mutation15 (35.7)12 (28.6)17 (40.5)10 (23.8)
Wildtype27 (64.3)30 (71.4)25 (59.5)32 (76.2)
DNMT3A, n (%)0.625b0.141b
Mutation13 (31.0)10 (23.8)15 (35.7)8 (19.0)
Wildtype29 (69.0)32 (76.2)27 (64.3)34 (81.0)
IDH1/IDH2, n (%)0.570b1.000b
Mutation9 (21.4)6 (14.3)7 (16.7)8 (19.0)
Wildtype33 (78.6)36 (85.7)35 (83.3)34 (81.0)
RUNX1, n (%)0.713b1.000b
Mutation5 (11.9)3 (7.1)4 (9.5)4 (9.5)
Wildtype37 (88.1)39 (92.9)38 (90.5)38 (90.5)
NRAS/KRAS, n (%)0.756b0.756b
Mutation5 (11.9)7 (16.7)5 (11.9)7 (16.7)
Wildtype37 (88.1)35 (83.3)37 (88.1)35 (83.3)
TET2, n (%)0.194b1.000b
Mutation8 (19.0)3 (7.1)5 (11.9)6 (14.3)
Wildtype34 (81.0)39 (92.9)37 (88.1)36 (85.7)
TP53, n (%)1.000b0.520b
Mutation5 (11.9)6 (14.3)7 (16.7)4 (9.5)
Wildtype37 (88.1)36 (85.7)35 (83.3)38 (90.5)
Relapse/n (%)0.261b0.822b
Yes13 (31.0)19 (45.2)15 (37.5)17 (40.5)
No29 (69.0)23 (54.8)27 (64.3)25 (59.5)

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

aMann-Whitney U-test

bChi-square test

In addition, the KEGG pathway enrichment manifested that SPNS2 was closely related to the Sphingolipid signaling pathway, and a study revealed that Sphingomyelin pathway is a kind of Sphingolipid signaling pathway, which can lead to either cell proliferation and differentiation or to apoptosis. [20]. The unsupervised clustering heat maps showed SPNS2 can co-express with AKT in Sphingolipid signaling pathway. Previous passage talked a function material acid ceramide which participated in the Sphingolipid signaling pathway, can regulate cell apoptosis via AKT pathway. This suggested that there were some association between the AKT pathway and the Sphingolipid signaling pathway [21]. However, the mechanism is unclear. In multiple analysis, SPNS2 was proved to be an independent poor factor for the survival, indicating that SPNS2 may related to carcinogenic function in AML, but the mechanism still needs to be further investigated.

 Table 3 

Comparison of clinical and molecular characteristics in different SPNS3 expression groups among allo-HSCT group

CharacteristicsTotalSPNS3P
High (n=35)Low (n=36)
Age (years), median (range)51 (18-72)53 (21-65)48.5 (18-72)0.360a
Age group, n (%)0.793b
<60 years52 (73.2)25 (71.4)27 (75.0)
≥60 years19 (26.8)10 (28.6)9 (25.0)
Gender, n (%)0.153b
Male30 (42.3)18 (51.4)12 (33.3)
Female41 (57.7)17 (48.6)24 (66.7)
WBC (×109/L), median (range)29.4 (0.6-223.8)19.6 (0.9-202.7)30.7 (0.6-223.8)0.441a
BM blasts (%), median (range)71 (30-100)69 (34-100)75 (30-99)0.982a
PB blasts (%), median (range)48.5 (0-96)57 (0-96)43 (0-94)0.118a
FAB subtypes, n (%)
M09 (12.7)5 (14.3)4 (11.1)0.735b
M123 (32.4)14 (40.0)9 (25.0)0.211b
M218 (25.4)11 (31.4)7 (19.4)0.285b
M31 (1.4)0 (0.0)1 (2.8)1.000b
M413 (13.8)4 (11.4)9 (25.0)0.220b
M54 (5.6)1 (2.9)3 (8.3)0.614b
M61 (1.4)0 (0.0)1 (2.8)1.000b
M71 (1.4)0 (0.0)1 (2.8)1.000b
Cytogenetics, n (%)
Normal32 (45.1)21 (60.0)11 (30.6)0.017b
t(9;22)/BCR-ABL12 (2.8)0 (0.0)2 (5.6)0.493b
inv(16)/CBFβ-MYH115 (7.0)1 (2.9)4 (11.1)0.357b
Complex11 (15.5)5 (14.3)6 (16.7)1.000b
11q23/MLL3 (4.2)1 (2.9)2 (5.6)1.000b
t(8;12)/RUNX1-RUNX1T11 (1.4)0 (0.0)1 (2.8)1.000b
Others16 (22.5)6 (17.1)10 (27.8)0.396b
Risk, n (%)
Good7 (9.9)1 (2.9)6 (16.7)0.107b
Intermediate42 (59.2)26 (74.3)16 (44.4)0.016b
Poor21 (29.6)8 (22.9)13 (36.1)0.300b
FLT3, n (%)0.099b
FLT3-ITD17 (23.9)11 (31.4)6 (16.7)
FLT3-TKD3 (4.2)0 (0.0)3 (8.3)
Wildtype51 (71.8)24 (68.6)27 (75.0)
NPM1, n (%)0.107b
Mutation18 (25.4)12 (34.3)6 (16.7)
Wildtype53 (74.6)13 (65.7)30 (83.3)
DNMT3A, n (%)0.415b
Mutation17 (23.9)10 (28.6)7 (19.4)
Wildtype54 (76.1)25 (71.4)29 (80.6)
IDH1/IDH2, n (%)0.173b
Mutation17 (23.9)11 (31.4)6 (16.7)
Wildtype54 (76.1)24 (68.6)30 (83.3)
RUNX1, n (%)0.151b
Mutation8 (11.3)6 (17.1)2 (5.6)
Wildtype63 (88.7)29 (82.9)34 (94.4)
NRAS/KRAS, n (%)0.484b
Mutation7 (9.9)4 (11.4)3 (8.3)
Wildtype64 (90.1)31 (88.6)33 (91.7)
TET2, n (%)0.614b
Mutation4 (5.6)1 (2.9)3 (8.3)
Wildtype67 (94.4)34 (97.1)33 (91.7)
TP53, n (%)1.000b
Mutation4 (5.6)2 (5.7)2 (5.6)
Wildtype67 (94.4)33 (94.3)34 (94.4)
Relapse/n (%)0.614b
Yes48 (67.6)25 (71.4)23 (63.9)
No23 (32.4)10 (28.6)13 (36.1)
 Fig 3 

KEGG enrichment of Co-expression genes of SPNS2 and SPNS3. A. Results of the KEGG pathway enrichment analysis associated with SPNS2 expression. B. Results of the KEGG pathway enrichment analysis associated with SPNS3 expression.

J Cancer Image (Click on the image to enlarge.)

SPNS3 is an atypical Solute carriers (SLCs) of major facilitator superfamily (MFS) type [22], and it can mediate the progress of the apoptosis and autophagy in mammal [23-25]. Autophagy-lysosome pathway (ALP) is one of the most important approach to eliminate abnormal proteins in human cells and there were some studies indicated that some genetic variation in autophagy-lysosome pathway plays a vital role in cancer development, such as lung cancer, gastric cancer, breast cancer, and renal cell carcinoma [26]. From the KEGG genes enrichment we can see SPNS3 mainly takes part in the Sphingolipid signaling pathway, and SPNS3 may also develop its function via similar mechanism as SPNS2 in AML [20]. In our study, overexpressed SPNS3 had a bad significance in both the chemotherapy group and the all-HSCT group. According to the above information, overexpression of SPNS3 may regulate and control the progression, proliferation and differentiation of AML by autophagy. As patients with high SPNS3 expression had bad survivals, SPNS3 may can be used as predictor for AML patients in the future.

 Table 4 

Multivariate analysis of EFS and OS in chemotherapy-only group and allo-HSCT group.

VariablesEFSOS
HR(95%CI)P-valueHR(95%CI)P-value
Chemotherapy-only group
SPNS23.072 (1.378-6.851)0.0061.884 (1.006-3.526)0.048
SPNS31.525 (0.737-3.156)0.2251.287 (0.728-2.276)0.386
PB (≥20 vs. <20 × 10%)0.680 (0.341-1.359)0.2750.653 (0.374-1.140)0.134
FLT3-ITD (positive vs. negative)0.889 (0.398-1.988)0.7750.863 (0.443-1.681)0.666
NPM1 (mutated vs. wild)0.870 (0.398-1.948)0.7360.875 (0.473-1.617)0.670
DNMT3A (mutated vs. wild)1.265 (0.595-2.687)0.5411.626 (0.920-2.871)0.094
TET2 (mutated vs. wild)0.476 (0.161-1.410)0.1800.619 (0.296-1.294)0.202
Allo-HSCT
SPNS31.092 (0.289-4.126)0.8972.789 (1.257-5.339)0.002
Age (≥60 vs. <60 years)0.740 (0.235-2.331)0.6071.061 (0.560-2.010)0.856
PB (≥20 vs. <20 × 10%)0.428 (0.149-1.227)0.1140.774 (0.404-1.481)0.439
FLT3-ITD (positive vs. negative)2.068 (0.450-9.508)0.3512.359 (1.062-5.240)0.035
NPM1 (mutated vs. wild)0.597 (0.132-2.697)0.5020.502 (0.214-1.174)0.112
IDH1/IDH2 (mutated vs. wild)1.456 (0.407-5.204)0.5630.789 (0.364-1.707)0.547
RUNX1(mutated vs. wild)2.275 (0.415-12.468)0.3441.529 (0.614-3.807)0.361

EFS event-free survival, OS overall survival, HR hazard ratio, CI confidential interval, PB peripheral blood

In multivariate analysis, SPNS2 was proved to be an independent unfavorable prognosis factor for both EFS and OS in chemotherapy group, and SPNS3 and FLT3-ITD were independent unfavorable prognosis factors for OS in the allo-HSCT group. Our result was along with the previous studies that FLT3-ITD was an adverse prognostic factor in AML [27, 28]. And in our study, SPNS2 and SPNS3 were proved to be independent negative prognosis factors in chemotherapy and allo-HSCT respectively.

In summary, high expressions of SPNS2 and SPNS3 can indicate adverse prognosis in chemotherapy AML patients, and the prognosis effect of SPNS2 can be overcome by allo-HSCT, and they might be used as predictors for AML patients in the future. Nevertheless, a larger sample size was needed to further validate our result and pathogenesis in AML still need further investigation.

Abbreviations

AML: Acute myeloid leukemia; SPNS: Spinster homolog; EFS: Event-free survival; OS: Overall survival; MFS: Major facilitator superfamily; NPC: Niemann-Pick type C disease; S1P: Sphingosine 1-phosphate; TCGA: The Cancer Genome Atlas; allo-HSCT: Allogeneic hematopoietic stem cell transplantation; PB: Peripheral blood; WBC: White blood cell; BM: Bone marrow; FAB: French-American-British; KEGG: Kyoto Encyclopedia of Genes and Genomes; SLCs: Solute carriers; ALP: Autophagy-lysosome pathway.

Supplementary Material

Attachment

Supplementary figures and tables.

Acknowledgements

We thank the TCGA database for providing data for us in this report.

Funding

This work was supported by grants from the National Natural Science Foundation of China (81600089).

Author Contributions

LF designed the outline. WHH and TTQ, drafted the manuscript. WHH, TTQ and TSZ designed the figures and tables. ZHC, CZS, CJL and CD offered professional suggestions to the manuscript. All authors read and approved the final manuscript.

Availability of data and materials

The datasets of this report were generated by TCGA.

Ethical approval and consent to participate

This study was approved by the Helsinki declaration and its subsequent amendments.

Competing Interests

The authors have declared that no competing interest exists.

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Author contact

Corresponding address Corresponding authors: Lin Fu, E-mail: fulin022com; Yan Liu, E-mail: liuyan5515com; Longzhen Cui, E-mail: clzhgxtcom


Received 2020-2-10
Accepted 2020-5-2
Published 2020-5-18