A novel scoring system for acute myeloid leukemia risk assessment based on the expression levels of six genes.

Acute myeloid leukemia (AML) is the most common type of acute leukemia and is a heterogeneous clonal disorder. At present, the pathogenesis of AML and potential methods to effectively prevent AML have become areas of interest in research. In the present study, two messenger ribonucleic acid sequencing datasets of patients with AML were downloaded from the Cancer Genome Atlas and Gene Expression Omnibus databases. The differentially expressed genes (DEGs) of the poor and good prognosis groups were screened using the Linear Models for Microarray Data package, and the prognosis‑related genes were screened using univariate Cox regression analysis. A total of 206 significant DEGs were identified. Following univariate and multivariate Cox regression analysis, 14 genes significantly associated with prognosis were screened and six of these genes, including triggering receptor expressed on myeloid cells 2 (TREML2), cysteine‑glutamate transporter (SLC7A11), NACHT, LRR, and PYD domains‑containing protein 2 (NLRP2), DNA damage‑inducible transcript 4 protein (DDIT4), lymphocyte‑specific protein 1 (LSP1) and C‑type lectin domain family 11 member A (CLEC11A), were used to construct model equations for risk assessment. The prognostic scoring system was used to evaluate risk for each patient, and the results showed that patients in the low‑risk group had a longer survival time, compared with those in the high‑risk group (P=9.59e‑06 for the training dataset and P=0.00543 for the validation dataset). A total of eight main Kyoto Encyclopedia of Genes and Genomes pathways were identified, the top three of which were hematopoietic cell lineage, focal adhesion, and regulation of actin cytoskeleton. Taken together, the results showed that the scoring system established in the present study was credible and that the six genes were identified, which were significantly associated with the risk assessment of AML, offer potential as prognostic biomarkers. These findings may provide clues for further clarifying the pathogenesis of AML.

Introduction

Acute myeloid leukemia (AML) is the most common type of acute leukemia. It is a heterogeneous clonal disorder characterized by an increase in the number of myeloid cells in the marrow and an arrest in their maturation, frequently resulting in hematopoietic insufficiency (1). The annual incidence of AML in the United States in 1999 was ~2.4 per 100,000 individuals, and the prevalence of the condition increased progressively with age to reach a peak of 12.6 per 100,000 individuals in adults aged 65 years or older (1). The incidence of AML has become higher than ever. In 2016, the number of new cases of AML in the United States was 19,950, representing an increase of 6,090 from the number reported in 2013 (2,3).

By contrast, the number of cases of AML-associated mortality in 2016 revealed an increase of only 230 cases, compared with the number of cases of AML-associated mortality in 2013 (2,3). With the ongoing improvements in chemotherapeutic protocols in support therapy, and the development of hematopoietic stem cell transplantation techniques, the prognosis of patients with AML has improved. However, there remain several challenges in the clinical treatment of AML. For example, it was previously reported that 10–20% of patients with AML do not enter remission following their first course of chemotherapy, a number of patients succumb to mortality due to complications of chemotherapy, and >50% of affected patients are expected to eventually relapse with low remission rates and short median survival rates (4,5). Therefore, AML remains one of the most difficult diseases to treat clinically; therefore, the examination of the pathogenesis of AML and methods to effectively prevent the condition have become areas of interest in research.

At present, the pathogenesis of AML remains to be fully elucidated, and it is generally considered to involve multiple mutations of gene loci with numerous mechanisms. Previous studies have identified several prognostic indicators for AML, including age, cytogenetic findings, white blood cell count, and the presence or absence of an antecedent hematologic disorder (e.g., myelodysplasia) (6). Until the 1990s, cytogenetic findings represented the most useful prognostic factor (7,8). However, additional factors associated with the pathogenesis and prognosis of AML have been found, including cell karyotype, micro-ribonucleic acid-155 (9), and gene mutation and expression (10). The aberrant expression of certain specific genes associated with hematopoiesis, bone marrow differentiation and immune stress can significantly affect the chemotherapeutic effects on and the prognosis of AML. For example, the high expression of brain and acute leukemia, cytoplasmic (BAALC) and MN1 has a close association with the poor prognosis of AML (10–13). As the previous prognostic scoring systems that have been used are mainly based on age, cytogenetic findings and white blood cell count, the examination of additional AML-related genes and the establishment of a more effective scoring system based on the expression levels of these genes are of important theoretical and clinical significance.

In order to investigate the possible unknown important pathogenic mechanisms and novel biomarkers of AML, comprehensive bioinformatics analysis methods were used in the present study. The messenger ribonucleic acid sequencing (mRNA-seq) data of patients with AML were downloaded from the Cancer Genome Atlas (TCGA) database, and were integrated with clinical data and survival information to screen out differentially expressed genes (DEGs) associated with AML. A prognostic scoring system was established based on the screened genes and simultaneously validated by a dataset from the Gene Expression Omnibus (GEO) database. The reliability of the novel prognostic scoring system was further validated by performing a correlation analysis between clinical characteristics and prognosis, and stratified analysis between risk assessment and clinical characteristics.

Materials and methods

Data sources

The mRNA‑seq expression profiles of adult patients with AML were downloaded from TCGA database (https://gdc-portal.nci.nih.gov/) on April 10, 2017, having been sequenced on the Illumina HiSeq™ 2000 platform (Illumina, San Diego, CA, USA). In total, there were 200 bone marrow tissue samples from patients with AML, of which 173 had corresponding clinical information barcode numbers. This dataset was used as the training dataset.

For the validation dataset, 'acute myeloid leukemia' and 'human' were used as key words to search the GEO database (https://www.ncbi.nlm.nih.gov/geo) on April 27, 2017. Subsequently, the GSE12417 expression dataset (14) from the GPL96 platform, which contained a total of 163 AML adult bone marrow tissue samples, was selected and downloaded. In the original article of the GSE12417 dataset, the trials were approved by the local institutional review boards of all participating centers, and informed consent was obtained from all patients in accordance with the Declaration of Helsinki (14). The overall analytical process used in the present study is presented in Fig. 1.

Figure 1

Overall analytical process of the study. TGCA, The Cancer Genome Atlas; AML, acute myeloid leukemia; GEO, Gene Expression Omnibus; KEGG, Kyoto Encyclopedia of Genes and Genomes.

Clinical information

The clinical information of the training and validation datasets were received and then sorted, as shown in Table I. Survival information was provided; the overall survival rates were 19.30±19.79 months in the training dataset and 15.12±14 months in the validation dataset, respectively (Table I).

Table I

Clinical information of TCGA training and validation datasets.

Clinical characteristics	TCGA (N=174)	GSE1241 (N=326)
Age (mean years ± standard deviation)	55.28±16.14	55.66±14.82
Gender (male/female/−)	93/80/1	–
FLT3 mutation (positive/negative/−)	50/116/8	–
BCR-ABL (positive/negative/−)	1/13/160	–
IDH1 R132 (positive/negative/−)	15/153/6	–
IDH1 R140 (positive/negative/−)	13/153/8	–
IDH1 R172 (positive/negative/−)	2/167/5	–
Activating RAS (positive/negative/−)	9/161/4	–
NPMc (positive/negative/−)	42/128/4	–
PML-RAR (positive/negative/−)	5/7/162	–
Death (deceased/alive/−)	103/57/14	206/120
Overall survival months (mean ± standard deviation)	19.30±19.79	15.12±14.00

'–' Indicates information unavailable. TCGA, The Cancer Genome Atlas; FLT3, FMS-like tyrosine kinase 3; BCR-ABL, breakpoint cluster region-Abelson; IDH1, isocitrate dehydrogenase (NADP+) 1, cytosolic; RAS, reticular activating system; NPMc, nucleophosmin mutation; PML-RAR, promyelocytic leukemia-retinoic acid receptor.

Screening of DEGs

Among the 173 AML samples in the training dataset, 160 had survival and prognosis information. Following the removal of those without clinical information and those with survival rates of <6 months, 141 samples remained for further analysis. Of these, samples with survival rates of <12 months were defined as the poor prognosis group, whereas those with survival rates of >24 months were classified into the good prognosis group. The DEGs of the two groups were examined using the Linear Models for Microarray Data (LIMMA) package (15) of R3.1.0 with a false discovery rate (FDR) threshold of <0.05.

Screening of genes associated with prognosis

For the 141 samples with survival rates of >6 months, univariate Cox regression analysis was used in the survival package of R3.1.0 language (16) to screen for genes significantly correlated with prognosis. P‑values were examined by log‑rank and P≤0.05 was set as the threshold of significant correlation. Multivariate Cox regression was then performed to narrow down the eligible genes associated with prognosis.

Establishment of the risk assessment model

Using the genes obtained in the above analyses, a system of patient risk assessment was established by regression factor-weighted gene expression based on linear combination to acquire the risk values for each patient. That is, each risk value was a linear combination of the mRNA expression values obtained following weighting with regression coefficients. The risk score for each patient was calculated according to the following equation: Risk score = βGENE1 × ExprGENE1 + β GENE2 × ExprGENE2 + ⋯ + βGENEn × ExprGENEn, where β represents the coefficient for each gene obtained from the training set and was used to validate the risk of patients in the validation dataset. The difference in prognosis between the high-risk and low-risk groups (with the risk score median as the break point) was also assessed.

Correlation analysis between risk scores and clinical features

The risk scores of samples in the training set and validation set were calculated according to the aforementioned risk assessment system. Likewise, the samples were divided into high and low risk types with the threshold being the median risk score. Additionally, corresponding clinical features of those samples that were significantly associated with prognosis were analyzed using Kaplan-Meier (KM) survival analysis. Consequently, their correlation analysis was performed by Cox regression, which combined the clinical data and the corresponding samples.

Stratification analysis of clinical features significantly correlated with risk scores

According to the aforementioned available information, stratification analysis was performed on the clinical features significantly associated with high and low risk. The detailed analytical procedure included: i) calculation of the correlation between the expression values of each selected gene and their high or low risk; ii) calculation of the correlation between the high- and low-risk groups with their respective survival prognosis with regard to the same risk condition; and iii) calculation of the correlation between different clinical conditions and survival prognosis with the same risk factor.

Functional analysis of important genes associated with high and low risk

According to the scores calculated by the risk assessment model, the samples were divided into a high risk and low risk group. In the training set, the DEGs were screened using the LIMMA package (15) (FDR<0.05). Subsequently, the genes associated with positive or negative risk were selected on the basis of the correlation coefficient between their expression values and corresponding risk values. Thereafter, their biological functions were analyzed by the Database for Annotation, Visualization, and Integrated Discovery (17) to screen the significantly enriched biological processes and pathways in combination with information from the Gene Ontology (GO; http://www.geneontology.org/) and the Kyoto Encyclopedia of Genes and Genomes (KEGG; http://www.genome.jp/kegg/pathway.html) databases. The cut‑off for the selection of significant categories was P<0.05.

Results

Identification and validation of a six gene prognostic signature

The genes in the training datasets were first filtered according to their expression values, and those with an average expression of <5 were removed. Subsequently, a total of 141 samples were screened out following the exclusion of those with survival rates of <6 months. Among the 141 samples, further grouping was performed to differentiate the samples with good or poor prognosis. Finally, a total of 55 samples from deceased patients with survival rates of <12 months were classified as the poor prognosis group, and 27 patient samples from living patients with survival rates of >24 months were classified as the good prognosis group. The DEGs of the two groups were screened and a total of 206 significant DEGs were screened out.

Subsequently, the prognostic values of the above 206 DEGs were assessed by univariate Cox regression analysis and a total of 162 genes associated with prognosis were screened out. Multivariate Cox regression analysis was then performed for these 162 genes associated with prognosis, and a total of 14 genes significantly associated with prognosis were selected according to the threshold of P<0.01. Finally, the model equations for risk assessment were established based on six genes, specifically NACHT, LRR, and PYD domains-containing protein 2 (NLRP2); triggering receptor expressed on myeloid cells 2 (TREML2); cysteine-glutamate transporter (SLC7A11); DNA damage-inducible transcript 4 protein (DDIT4); lymphocyte-specific protein 1 (LSP1); and C-type lectin domain family 11 member A (CLEC11A) (Table II). The risk scores were determined as follows: Risk score = 1.053 × ExprTREM L2 + 0.426 × ExprSLC7A11 + 0.222 × ExprNLRP2 + 0.548 × E xprDDIT4 + (−0.771) × ExprLSP1 + (−0.396) × ExprCLEC11A

Table II

Information of six genes based on which the model equations for risk assessment were constructed.

Gene	Coef	HR	P-value
TREML2	1.053	0.349	1.76E-05
SLC7A11	0.426	0.653	9.52E-05
NLRP2	0.222	0.801	0.000442
DDIT4	0.548	0.578	0.000609
LSP1	−0.771	2.162	0.000692
CLEC11A	−0.396	1.486	0.000989

TREML2, triggering receptor expressed on myeloid cells 2; SLC7A11, cysteine-glutamate transporter; NLR3, NACHT, LRR, and PYD domains-containing protein 2; DDIT4, DNA damage-inducible transcript 4 protein; LSP1, lymphocyte‑specific protein 1; CLEC11A, C-type lectin domain family 11 member A.

Validation of the model classification effect

The model equation for risk assessment was used to evaluate the risk of each patient, following which patients in the TCGA training group were divided into high-risk patients and low-risk patients according to the median risk score. Patients in the low-risk group had longer survival rates, compared with those in the high-risk group. In the training dataset, the mean survival rate of 71 samples in the high-risk group was 14.06±14.81 months, whereas that of the 70 samples in the low-risk group was 28.9 6±21.57 months (P=9.59e-06). In the validation dataset, the mean survival rate of 81 samples in the high-risk group was 17.48±7.49 months, whereas that of 82 samples in the low-risk group was 28.24±12.89 months (P=0.00543). The significant association between the expression of the above six genes and the survival information was validated using KM survival curve analysis (Fig. 2A and B).

Figure 2

Kaplan-Meier survival curves. Kaplan-Meier survival curves of high-risk and low-risk group samples in the (A) training dataset and (B) validation dataset.

Expression profile of the six important genes

In the training dataset, the expression values of TREML2, SLC7A11, NLRP and DDIT in the high‑risk group were significantly higher, compared with those in the low-risk group (P<0.005), whereas the expression values of LSP and CLEC11 in the high-risk group were significantly lower, compared with those in the low-risk group (P<0.005) (Fig. 3A). In the validation dataset, the expression trends of five genes were similar to those in the training dataset (P<0.005), with the exception of NLRP2 (0.01≤P<0.05) (Fig. 3B).

Figure 3

Expression values of the six signature genes. Expression values of genes in the (A) training dataset and (B) validation dataset. Significant differences between low-risk samples (blue bar) and high-risk samples (red bar) are indicated (***P<0.005; *0.01≤P<0.05). TREML2, triggering receptor expressed on myeloid cells 2; SLC7A11, cysteine-glutamate transporter; NLRP, NACHT, LRR, and PYD domains-containing protein 2; DDIT4, DNA damage-inducible transcript 4 protein; LSP1, lymphocyte‑specific protein 1; CLEC11A, C‑type lectin domain family 11 member A.

Correlation analysis between risk score and clinical features

The clinical features that were significantly associated with prognosis were screened using univariate and multivariate Cox regression analysis and the results showed that, in addition to risk score, which was the independent prognostic factor, age was another factor associated with clinical prognosis (Table III).

Table III

Results of clinical prognosis by Cox regression analysis.

Clinical characteristic	Univariate Cox	Multivariate Cox
Gender (male vs. female)	0.215	–
FLT3 mutation (positive vs. negative)	0.051	–
IDH1 R132 (positive vs. negative)	0.770	–
IDH1 R140 (positive vs. negative)	0.985	–
IDH1 R172 (positive vs. negative)	0.876	–
Activating RAS (positive vs. negative)	0.892	–
NPMc (positive vs. negative)	0.027	0.735
Age (above vs. below median of 58 years)	0.000283	0.018
Risk score	2.12E-06	0.0000712

FLT3, FMS-like tyrosine kinase 3; IDH1, isocitrate dehydrogenase (NADP+) 1, cytosolic; RAS, reticular activating system; NPMc, nucleophosmin mutation.

Correlation between individual signature genes and risk score

The correlation between the six individual signature genes (TREML2, SLC7A11, NLRP2, DDIT4, LSP and CLEC11A) and risk score model equations were analyzed using univariate Cox regression. As shown in Table IV, five genes, including TREML2, SLC7A11, NLRP2, LSP and CLEC11A, were associated with age (P<0.05) and TREML was also associated with FMS-like tyrosine kinase 3 (FLT3) mutation and nucleophosmin mutation (NPMc) (P<0.05).

Table IV

Correlation between six individual signature genes and risk score.

Clinical characteristic	TREML2	SLC7A11	NLRP2	DDIT4	LSP1	CLEC11A
Age (>58, vs. <58 years)	0.039	0.031	0.001	0.113	0.001	0.029
Gender (male, vs. female)	0.487	0.378	0.149	0.914	0.294	0.835
FLT3 mutation (positive, vs. negative)	0.012	0.494	0.095	0.274	0.250	0.130
IDH1 R132 (positive, vs. negative)	0.424	0.376	0.661	0.270	0.809	0.304
IDH1 R140 (positive, vs. negative)	0.896	0.319	0.647	0.701	0.380	0.625
IDH1 R172 (positive, vs. negative)	0.852	0.452	0.878	0.827	0.503	0.964
Activating RAS (positive, vs. negative)	0.558	0.166	0.860	0.969	0.152	0.766
NPMc (positive, vs. negative)	0.049	0.329	0.336	0.987	0.807	0.898

TREML2, triggering receptor expressed on myeloid cells 2; SLC7A11, cysteine-glutamate transporter; NLRP, NACHT, LRR, and PYD domains-containing protein 2; DDIT4, DNA damage-inducible transcript 4 protein; LSP1, lymphocyte‑specific protein 1; CLEC11A, C-type lectin domain family 11 member A; FLT3, FMS-like tyrosine kinase 3; IDH1, isocitrate dehydrogenase (NADP+) 1, cytosolic; RAS, reticular activating system; NPMc, nucleophosmin mutation.

The correlations between clinical features and prognosis were analyzed. The results showed that age, FLT3 mutation, isocitrate dehydrogenase (NADP+) 1, cytosolic R132, and NPMc were significantly associated with prognosis (Table V). The KM survival curves of the above four factors in the low-risk and high-risk groups are shown in Fig. 4A–D.

Table V

Correlation between risk score and prognosis in the same clinical setting.

Clinical characteristic	P-value
Age (>58 years, N=70)	0.081
Age (<58 years, N=71)	6.30E-05
Gender (male, N=75)	0.249
Gender (female, N=66)	0.102
FLT3 mutation (positive, N=37)	0.020
FLT3 mutation (negative, N=97)	8.75E-05
IDH1 R132 (positive, N=12)	0.117
IDH1 R132 (negative, N=126)	1.21E-05
IDH1 R140 (positive, N=12)	0.741
IDH1 R140 (negative, N=125)	0.251
IDH1 R172 (positive, N=2)	–
IDH1 R172 (negative, N=135)	0.349
Activating RAS (positive, N=8)	0.059
Activating RAS (negative, N=130)	2.44E-01
NPMc (positive, N=36)	0.015
NPMc (negative, N=102)	6.97E-05

FLT3, FMS-like tyrosine kinase 3; IDH1, isocitrate dehydrogenase (NADP+) 1, cytosolic; RAS, reticular activating system; NPMc, nucleophosmin mutation.

Figure 4

KM survival curves in the low- and high-risk patient groups. (A) KM survival curves of patients (a) younger and (b) older than the median age (58 years). (B) KM curves of patients in the (a) IDH R132-negative group and (b) IDH R132-positive group, (C) KM curves of patients in the (a) FLT3 mutation-negative group and (b) FLT3 mutation-positive group. (D) KM curves of patients in the (a) NPMc-negative group and (b) NPMc-positive group. The high-risk group is denoted by red and purple curves, the low-risk group is denoted by black and blue curves. KM, Kaplan-Meier; L, low-risk; H, high-risk; IDH1 R132, isocitrate dehydrogenase (NADP+) 1, cytosolic R132; FLT3, FMS-like tyrosine kinase 3; NPMc, nucleophosmin mutation.

The correlations between different clinical conditions and survival prognosis under the same risk condition were analyzed, and the results showed that age was significantly associated with prognosis under the same risk conditions (Table VI). The KM survival curves of age and prognosis in the low-risk and high-risk groups are shown in Fig. 5A–C. The risk score, overall survival, and the expression values of the six signature genes in the training dataset (Fig. 6Aa-c) and validation dataset (Fig. 6Ba-c) are shown in Fig. 6.

Table VI

Correlation between clinical features and prognosis under the same risk factors.

Clinical characteristic	High risk	Low risk
Age (above vs. below median of 58 years)	0.054	6.46E-05
Gender (male vs. female)	0.310	0.131
FLT3 mutation (positive vs. negative)	0.818	0.574
IDH1 R132 (positive vs. negative)	0.539	0.206
IDH1 R140 (positive vs. negative)	0.674	0.067
IDH1 R172 (positive vs. negative)	0.553	0.570
Activating RAS (positive vs. negative)	0.228	0.327
NPMc (positive vs. negative)	0.753	0.517

FLT3, FMS-like tyrosine kinase 3; IDH1, isocitrate dehydrogenase (NADP+) 1, cytosolic; RAS, reticular activating system; NPMc, nucleophosmin mutation.

Figure 5

Kaplan-Meier survival curves of age and prognosis. (A) Survival curves for low-risk patients aged below the median age (black curve) and above the median age (red curve). (B) Survival curves for high-risk patients aged below the median age (blue curve) and above the median age (purple curve). (C) Survival curves for all groups. L, low-risk; H, high-risk.

Figure 6

Risk score, overall survival and the gene expression values. (A) Training set (a) risk score, (b) overall survival and (c) expression values of six signature genes. (B) Validation set (a) risk score, (b) overall survival and (c) expression values of six signature genes. The abscissa values in Aa and b, and Ba and b indicate the sample number after sorting of the risk score from low to high. In Ab and Bb, the orange spots represent samples from deceased patients and the black spots represent samples from living patients.

Functional enrichment analysis of genes associated with different prognoses

The DEGs of the high-risk group and low-risk group in the training dataset were screened using LIMMA (15). A total of 309 DEGs were obtained with the criterion of FDR<0.05.

Following correlation analysis between the DEGs and risk value, 111 and 198 DEGs were obtained with expression levels associated with negative or positive risk, respectively. GO function and KEGG pathway enrichment analysis of these DEGs were performed and the results are shown in Fig. 7. The downregulated genes were significantly enriched into 10 GO terms, predominantly associated with cell defense and immune response, whereas the upregu-lated genes were significantly enriched into 12 GO terms, predominantly associated with morphogenesis and development (Fig. 7A). With regard to the KEGG pathways, as no significant pathways were enriched for the upregulated and downregulated genes, respectively, all these genes were combined to show the KEGG pathway enrichment results. These genes were predominantly enriched in eight KEGG pathways: Systemic lupus erythematosus, type 2 diabetes mellitus, regulation of actin cytoskeleton, hematopoietic cell lineage, complement and coagulation cascades, extracellular matrix receptor interaction, focal adhesion, and galactose metabolism (Fig. 7B).

Figure 7

Functional enrichment analysis of top 20 genes with significant positive and negative correlations. (A) GO function analysis of genes with significantly downregulated and upregulated expression; (B) Kyoto Encyclopedia of Genes and Genomes pathway analysis. GO, Gene Ontology.

Discussion

Previous studies have identified several prognostic indicators for AML, including age and cytogenetic findings (6). With the development of molecular biology, genetics, and blood cell disease detection technology, increasing factors associated with AML pathogenesis and prognosis have been found, including cell karyotype, and gene mutation and expression (10). In the present study, the large quantities of mRNA-seq data on patients with AML published in TCGA database were used to screen out the significant DEGs associated with AML. As a large-scale cancer genomics project, TCGA database contains substantial cancer genomics data from multiple technical platforms (18). The data are important to cancer research and several studies have demonstrated the value of analyzing networks based on TCGA database (19,20). The present study was performed using 141 patients with AML in the training dataset and 163 patients with AML in the validation dataset. The two datasets provided survival information. According to the survival rates, of the 141 patients with AML in the training dataset, 55 poor prognosis samples were distinguished from 27 good prognosis samples. The aberrant expression of certain specific genes associated with hematopoiesis, bone marrow differentiation, and immune stress can significantly affect the chemotherapeutic effects on and the prognosis of AML, and can become preferred potential candidate genes for investigations, providing assistance in revealing the pathogenesis of AML. In the present study, a total of 206 significantly DEGs were screened out between the poor prognosis group and the good prognosis group. Following univariate Cox regression analysis and multivariate Cox regression analysis, a total of 14 genes significantly associated with prognosis were screened. Finally, six genes (TREML2, SLC7A11, NLRP2, DDIT4, LSP and CLEC11A) were used to establish the model equations for risk assessment. As previous prognostic scoring systems have mainly been based on age, cytogenetic findings and white blood cell count, the establishment of the present prognostic scoring system based on the expression level of AML-related genes has important theoretical and clinical significance, and offers potential for practical application in preclinical and clinical trials.

The six candidate signature genes were divided into two groups according to their expression profiles. The first group included four upregulated genes, which were TREML2, SLC7A11, NLRP and DDIT4. TREM proteins are a family of cell surface receptors, which are involved in diverse cell processes, including inflammation, bone homeostasis, neurological development and coagulation (21). Reportedly, TREML is a potential susceptible gene of osteoporosis. In addition, missense mutation of TREML has a protective effect in the development of Alzheimer's disease (22,23). Based on the present study, it may be associated with the progression of AML.

SLC7A1 is a member of a heterodimeric Na+-independent anionic amino acid transport system, which mediates cysteine-glutamate exchange and thereby regulates intracellular glutathione levels (24,25). In addition, SLC7A1 controls the production of pheomelanin pigment and the proliferation of cultured cells (26), and protects cancer cells of the NCI-60 panel from chemoresistance to numerous compounds (24). The impairment of SLC7A1 can result in the disruption of glutamate homeostasis and lead to a variety of central nervous system disorders, including drug addiction, schizophrenia and neurodegenerative conditions (27). Studies have indicated that the expression of SLC7A1 is markedly increased in breast cancer cell lines and clinical samples (28), and can serve as a predictor of cellular response to L-alanosine- and glutathione-mediated resistance to geldanamycin (24). In gastric cancer, the long non-coding RNA SLC7A11-AS1 can promote tumor growth, and its decreased expression is linked with poor prognosis (29). According to the results of the present study, SLC7A1 may be important in the pathogenesis of AML, yielding a potential target for AML treatment.

NALP genes are characterized by the N-terminal pyrin domain (PYD), and are involved in the activation of caspase-1 by Toll-like receptors and in protein complexes that activate proinflammatory caspases (30). As the most well known member of the NALP gene family, NLRP has been shown to form the core of the inflammasome and respond to numerous pathogen-, danger-, and disease-associated molecular patterns (31–33). Similarly, NALP2 is crucial in inflammation through the regulation of nuclear factor-κB activity, and the PYD of NALP2 can inhibit cell proliferation and tumor growth in human glioblastoma (34). Additionally, NALP has been identified as a predictive biomarker for pregnancy following in vitr fertilization (35). However, there is no direct evidence to date that NALPs are associated with AML.

DDIT4, also known as regulated in development and DNA damage response 1 (REDD1), usually acts as a negative regulator of mechanistic target of rapamycin (mTOR), which regulates a variety of cellular functions including growth, proliferation and autophagy (36,37). Due to its effect on mTOR, which has been associated with aging and linked with diseases including tuberous sclerosis, diabetes and cancer (38), DDIT has attracted increasing interest in clinical studies. The high expression of DDIT has been considered as a prognostic marker in certain malignancies, including AML, breast cancer, and colon, skin and lung cancer (39). This finding supports the reliability of the results of the present study.

The second group included two downregulated genes, LSP and CLEC11A, which are associated with cellular immunity, hematopoiesis and the cytoskeleton. LSP was originally reported as a lymphocyte‑specific actin‑binding protein in murine lymphocytes (40) and was subsequently found in all hematopoietic cells (41). LSP has been reported to regulate cell biology in several types of human cancer, including lymphomas (42), pancreatic cancer (43), breast cancer (44), dermatofibroma (45) and hepatocellular carcinoma (HCC) (46). However, the functions of LSP in AML remain to be elucidated. It has been reported that LSP is downregulated in breast cancer and in patients with HCC, and is considered a risk factor for these two types of cancer (44,46,47). On the basis of previous findings, the downregulation of LSP in the poor prognosis group in the present study indicated that LSP may serve as a prognostic marker and a potential therapeutic target in AML.

CLEC11A, a secreted sulfated glycoprotein expressed in the bone marrow and skeletal tissues, can promote colony formation by human hematopoietic progenitors in culture and assist in maintenance of the adult skeleton (48–51). Previously, CLEC11 was identified as a biomarker for predicting colorectal cancer (52). A previous study confirmed the central role of CLEC11 as a potential regulator of multiple myeloma SET protein in multiple myeloma cell survival and regulation (53). In addition, the plasma level of CLEC11 has been associated with hemoglobin levels and was found to be increased in patients following bone marrow transplantation (54,55). Therefore, it has been considered as a hematopoietic growth factor and novel drug target for myeloma. However, the physiological function of CLEC11 in AML has not been reported.

As SLC7A11, NLRP2, DDIT and LSP have previously been reported to be associated with cancer, it was hypothesized that the six candidate signature genes identified in the present study may be novel factors associated with AML. A correlation analysis between the risk assessment model and clinical features was performed and the results showed that both the risk score and age were prognostic factors, and that age was significantly associated with prognosis under the same risk conditions. The reliability of the model equations for risk assessment was further validated in an independent validation dataset. These investigations aimed to provide an effective tool for the clinical diagnosis of AML, which may assist in elucidating the possible pathogenesis of AML.

Functional annotations of the significant DEGs according to the GO and KEGG databases can provide numerous candidate genes and more information on the pathogenesis of AML. In the present study, GO function analysis of DEGs was performed. The GO terms of the significantly downregulated genes were mainly associated with cell defense and immune response, whereas the GO terms of the significantly upregulated genes were mainly associated with morphogenesis and development, indicating that the immune defense system of the organism was suppressed, with the abnormal amplification of cancer cells predominant. This finding was consistent with the characterization of AML (1). Subsequently, KEGG pathway analysis was performed on the 20 significant characteristic factors, and the results showed that these genes were mainly involved in eight KEGG pathways. The top three significant KEGG pathways were hematopoietic cell lineage, focal adhesion, and regulation of actin cytoskeleton, which are all associated with the abnormal amplification of hemocytes. For example, the 'hematopoietic cell lineage' pathway is important in the processes of hematopoiesis and immune response (56), whereas the 'focal adhesion' pathway is associated with another blood disease, macrothrombocytopenia (57). The results of the present study may provide clues for further clarifying the pathogenesis of AML. However, there were several limitations in the present study. For example, the predictive capability of the model has not been confirmed by direct experiments. In addition, the expression of the six important genes (TREML2, SLC7A11, NALP., DDIT4, LSP and CLEC11A) and their functions in AML require further validation in experiments in vitr and in vivo. Additionally, the survival rates of different individuals in the two datasets were different, which may influence the accuracy of the analysis. Therefore, further analyses are required to elucidate the mechanisms underlying the processes of tumorigenesis and the development of AML.

In conclusion, the present study provided a credible risk assessment model for AML prognosis based on a comprehensive bioinformatics analysis of six candidate genes using data from two independent datasets. All six genes were significantly associated with the diagnosis of AML and may be potential prognostic biomarkers.