Open Access

Bioinformatic analysis of the effects and mechanisms of decitabine and cytarabine on acute myeloid leukemia

  • Authors:
    • Shiyong Zhou
    • Pengfei Liu
    • Huilai Zhang
  • View Affiliations

  • Published online on: May 12, 2017     https://doi.org/10.3892/mmr.2017.6581
  • Pages: 281-287
  • Copyright: © Zhou et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Acute myeloid leukemia (AML) is a frequently occurring malignant disease of the blood and may result from a variety of genetic disorders. The present study aimed to identify the underlying mechanisms associated with the therapeutic effects of decitabine and cytarabine on AML, using microarray analysis. The microarray datasets GSE40442 and GSE40870 were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) and differentially methylated sites were identified in AML cells treated with decitabine compared with those treated with cytarabine via the Linear Models for Microarray Data package, following data pre‑processing. Gene Ontology (GO) analysis of DEGs was performed using the Database for Annotation, Visualization and Integrated Analysis Discovery. Genes corresponding to the differentially methylated sites were obtained using the annotation package of the methylation microarray platform. The overlapping genes were identified, which exhibited the opposite variation trend between gene expression and DNA methylation. Important transcription factor (TF)‑gene pairs were screened out, and a regulated network subsequently constructed. A total of 190 DEGs and 540 differentially methylated sites were identified in AML cells treated with decitabine compared with those treated with cytarabine. A total of 36 GO terms of DEGs were enriched, including nucleosomes, protein‑DNA complexes and the nucleosome assembly. The 540 differentially methylated sites were located on 240 genes, including the acid‑repeat containing protein (ACRC) gene that was additionally differentially expressed. In addition, 60 TF pairs and overlapped methylated sites, and 140 TF‑pairs and DEGs were screened out. The regulated network included 68 nodes and 140 TF‑gene pairs. The present study identified various genes including ACRC and proliferating cell nuclear antigen, in addition to various TFs, including TATA‑box binding protein associated factor 1 and CCCTC‑binding factor, which may be potential therapeutic targets of AML.

Introduction

Acute myeloid leukemia (AML) is one of the most frequently occurring malignant diseases of the blood and patients of all ages may present with symptoms. It has previously been reported that AML in children accounts for 25% of pediatric leukemia cases and affects ~180 patients annually in Japan (1). A total of 19,000 cases of AML are diagnosed each year, with ~10,000 of these in the United States (2). Outcomes have improved in younger patients, with a 40–50% 5-year overall survival rate (3). However, the majority of AML cases occur in adults, and in these cases the mortality rate remains high. It has been demonstrated that only 10–20% of patients aged >60 years survive to 5 years; 80% of patients are incurable as a result of primary refractoriness, relapse or treatment-associated mortality (4,5). AML has several subtypes and treatment and prognosis varies among them. AML is treated traditionally with chemotherapy and recent genetic research has provided more personalized treatment options. Clinicians can now predict which drug or drugs may work best for a particular person, and how long that person is likely to survive. Furthermore, numerous studies have reported that various genetic abnormalities in the following genes: Nucleophosmin 1, runt related transcription factor 1, Tet methylcytosine dioxygenase 2 and isocitrate dehydrogenase [NADP(+)] 1 cytosolic, are associated with the occurrence, progression and recurrence of AML and may therefore be used to predict prognosis and guide future therapeutic research (69). Döhner et al (4) summarized the frequency and clinical significance of various important mutated genes. The primary first line therapeutic for the treatment of AML is combined chemotherapy with anthracycline and cytarabine (10). Further therapeutic options include the hypomethylating agents decitabine and azacitidine) low-dose cytarabine, investigational agents, and supportive care with hydroxyurea and transfusions (11). Decitabine is a deoxynucleoside analogue of cytidine that selectively inhibits DNA methyltransferases. It is considered an effective and well-tolerated alternative treatment to cytarabine or supportive care in older patients with AML (12). To improve the efficacy and structure of decitabine, the present study examined the mechanism underlying the effects of decitabine and cytarabine on AML, via microarray analysis. Although some progress has been made in targeted therapy of AML, the diagnosis and treatment of it remain challenging. The present study identified additional biomarkers associated with the therapeutic effects of drugs, in order to explore the corresponding mechanisms.

Materials and methods

Microarray data

The microarray datasets GSE40442 (13) and GSE40870 (13) were downloaded from the Gene Expression Omnibus (GEO) database (www.ncbi.nlm.nih.gov/geo/). The expression profile of GSE40442 contained 67 primary AML samples cultured with medium only for 1 day, followed by 3 days treatment with decitabine (the case group; n=17), cytarabine (the control group; n=16), dimethyl sulfoxide (DMSO; n=17) or untreated (n=17). These data were identified via the GPL5188 [HuEx-1_0-st] Affymetrix Human Exon 1.0 ST Array [probe set (exon) version] platform. The GSE40870 profile presented the methylation data of AML cell samples treated with decitabine (the case group; n=16), cytarabine (the control group; n=16) or DMSO (n=16). Detection of the methylation data was performed via GPL13534 Illumina HumanMethylation450 BeadChip (HumanMethylation 450_15017482).

Data preprocessing

To create the expression profile, the original data were converted into a recognizable format in R, and the affy (14) package (bioconductor.org/ packages/release/bioc/html/affy.html) was used for background correction and normalization, followed by conversion from the probe symbol to the gene symbol with the biomaRt (15) package of R (bioconductor.org/packages/release/bioc/html/biomaRt.html). The β-value of every methylated site in all samples was extracted via GenomeStudio software version 2.0 (Illumina, Inc., San Diego, CA, USA) to create a methylation profile.

Identification of differentially expressed genes (DEGs) and differentially methylated sites

For GSE40442, the Linear Models for Microarray Data (16) package of R (bioconductor.org/packages/release/bioc/html/limma.html) was used to identify the DEGs in AML cells treated with decitabine compared with those treated with cytarabine. The DEGs were identified according to the criteria P<0.05 and log(fold-change)>0.5. The heatmap of DEGs in every sample of the control and the case group was constructed. For GSE40870, the differentially methylated sites were identified in AML cells treated with decitabine compared with cytarabine via the Illumina Methylation Analyzer (17) package of R (ima.r-forge.r-project.org/), and were screened out with the criteria P<0.05 and log(fold-change)>0.2.

Functional enrichment analysis

Gene Ontology (GO) enrichment analysis of DEGs was performed via the Database for Annotation, Visualization and Integrated Discovery (david.abcc.ncifcrf.gov/) (18) with the threshold of P<0.05.

Screening of important genes and methylated sites

Genes corresponding to the differentially methylated sites were obtained by the annotation package of the methylation microarray platform. The genes that exhibited an overlap compared with DEGs were selected, and those that exhibited the opposite trend in the methylation variation compared with their expression were screened out.

Identification and analysis of important transcription factor (TF)-gene pairs and establishment of TF-gene regulated network

Methylation in the gene promoter region may affect the binding of TFs to genes and result in the variation of gene expression. Firstly, chromosomal locations of the methylation sites were identified using the annotation package of the methylation microarray platform. Following this, chromosomal locations of all the known and predicted TF binding sites were downloaded from the University of California Santa Cruz (UCSC) database (19) (genome.ucsc.edu/). The methylation sites were considered to affect the binding of TFs and genes when the chromosomal location of the methylation sites overlapped with the region of the TF binding site. Furthermore, all the known and predicted TF-gene pairs were downloaded from UCSC and the TF-gene pairs were screened out. The TF-gene regulated network was established via Cytoscape version 3.11 (www.cytoscape.org/).

Results

DEGs and differentially methylated sites

A total of 190 DEGs (102 up- and 88 downregulated) and 540 differentially methylated sites were identified in AML cells treated with decitabine compared with cytarabine, and all the identified differentially methylated sites were hypomethylated. The top 30 DEGs and the top 30 differentially methylated sites are presented in Tables I and II, respectively, and the heatmap of DEGs is presented in Fig. 1.

Table I.

Top 30 differentially expressed genes in acute myeloid leukemia cells treated with decitabine compared with those treated with cytarabine.

Table I.

Top 30 differentially expressed genes in acute myeloid leukemia cells treated with decitabine compared with those treated with cytarabine.

GeneLog(fold-change)P-value
PNMA51.008734 4.61×10−7
COL14A10.981479 7.39×10−6
LINC013440.688924 1.12×10−5
PPP1R270.991804 1.52×10−5
ACRC0.794564 1.99×10−5
TKTL11.047284 2.91×10−5
DAZL0.5044240.000131
RBMY3AP−0.506650.000302
MIR6750.5981420.000474
MYBL2−0.621180.000611
BNIP3P90.5057330.00071
HIST1H1C0.5181460.000896
TK1−0.631390.000925
HIST1H1E0.6127660.001018
PCNA−0.526070.001235
TRAJ13−0.965230.001403
RN7SKP600.6407810.001425
CDKN1A−0.617490.0015
HMGN50.5750890.00238
NFE41.0695620.002412
YPEL5P10.8693790.002471
FAM111B−0.863190.002552
HIGD1AP80.6929030.002888
OR2L30.9132120.003109
OR52P2P−1.041220.003232
MDM2−0.546660.003275
GACAT20.6920880.003317
CCT4P2−0.722330.003713
HIST1H1T0.7615000.003745
TMEM261P1−0.520410.003776

Table II.

Top 30 differentially methylated sites in AML cells treated with decitabine compared with those treated with cytarabine.

Table II.

Top 30 differentially methylated sites in AML cells treated with decitabine compared with those treated with cytarabine.

Methylation Log(fold-change)P-value
cg22040989−0.46477 7.71×10−27
cg19098118−0.32461 3.17×10−22
cg14063817−0.34161 1.51×10−19
cg17631454−0.33468 5.13×10−19
cg02597199−0.32804 1.82×10−18
cg08071595−0.35059 3.15×10−18
cg27576136−0.20948 1.73×10−17
cg09374462−0.25800 1.77×10−17
cg12442125−0.35154 4.70×10−17
cg05592278−0.31725 4.70×10−17
cg27052900−0.24157 4.70×10−17
cg22802167−0.26297 5.69×10−17
cg08550094−0.35068 6.61×10−17
cg21486341−0.20684 6.79×10−17
cg08411833−0.27190 8.08×10−17
cg17806847−0.25718 8.08×10−17
cg03865944−0.20673 9.44×10−17
cg03611733−0.21888 9.80×10−17
cg12091641−0.31272 1.43×10−16
cg17338368−0.24704 2.00×10−16
cg23641672−0.21018 2.87×10−16
cg23836413−0.20551 2.87×10−16
cg05073880−0.22513 3.63×10−16
cg12866103−0.29918 5.32×10−16
cg03282689−0.25659 6.36×10−16
cg07042346−0.24842 8.04×10−16
cg09014775−0.21864 8.09×10−16
cg05303739−0.20861 1.05×10−15
cg11017535−0.24066 1.51×10−15
cg13987334−0.23196 1.51×10−15
Enriched GO terms of the DEGs

A total of 36 enriched GO terms of DEGs, including nucleosome, protein-DNA complex, and nucleosome, chromatin and protein-DNA complex assemblies, were obtained and are presented in Table III.

Table III.

Enriched GO terms of differentially expressed genes.

Table III.

Enriched GO terms of differentially expressed genes.

CategoryGO IDGO nameP-value
CC0000786Nucleosome 1.03×10−7
CC0032993Protein-DNA complex 6.69×10−7
BP0006334Nucleosome assembly 8.70×10−7
BP0031497Chromatin assembly 1.07×10−6
BP0065004Protein-DNA complex assembly 1.40×10−6
BP0034728Nucleosome organization 1.59×10−6
BP0006323DNA packaging 6.06×10−6
CC0000785Chromatin 7.65×10−6
BP0006333Chromatin assembly or disassembly 9.73×10−6
CC0005694Chromosome 3.77×10−5
CC0044427Chromosomal part 7.21×10−5
BP0016584Nucleosome positioning 2.24×10−4
BP0065003Macromolecular complex assembly0.001008
BP0034622Cellular macromolecular complex assembly0.001458
CC0031012Extracellular matrix0.001562
BP0043933Macromolecular complex subunit organization0.001593
BP0034621Cellular macromolecular complex subunit organization0.002613
BP0006259DNA metabolic process0.003411
BP0006325Chromatin organization0.003467
CC0005654Nucleoplasm0.004301
CC0005578Proteinaceous extracellular matrix0.006181
BP0006260DNA replication0.006457
CC0044421Extracellular region part0.007453
BP0051276Chromosome organization0.011355
MF0003677DNA binding0.012103
BP0006974Response to DNA damage stimulus0.015069
BP0030162Regulation of proteolysis0.018269
BP0033554Cellular response to stress0.022660
BP0006281DNA repair0.024911
MF0005125Cytokine activity0.024942
BP0032026Response to magnesium ion0.030924
CC0031981Nuclear lumen0.035330
BP0046685Response to arsenic0.038507
CC0000307Cyclin-dependent protein kinase holoenzyme complex0.039727
BP0051726Regulation of cell cycle0.040353
MF0004984Olfactory receptor activity0.049749

[i] GO, Gene Ontology; CC, cellular component; BP, biological process; MF, molecular function.

Important genes and methylated sites

A total of 240 genes were screened, in which 540 differentially methylated sites were identified. These 240 genes were compared with the 190 DEGs, and the acid-repeat containing protein (ACRC) exhibited an overlap. Furthermore, ACRC corresponded to the methylated site of cg26924445 and demonstrated the opposite trend in the methylation variation compared with gene expression.

Important TF-gene pairs and the TF-gene regulated network

A total of 60 TF-gene pairs and overlapped methylated sites were screened out, including cg22475974-SET domain bifurcated (SETDB)1, cg14063817-estrogen receptor (ERA, cg22475974-ERα A, cg05835309-SETDB1 and cg00334293-signal transducer and activator of transcription-3. In addition, 140 pairs of TFs and DEGs were identified, including CCAAT/enhancer binding protein β (CEBPB)-cysteine rich secretory protein 3, CEBPB-C-X-C motif chemokine ligand 2, CEBPB-fanconi anemia complementation group I, CEBPB-histone cluster 1 H1 family member C and CEBPB-microRNA 378e. In addition, the 60 pairs of TFs and overlapped methylated sites contained 20 TFs and 51 methylated sites. The TF-gene regulated network was established according to the 140 TF-gene pairs (Fig. 2). A total of 68 nodes and 140 pairs were involved in the regulated network. Furthermore, the 68 nodes contained 18 TFs (presented as triangles) and 50 genes (presented as circles). The top 20 nodes according to connections with other nodes in the network are presented in Table IV.

Table IV.

Top 20 most significant nodes according to degree.

Table IV.

Top 20 most significant nodes according to degree.

NodeDegree
TAF136
CTCF25
C-MYC14
FOXA110
RAD219
CEBPB8
PCNA8
HEY16
NRSF6
FANCI6
PU.15
STAT35
CXCL25
THBS15
HIST1H4H5
NAMPT5
RPL36AL5
HIST1H1E5
GABP4
CCNE24

[i] Degree, connection with other nodes.

Discussion

Decitabine (Dacogen®; 5-aza-2′-deoxycytidine) has been extensively used for the treatment of AML as an inhibitor of DNA methylation, which triggers demethylation leading to consecutive reactivation of epigenetically silenced tumor suppressor genes (20). When administered at low doses, decitabine may reduce genomic DNA methylation as a consequence of irreversible binding to DNA methyltransferases following incorporation into newly synthesized DNA (21). Cytarabine inhibits DNA synthesis by suppressing DNA polymerase activity; however, it additionally inhibits the elongation of the polynucleotide chain and interferes with the physiological function of DNA, which is important for the treatment of hematological malignancies (22,23). In the present study, all the identified differentially methylated sites were hypomethylated in the primary AML samples treated with low-dose decitabine compared with cytarabine, which is consistent with differing underlying mechanisms of decitabine and cytarabine in AML. The AML cell samples treated with decitabine differed from those treated with cytarabine in the heatmap of DEGs.

In the present study, a total of 36 GO terms enriched in DEGs were obtained. They were primarily associated with the combination of protein and DNA, (protein-DNA complex and protein-DNA complex assembly), chromosome conformation (chromatin assembly or disassembly, chromosomal part and chromatin organization), and biological processes associated with the assembly of macromolecular complexes (nucleosome assembly, protein-DNA complex assembly, macromolecular complex assembly and cellular macromolecular complex assembly). It has previously been demonstrated that DNA methylation is important in the biological processes of genomic imprinting, X-chromosome inactivation, suppression of transposable elements and carcinogenesis (2428). DNA methylation is considered a potent epigenetic modification and may inhibit TF recruitment, resulting in suppression of transcription (24,29), and closely associates with health and disease in humans (30,31). Furthermore, it has been previously reported that DNA methylation is an epigenetic activity that affects the structure of chromosomes, but not the sequence of genes (3234). Therefore, the aforementioned data demonstrated that decitabine may affect AML via gene methylation.

Of the identified DEGs, ACRC was the only one to additionally contain a differentially methylated site, cg26924445, and demonstrated an opposite trend in methylation variation compared with expression. Nestheide et al (35) suggested that ACRC is an important biomarker of Ewing sarcoma and concludes that epigenetic dysregulation may contribute to the pathogenesis of angiosarcoma, via analysis of expression and methylation profiles. The results of the present study demonstrated that decitabine can alter the methylation status of cg26924445, and that as in their study, increasing expression of ACRC was conducive to treating AML. Therefore, it was suspected that decitabine might treat AML through altering the methylation status of cg26924445 in ACRC. The results of the present study revealed that the TATA-box binding protein associated factor 1 (TAF1) regulated the most genes or TFs in the TF-gene regulated network. Therefore, TAF1 may act as a critical TF for decitabine treatment of AML, and may be important in the differing underlying molecular mechanisms of decitabine and cytarabine. Ben Abdelali et al (36) reported that the SET-NUP214 (TAF1/CAN) fusion gene is an important influencing factor in the survival rate of AML. Therefore, TAF1 may be a potential novel target gene in decitabine-treated AML. The CCCTC-binding factor (CTCF) was another TF that regulated numerous genes or TFs. Manodoro et al (37) demonstrated that in AML, the methylation of CTCF binding sites may result in loss of imprinting at 14q32. Furthermore, the present study demonstrated that proliferating cell nuclear antigen (PCNA) was the gene regulated by the greatest number of TFs, and Buchi et al (38) reported that the expression of PCNA was altered in AML treated with decitabine or cytarabine.

In conclusion, the results of the present study suggested that decitabine suppresses the function of certain antitumor genes via methylation, in its role as a therapeutic agent for the treatment of AML, and that this underlying mechanism of action differs to that of cytarabine. The present study provides information regarding potential drug targets, which may improve the efficacy of decitabine in the treatment of AML.

Acknowledgements

The present study was supported by the Health Bureau Science and Technology Foundation of Tianjin (grant no. 2012KZ063) and the Municipal Science and Technology Commission of Tianjin (grant no. 15ZLZLZF00440).

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Zhou S, Liu P and Zhang H: Bioinformatic analysis of the effects and mechanisms of decitabine and cytarabine on acute myeloid leukemia. Mol Med Rep 16: 281-287, 2017.
APA
Zhou, S., Liu, P., & Zhang, H. (2017). Bioinformatic analysis of the effects and mechanisms of decitabine and cytarabine on acute myeloid leukemia. Molecular Medicine Reports, 16, 281-287. https://doi.org/10.3892/mmr.2017.6581
MLA
Zhou, S., Liu, P., Zhang, H."Bioinformatic analysis of the effects and mechanisms of decitabine and cytarabine on acute myeloid leukemia". Molecular Medicine Reports 16.1 (2017): 281-287.
Chicago
Zhou, S., Liu, P., Zhang, H."Bioinformatic analysis of the effects and mechanisms of decitabine and cytarabine on acute myeloid leukemia". Molecular Medicine Reports 16, no. 1 (2017): 281-287. https://doi.org/10.3892/mmr.2017.6581