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Clinical Medicine Insights 
 

DOI:    https://doi.org/10.52845/CMI/2023-4-1-1 

CMI 04 (01), 363-371 (2023)                                                                                                                                                   
ISSN (O) 2694-4626

 

RESEARCH ARTICLE                                                                                                    

Identification The Hub Genes In HL-60 Leukemia Cells On Decitabine 

Through Bioinformatics Analysis 

Fuxue Meng*, Lonakuan Li, Qin Zheng 

Medical Experiment Center, the Third Affiliated Hospital of Guizhou Medical University, Duyun 

558000, Guizhou Province, China. 

  Corresponding Author: Fuxue Meng 

  

Introduction

Acute myeloid leukemia (AML) is a malignant clonal proliferative disease derived from hematopoietic stem 

cells, and its 5-year survival rate is very low. In the past few decades, the development of sequencing 

technology has resulted in the accumulation of a large number of omics data for various complex diseases. 

The subsequent development of bioinformatics revealed to us more rapid and intuitive multiple gene 

expression patterns
[1;2]

. 

Decitabine, as a specific inhibitor of DNA methyltransferase (DNMT), can be incorporated into DNA during 

the replication process, irreversibly or covalently bound to DNMT, depleting the storage of DNMT in the 

cell, and making DNA progressive demethylation. Decitabine can also enhance the body's anti-tumor 

immunity through non-methylated anti-tumor effects, stimulate the expression of tumor-associated antigens, 

or change the cellular immune status by regulating regulatory T cells; it can be mediated by P53 damage 

repair pathway promotes apoptosis of leukemia cells
[3;4]

. Study
[5] 

have shown that compared with the 

Abstract 

Objective: Aims to identification the hub genes in HL-60 acute myeloid leukemia cells treated with 

decitabine, provide meaningful insights into the pathogenesis of AML. Methods: The gene expression 

profile of GSE24224 was obtained from GEO database, GO and pathway enrichment were performed 

though DAVID, established a PPI network from the STRING database and was displayed through the 

weight network diagram of omicShare tools. MiRNA targets and RBP targets prediction were carried out to 

further unravel the functions of hub genes identified. Results: 1558 differentially expressed genes were 

identified. GO and pathway analyses mainly involved citrulline metabolic process, positive regulation of 

neutrophil extravasation, positive regulation of cell adhesion molecule production, phospholipase A2 

inhibitor activity, hematopoietic cell lineage, TGF-β signaling pathway, p53 signaling pathway. The miRNA 

and RBP targets prediction hinted that the 10 hub genes identified plays an important role in prognosis of 

AML. Conclusion: This study intimated that hub genes C3, C3AR1, FPR2, GNA11, PTAFR, ITGAM, 

ANXA1, LPAR1 CXCR4 and FPR1 identified predictively targeted hsa-miR-520d-5p, hsa-miR-362-5p, 

hsa-miR-224-5p, hsa-miR-1913, hsa-miR-196b-5, hsa-miR-188-5p, hsa-miR-130a-5p, hsa-miR-204-5p, 

hsa-miR-211-5p, hsa-miR-671-5p, hsa-miR-296-3p and hsa-miR-23b-3p and ADAR1, DGCR8, DKC1, 

ELAVL1, FBL, FUS, HNRNPC, IGF2BP2, NOP58, TAF15, U2AF2 and UPF1 proteins may regulate the 

occurrence and development of AML. This study provides meaningful insights and ideas for further 

understanding the pathogenesis of AML. 

Key words: acute myeloid leukemia; hub genes; HL-60; bioinformatics analysis; decitabine 

Copyright : © 2021 The Authors. Published by Medical Editor and Educational Research Publishers Ltd. 

This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/lic enses/by-

nc-nd/4.0/). 

https://creativecommons.org/lic%20enses/by-nc-nd/4.0/
https://creativecommons.org/lic%20enses/by-nc-nd/4.0/


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simulated control group, there are significant differences in gene expression in the HL-60 cellS group treated 

with decitabine, which plays a key role in the function of HL-60 cells, but the specific mechanism is still 

unclear. 

Therefore, this study utilized bioinformatics analysis methods to analyze the HL-60 cell gene chip that was 

acted by decitabine, in order to discover biomarkers related to the disease, and provide a theoretical basis for 

revealing the molecular mechanism of the occurrence and development of AML. 

Methods 

Microarray data collection and DEG screening 
The publicly accessible data GSE24224 was obtained from the Gene Expression Omnibus 

(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc =GSE24224) that deposited by Fabiani et al
[5]

. 

Containing six samples (three decitabine treated HL-60 cells and three mock treated HL-60 cells) were 

utilized in the present study. Gene expression profiling (GEP) was performed using the Affymetrix U133 

Plus 2.0 expression array. All hybridization reactions were performed using GeneChip Fluidics Station 450, 

and GeneChips were scanned using the Affymetrix GeneChip Scanner 3000 (Affymetrix, USA). The raw 

data of GSE24224 was processed using the Affy package pair in R, using correction, normalization and 

log2 conversion
[6]

. The differentially expressed genes (DEGs) in decitabine treated HL-60 cells compared 

with mock treated HL-60 cells were determined using limma package
[7;8]

. DEGs were screened with a false 

discovery rate (FDR) corrected P<0.05 and |log fold-change (FC)|>1, then were confirmed using the 

GEO2R application from GEO.  

Functional enrichment analysis 
Investigation into the functions of enriched DEGs may improve understanding of their involvement in AML. 

In the present study, functional enrichment analysis of DEGs based on DAVID 

(https://david.ncifcrf.gov/tools.jsp), a widely used web-based genomic functional annotation tool. DEGs 

were subjected to molecular function and pathway studies by Gene Ontology (GO) analysis and Kyoto 

Encyclopedia of Genes and Genomes (KEGG) pathway analysis. P < 0.05 was set as cutoff values. 

Protein-protein interaction (PPI) network construction and Hub genes identification 
Genes are likely to function together rather than alone in complex diseases. Hub nodes in the network may 

represent key role. In the present study, protein-protein interaction (PPI) network analysis was performed to 

investigate the DEGs based on the STRING online database (https://string-db.org/cgi/input.pl). The 

minimum required interaction score was set as medium confidence > 0.8. In addition, the network was 

constructed through the weight network diagram of omicShare tools 

(https://www.omicshare.com/tools/index.php/) to confirm the hub genes and the genes with top-ten highest-

weight nodes were defined as hub genes.  

miRNA target prediction 

In this study, the targets of the hub genes were predicted using four databases: miRWalk 

(http://mirwalk.umm.uni-heidelberg.de/), TargetScan (http://www. targetscan.org), miRMap 

(https://mirmap.ezlab.org) and starBase v2.0 (http://starbase.sysu.edu.cn/). The screening criterion was that 

the miRNA target exists in the four databases concurrently. The Venny 2.1 Online Tool 

(https://bioinfogp.cnb.csic.es/tools/venny/) was used to find overlapping genes between DEGs and 

predictive genes of DEMs. The miRNA-gene regulatory network was depicted and visualized using 

Cytoscape 3.7.1 (https://cytoscape.org/). 

RNA binding protein (RBP) target prediction 
As the core position of the post-transcriptional regulatory network, RNA binding protein (RBP) participates 

in multiple processes of RNA processing, including alternative splicing, RNA transport and stability 

maintenance, RNA localization, and mRNA translation 
[9]

. In order to further understand the function of the 

hub gene, we used the RBP-mRNA module on the starBase v2.0 software to performed RBP prediction. 

Results 

Differentially expressed gene acquisition 

By the cutoff of a 1-fold change and P < 0.05, 1592 differentially expressed were identified filtered out. 

After removing the genes without gene symbol, 1558 differentially expressed genes remain, of which 1368 

were up-regulated genes and 190 were down-regulated genes. The heatmap with clustering of differentially 



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expressed gene were illustrated, and volcano plots were generated to demonstrate the distribution of the 

differentially expressed gene ( Figure 1 ).   

 

 
Figure 1 Heatmap and volcano plots showing the difffferential expression genes between decitabine 

treated HL-60 cells and group. A: The Heatmap of difffferential expression genes; B: the volcano plots 

difffferential expression genes (|log 2 FoldChange| ≧ 1 and P value < 0.05). 

 

GO and pathway analysis 

The GO functional enrichment resulted in DEGs 

mapped to 198 GO terms. With P<0.05 as the 

significant enrichment criterion, top ten of 

significant enriched functional clusters were 

screened. Of them, the DEGs GO enrichment 

mainly involved citrulline metabolic process, 

positive regulation of neutrophil extravasation, 

positive regulation of cell adhesion molecule 

production, phospholipase A2 inhibitor activity, 

MRF binding, phagolysosome, synaptonemal 

complex. KEGG enrichment analysis with P<0.05 

was used as an enrichment screening standard, 

involved such as staphylococcus aureus infection, 

hematopoietic cell lineage, TGF-beta signaling 

pathway, p53 signaling pathway ( Figure 2 ) . 

 

 
Figure 2 GO and KEGG analysis of DEGs. A: KEGG enrichment histogram analysis of DEGs ; B: 

KEGG enrichment bubble chart of DEGs; C: GO enrichment histogram analysis of DEGs; D: GO  

enrichment bubble chart of DEG (P<0.05). 

PPI network analysis 

To screen and identification the hub genes in HL-

60 cells, we established a PPI network from the  

 

STRING database with scores of > 0.8. Then, the 

network was constructed through the weight 



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CMI  JOURNAL                                                                                                       Fuxue Meng et al. 

 

 
 

network diagram of omicShare tools to confirm 

the hub genes and the genes with top-ten highest-

weight node of DEGs such as C3, C3AR1, FPR2, 

GNA11, PTAFR, ITGAM, ANXA1, LPAR1 

CXCR4 and FPR1 were defined as hub genes 

( Figure 3 ). 

 

 
Figure 3 PPI network construction and hub genes determination. B: PPI network of DEGs; A,C,D: 

the weight network of DEGs, C3, C3AR1, FPR2, GNA11, PTAFR, ITGAM, ANXA1, LPAR1 CXCR4 

and FPR1 identified as the hub genes (scores > 0.8 ). 

 

MiRNA target prediction 

For further understand the functions of the hub 

genes, the miRNA target prediction were carried 

out through four databases: miRWalk, 

TargetScan, miRMap and starBase v2.0. The C3 

and FRP1 genes only have overlapping miRNA 

targets in three of the databases, and the remaining 

eight hub genes existed overlapping miRNA 

targets in the four databases, as the figure 4 

shows.

 

 



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CMI  JOURNAL                                                                                                       Fuxue Meng et al. 

 

 
 

Figure 4 miRNA target prediction of hub genes. A, B: Predict miRNA targets the wenn map of C3 and 

FRP1 in three databases that predict miRNA targets; C: Eight hub genes for predicted miRNA 

targets network; D: Network of 7 hub genes with overlapping miRNA targets. 

 

RNA binding protein (RBP) target prediction 

RNA in the cell interacts with RBP to form a 

ribonucleoprotein (RNP) complex, which plays an 

important role in RNA synthesis, transport, 

stability, translation, and cellular localization. In 

order to further understand the function of hub 

genes, we carried out the RBP prediction. The 

results show that the top 10 RBP targets predicted 

by the 8 hub genes with the strongest weight were 

ELAVL1, FUS, DKC1, HNRNPC, ADAR, 

IGF2BP2, NOP58, TAF15, UPF1, U2AF2 (Figure 

5).

 

 
Figure 5 RBP targets prediction of hub genes. A, B: The networks of RBP targets. The larger point, 

darker the color, indicating the stronger the correlation and the greater the frequency of action; C: 

Screening of RBP targets with strong correlation. 

 

Discussion 

This study obtained gene expression profiles of 

HL-60 cells that decitabine induced and mock 

control group GSE24224 from GEO database and 

carried out DEGs screening, to understand the 

biological functions biological and the enrichment 

pathways involved through GO and KEGG 

analysis. Hereafter, PPI and weighted network 

analysis were conducted to identify the hub genes 

that play a key regulatory role in HL-60 cells. 

Further performed miRNA targets prediction and 

RBP targets prediction to clarify the possible 

mechanism of the hub gene for AML. 

In this study, we screened 1558 DEGs, of which 

1368 were up-regulated and 190 were down-

regulated. Based on GO enrichment analyses, it 

was found that DEGs mainly involved molecular 

functions including hematopoietic cell lineage, 

TGF-β signaling pathway, p53 signaling pathway. 

The TGF-β signaling pathway plays a negative 

role in the regulation of cell proliferation and 

differentiation in the hematopoietic system. When 

ZFYVE16 is overexpressed, the negative 

regulation of the TGF-β signaling pathway is 

enhanced, which may inhibit the malignant 

proliferation activated by FLT3
[10]

. In addition, 

TGF-β stimulates the tumor pre-osteolytic and 

osteolytic factor production , Thereby stimulating 

further bone absorption 
[11;12]

. This classifies TGF-

β as as an important factor responsible for the 

feedforward vicious circle that drives tumor 

growth in the bones. In addition, abnormal 

reactivation of TGF-β usually leads to 

carcinogenic behavior 
[13;14]

. Their role in 

tumorigenesis usually reflects their role in 

embryonic development, but also extends to other 

features often observed in cancer, such as 

cachexia and bone loss 
[15]

. In the stage of prostate 

tumorigenesis, increased TGF-β production leads 

to the degradation of extracellular matrix, 

immunosuppression and angiogenesis, all of 

which lead to escape cell death and increase cell 

survival rate, which is conducive to the 

reproduction of cancer cells
 [16]

. The regulation of 

p53 gene activity is mainly at the post-

transcriptional level. After phosphorylation of p53 

protein is activated, it becomes p-p53, which then 



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acts by inducing cell cycle arrest and inducing 

apoptosis to inhibit tumor cell proliferation
 [17;18]

. 

After the above research and analysis, PPI 

networks were constructed by STRING, and from 

the intersection, 10 hub genes were obtained, 

which were verified and show with weighted 

network analysis. The analytic results of C3, 

C3AR1, FPR2, GNA11, PTAFR, ITGAM, 

ANXA1, LPAR1 CXCR4 and FPR1 were 

statistically significant, which suggested that these 

ten genes possibly play a key regulatory role in 

AML.  

In order to further understand the functions of ten 

hub genes, we performed miRNA targets 

prediction and RBP targets prediction. miRNA is 

an endogenous single-stranded small RNA with a 

length of 21-25 bases, which can regulate the 

expression of target genes by specifically binding 

to mRNA to degrade or inhibit protein translation 

of mRNA. Studies have shown that miRNAs are 

often located in tumorigenesis-related regions or 

fragile sites, amplification regions, heterozygous 

loss regions or breakpoint regions. The specificity 

of miRNA expression in tumor cells is specific to 

tumor occurrence, development, invasion, 

metastasis, and metastasis. It plays an important 

role in the prognosis and other processes, 

providing potential therapeutic targets or new 

strategies
[19;20]

. In this study, we selected 7 hub 

genes whose miRNA prediction targets were hsa-

miR-520d-5p, hsa-miR-362-5p, hsa-miR-224-5p, 

hsa-miR-1913, hsa-miR-196b-5，hsa-miR-188-

5p, hsa-miR-130a-5p, hsa-miR-204-5p, hsa-miR-

211-5p, hsa-miR-671-5p, hsa-miR-296-3p and 

hsa-miR-23b-3p. Li
[21]

 showed that hsa-miR-362-

3p was highly expressed as a marker in AML, and 

suggesting that it is related to the poor prognosis 

of AML patients. The prediction result of hsa-

miR-1913 in AML is consistent with the result of 

Wang
[22]

. 

RBP refers to the general term for proteins that 

directly bind to RNA. RNA in cells interacts with 

RBP to form a ribonucleoprotein (RNP) complex, 

which plays an important role in RNA synthesis, 

transport, stability, translation, and cell 

positioning
[23]

. In the current study, the hub genes 

C3, GNA11, PTAFR, ITGAM, ANXA1, LPAR1 

and CXCR4 were predicted to be strongly 

associated with proteins ADAR1, DGCR8, 

DKC1, ELAVL1, FBL, FUS, HNRNPC, 

IGF2BP2, NOP58, TAF15, U2AF2 and UPF1. 

Interestingly, Xiao
[24]

 and Peng
[25]

reported that 

ADAR1 plays an important role in acute myeloid 

leukemia. Among them, Xiao’s study demonstrate 

that ADAR1 may be involved in the regulation of 

the proliferation of AML cells partially via 

regulation of the Wnt signaling pathway
[24]

. He et 

al reported that IGF2BP2 overexpression indicates 

poor survival in patients with AML, IGF2BP2 

may serve as a biomarker to predict the prognosis 

of AML and as a potential target in AML
[26]

. 

Conclusion 

In conclusion, the present study identified a panel 

of 10 genes in AML HL-60 cells. Gene function 

and pathway investigation indicated that these 

genes were mainly engaged in citrulline metabolic 

process, positive regulation of neutrophil 

extravasation, positive regulation of cell adhesion 

molecule production, phospholipase A2 inhibitor 

activity, hematopoietic cell lineage, TGF-β 

signaling pathway, p53 signaling pathway. 

Moreover, the miRNA and RBP targets prediction 

hinted that the 10 hub genes identified plays an 

important role in prognosis of AML. Hub genes 

C3, C3AR1, FPR2, GNA11, PTAFR, ITGAM, 

ANXA1, LPAR1 CXCR4 and FPR1 predictively 

targeted hsa-miR-520d-5p, hsa-miR-362-5p, hsa-

miR-224-5p, hsa-miR-1913, hsa-miR-196b-5, 

hsa-miR-188-5p, hsa-miR-130a-5p, hsa-miR-204-

5p, hsa-miR-211-5p, hsa-miR-671-5p, hsa-miR-

296-3p and hsa-miR-23b-3p and ADAR1, 

DGCR8, DKC1, ELAVL1, FBL, FUS, HNRNPC, 

IGF2BP2, NOP58, TAF15, U2AF2 and UPF1 

proteins may regulate the occurrence and 

development of AML, but further verification is 

needed. In short, this study provides meaningful 

insights and ideas for further understanding the 

pathogenesis of AML. 

Abbreviation 

AML    Acute myeloid leukemia 

DNMT   DNA methyltransferase  

DEGs    Differentially expressed genes 

RBP     RNA binding protein 

C3AR1   Complement component 3a receptor 1 

FPR2     Formyl peptide receptor 1 



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GNA11   G protein subunit alpha 11  

PTAFR   Platelet activating factor receptor 

ITGAM   Integrin subunit alpha M 

ANXA1   Annexin A1  

LPAR1    Lysophosphatidic acid receptor 1  

CXCR4   C-X-C motif chemokine receptor 4  

FPR1     Formyl peptide receptor 1  

ELAVL1  ELAV like RNA binding protein 1 

FUS      FUS RNA binding protein  

DKC1    Dyskerin pseudouridine synthase 1  

HNRNPC  Heterogeneous nuclear 

ribonucleoprotein C 

ADAR    Adenosine deaminase, RNA specific  

IGF2BP2  Insulin like growth factor 2 mRNA 

binding protein 2  

NOP58    NOP58 ribonucleoprotein 

TAF15    TATA-box binding protein associated 

factor 15  

UPF1     UPF1, RNA helicase and ATPase 

U2AF2    U2 small nuclear RNA auxiliary factor 2  

Acknowledgements 

We acknowledge GEO database for providing 

their platforms and contributors for uploading 

their meaningful datasets. Simultaneously, we also 

appreciate to the OmicShare tool that online data 

analysis platform. 

Authors contributions 

The design, study conduct, and financial support 

for this research were provided by Meng FX, Li 

LK and Zheng Q participated in the interpretation 

of data, review, and approval of the publication. 

Funding 

This study was supported by the Guizhou 

Provincial Health Commission Science and 

Technology Project (gzwjkj2019-2-006) and the 

Qiannan Prefecture Science and Technology 

Project (Qiannankeheshezi 2018 (36)). 

Availability of data and materials 

The original data of this research come from the 

third platform GEO database (GSE24224), and 

bioinformatics analysis was carried out through 

relevant analysis tools. 

Ethics approval and consent to participate  

Not applicable.  

Consent for publication  

Not applicable.  

Competing interests 

The authors declare that they have no competing 

interests. 

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