Screening and identification of key genes in
imatinib-resistant chronic myelogenous leukemia
cells: a bioinformatics study
Hong Zhang, Peiran Wang, Ting Song, Uwituze Laura Bonnette & Zhichao
Zhang
To cite this article: Hong Zhang, Peiran Wang, Ting Song, Uwituze Laura Bonnette &
Zhichao Zhang (2021) Screening and identification of key genes in imatinib-resistant chronic
myelogenous leukemia cells: a bioinformatics study, Hematology, 26:1, 408-414, DOI:
10.1080/16078454.2021.1931740
To link to this article: https://doi.org/10.1080/16078454.2021.1931740
© 2021 The Author(s). Published by Informa
UK Limited, trading as Taylor & Francis
Group
View supplementary material
Published online: 29 May 2021. Submit your article to this journal
Article views: 795 View related articles
View Crossmark data
Screening and identification of key genes in imatinib-resistant chronic
myelogenous leukemia cells: a bioinformatics study
Hong Zhanga
, Peiran Wangb
, Ting Songb
, Uwituze Laura Bonnettea and Zhichao Zhanga
State Key Laboratory of Fine Chemicals, School of Life Science and Technology, Dalian University of Technology, Dalian, People’s
Republic of China; b
State Key Laboratory of Fine Chemicals, School of Chemistry, Dalian University of Technology, Dalian, People’s
Republic of China
ABSTRACT
Background: Chronic myelogenous leukemia (CML) is one of the most common cancers in the
world. Imatinib is one of the most effective therapeutic strategies to inhibit the BCR-ABL
tyrosine Kinase in patients with CML, but resistance is increasingly encountered.
Material and Methods: Microarray data GSE7114, GSE92624 and GSE97562 were downloaded
and analyzed from Gene Expression Omnibus (GEO) to identify the candidate genes in the
imatinib-resistant CML cells. The differentially expressed genes (DEGs) were appraised, and
the protein–protein interaction (PPI) network was created by using STRING and Cytoscape.
Results: We screened a total of 217 DEGs, including 151 upregulated genes and 66
downregulated genes. The enriched functions and pathways of genes include insulin-like
growth factor I binding, cysteine-type endopeptidase inhibitor activity involved in apoptotic
process, cell adhesion, positive regulation of nitric oxide biosynthetic process and
hematopoietic cell lineage. Nine hub genes were appraised and Gene Ontology enrichment
analysis revealed that these genes are mainly enriched in cell cycle, peptidase inhibitor
activity and cell division. Several genes such as BIRC5, CCNE2 and MCM4 were identified in
survival analysis and these genes alteration are significantly associated with worse overall
survival and disease-free survival.
Conclusions: These genes have the potential to become surrogate markers for a clinical
evaluation of imatinib-resistant CML patients. Our results provide potential target genes for
diagnosis and treatment of imatinib-resistant CML patients.
Abbreviations: CML: Chronic myelogenous leukemia; GEO: Gene Expression Omnibus; DEGs:
differentially expressed genes; PPI: protein-protein interaction GO: Gene Ontology; KEGG:
Kyoto Encyclopedia of Genes and Genomes
KEYWORDS
Chronic myelogenous
leukemia; imatinib-resistant;
microarray; differentially
expressed genes were
identified; protein-protein
interaction; KEGG and GO
enrichment analyses
Introduction
Chronic myelogenous leukemia (CML), mainly induced
by the juxtaposition of DNA sequences from the BCR
and ABL genes, is one of the most common cancer
with a rise in new cases worldwide each year. BCRABL encodes protein p210BCR-ABL which is indispensable and important with dysregulated tyrosine kinase
activity [1,2]. Accumulating evidence has demonstrated that abnormal expression of genes is involved
in the imatinib-resistant CML cells, as well as mutations
of tumor-suppressor genes [3–5]. However, due to the
lack of effective therapeutic methods when patients
with CML resistant to imatinib at the advanced stage
of the disease, the mortality rate of this disease
remains high. Therefore, it is important to understand
the specific molecular mechanisms involved in the
development and progression of imatinib-resistant
CML cells and thus develop effective diagnostic and
therapeutic strategies.
During the recent years, gene chip technology and
microarray analysis have been widely used to screen
new effective biomarkers through computational
approaches [6], which have explored the differentially
expressed genes (DEGs) involved in the imatinib-resistant CML cells. Extensive studies have focused on the
role of single biomarker in signaling pathway.
However, some recent findings suggested that the
network biomarkers and network medicine may contribute to cancer development and progression.
Thus, in the present study, 3 mRNA microarray datasets
from Gene Expression Omnibus (GEO) were downloaded and analyzed to obtain DEGs between imatinib-sensitive CML cells and imatinib-resistant CML
cells. Subsequently, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway
enrichment analysis and protein–protein interaction
(PPI) network analyses help us to understand the molecular mechanisms in the imatinib-resistant CML cells.
© 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which
permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
CONTACT Zhichao Zhang [email protected]; Ting Song [email protected]
Supplemental data for this article can be accessed https://doi.org/10.1080/16078454.2021.1931740.
HEMATOLOGY
2021, VOL. 26, NO. 1, 408–414
https://doi.org/10.1080/16078454.2021.1931740
In conclusion, a total of 217 DEGs and 9 hub genes
were identified, indicating that targeting these genes
is a potential therapeutic strategy for imatinib-resistant
CML cells.
Material and methods
GEO Datasets microarrays data
GEO Datasets (https://www.ncbi.nlm.nih.gov/geo)
database was selected for our study. It is a public functional genomics data repository of high throughput
gene expression data, chips and microarrays. Three
gene expression datasets (GSE7114 [7], GSE92624 [8]
and GSE97562 [9]) were downloaded from GEO.
Screening of DEGs
The DEGs between imatinib-sensitive CML samples
and imatinib-resistant CML samples were screened
using GEO2R (https://www.ncbi.nlm.nih.gov/geo/
geo2r). GEO2R is an online analysis tool that helps
users to identify DEGs under experimental conditions
[10]. The adjusted P-value and |log2FC| were calculated. Genes that met the cutoff criteria, adjusted P <
0.05 and |log2FC|>1.0, were considered as DEGs.
According to the annotation information in the platform, the probe was converted into the corresponding
gene symbol and deleted or averaged which sets
without corresponding gene symbols or genes with
more than one probe set [11,12].
GO enrichment analysis and KEGG pathway
analysis
The Database for Annotation, Visualization and Integrated Discovery (DAVID; https://david.ncifcrf.gov)
[13,14] was applied for GO enrichment analysis and
KEGG pathway analysis for integrated differential
genes. DAVID is an online analysis tool that integrates
all the major public bioinformatics resources, and provides gene related biological mechanisms for users to
extract biological information. KEGG is database
resource that ables to integrate currently known
protein functions and biological systems. The pathways of KEGG mainly including metabolism, environmental information related processes, genetic
information processing and cell physiological processes [15]. GO is a major bioinformatics tool to annotate genes and analyze biological process (BP),
molecular function (MF), and cellular component (CC)
of these genes [16].
PPI program analysis and module analysis
PPI network analysis was performed for differential
genes by using Search Tool for the Retrieval of
Interacting Genes (STRING; https://string-db.org) [17]
online database. PPI of the DEGs were constructed
from the STRING database, and were analyzed by
Cytoscape software. Cytoscape is an open source
bioinformatics tool for visualizing molecular interaction networks of genes and proteins [18]. The Molecular Complex Detection (MCODE) app in Cytoscape
was used to analyze a given network based on topology to find thickly connected point [19]. MCODE
scores > 4 and the number of nodes >5 were set as
cut-off criteria with the default parameters (degree
cutoff = 2, node score cutoff = 0.2, K-core = 2 and max
depth = 100).
Hub genes selection and analysis
The genes with degree of connectivity >10 were identified as hub genes. The Cytoscape plugin Biological
Networks Gene Ontology tool (BiNGO) was used to
analyze and visualize hub genes biological process
[20,21]. The overall survival and disease-free survival
of hub genes were analyzed using Kaplan-Meier
curve in cBioPortal (https://www.cbioportal.org). A
difference with a P-value <0.05 was considered signifi-
cant [22].
Results
Identification of DEGs in imatinib-resistant CML
cells
We performed differential analysis of 24 samples of
imatinib-resistant CML and 24 samples of imatinib-sensitive CML. After standardization of the microarray
results, a total of 4035 differential genes were identified from GSE7114. Additionally, a total of 5642 differential genes were identified from GSE97562 and a total
of 3,949 differential genes were identified from
GSE92624. Venn analysis was performed to get the
intersection of the DEG profiles (Supplementary
Figure S1). Based on the criteria |log2FC|>1 and Pvalue <0.05, 217 DEGs were significantly differentially
expressed among all three groups, consisting of 151
upregulated genes and 66 downregulated genes
between imatinib-resistant CML cells and imatinib-sensitive CML cells.
GO biological process analysis and KEGG
pathway enrichment
GO function and KEGG pathway enrichment analysis of
DEGs were performed using DAVID. GO analysis results
showed that changes in molecular function (MF) of
DEGs were significantly enriched in insulin-like
growth factor I binding, cysteine-type endopeptidase
inhibitor activity involved in apoptotic process,
protein binding and platelet-derived growth factor
HEMATOLOGY 409
receptor binding (Table 1). Changes in biological
process (BP) were mainly enriched in cell adhesion,
positive regulation of nitric oxide biosynthetic
process and inflammatory response (Table 1).
Changes in cell component (CC) of DEGs were mainly
enriched in the extracellular space, plasma membrane
and cell surface (Table 1). The results of KEGG pathway
analysis showed that DEGs were mainly enriched in
hematopoietic cell lineage, transcriptional misregulation in cancer, inflammatory mediator regulation of
transient receptor potential (TRP) channels and cytokine-cytokine receptor interaction (Table 1).
PPI network construction and module analysis
Next, we sought to further understand the functional
modules in the PPI networks of the DEGs unique to
imatinib-resistant CML cells to identify the key genes
for this disease. We used the STRING database to construct PPI networks of DEGs (Supplementary Figure S2)
and the Cytoscape to obtain the most significant
module (Figure 1A,B). The MCODE Cytoscape plugin
was used to construct the functional modules in the
PPI network of the DEGs unique to imatinib-resistant
CML cells. The module in Figure 1B has a score of
9.111 and contains 10 nodes and 41 edges. The GO
function and KEGG pathway enrichment analysis of
most significant module were analyzed using DAVID.
Results showed that genes in this module were
mainly enriched in cell cycle, endopeptidase inhibitor
activity and cell division (Table 2).
Hub gene selection and analysis of prognostic
value
Nine of the most significant module genes evaluated
by connectivity degree in the PPI network were identified as hub genes. The biological process analysis of
the hub genes was shown in Supplementary Figure S3.
The prognostic values of the selected key genes
unique to imatinib-resistant CML patients were analyzed using the cBioPortal online tool. The overall survival analysis and disease-free survival analysis of the
hub genes were shown using Kaplan-Meier curve. We
retrieved the survival curves of the patients from the
TCGA database with the corresponding imatinib-resistant CML and analyzed survival by the expression levels
of the key genes (Figure 2A,B). Imatinib-resistant CML
patients with BIRC5, CCNE2, MCM4, PTTG1 alteration
showed worse disease-free survival. Nonetheless, imatinib-resistant CML patients had an association of
genomic alterations in BIRC5, CCNE2, MCM4, ZWINT
showed reductions in overall survival. In addition, the
BIRC5, CCNE2, MCM4 alteration was significantly
associated with worse overall survival and diseasefree survival. Of the key genes unique to the imatinib-resistant CML patients the expression levels of
only BIRC5, CCNE2 and MCM4 were associated with
patient survival time (P < 0.05). Survival analysis
showed that higher expression levels of the prognosis-related key genes were associated with shorter survival time of imatinib-resistant CML patients.
RT-PCR analysis
We used imatinib-resistant CML cell samples and imatinib-sensitive CML cell samples to evaluate the
expression level of the three hub genes by RT-PCR
analysis. GAPDH is one of the most commonly used
housekeeping genes andused as the internal control.
Figure 1. PPI network constructed with the DEGs by using
Cytoscape. (A) The PPI network of DEGs was constructed
using Cytoscape. Red nodes represent upregulated genes.
Blue nodes represent downregulated genes. (B) The most significant module was obtained from PPI network with 9 nodes
and 34 edges.
Table 2. GO and KEGG pathway enrichment analysis of hub
genes.
GO-ID Description P-value Count
GO:0051301 cell division 2.03E−07 6
GO:0000082 G1/S transition of mitotic cell cycle 1.19E−05 4
GO:0005634 nucleus 0.001207 8
GO:0004869 endopeptidase inhibitor activity 0.016002 2
hsa04110 Cell cycle 5.57E−05 4
hsa04114 Oocyte meiosis 0.07814 2
Table 1. GO and KEGG pathway enrichment analysis of DEGs.
GO-ID Description P-value Count
GO:0007155 cell adhesion 4.06E−08 23
GO:0045429 positive regulation of nitric oxide
biosynthetic process
7.58E−07 8
GO:0006954 inflammatory response 3.71E−06 18
GO:0002576 platelet degranulation 4.35E−06 10
GO:0006915 apoptotic process 5.43E−06 22
GO:0005615 extracellular space 1.10E−06 37
GO:0005886 plasma membrane 4.55E−06 76
GO:0009986 cell surface 4.58E−05 19
GO:0031994 insulin-like growth factor I binding 1.60E−07 6
GO:0043027 cysteine-type endopeptidase
inhibitor activity involved in
apoptotic process
1.39E−04 5
GO:0005515 protein binding 4.88E−04 128
GO:0005161 platelet-derived growth factor
receptor binding
6.61E−04 4
hsa04640 Hematopoietic cell lineage 1.76E−04 9
hsa05202 Transcriptional misregulation in
cancer
2.26E−04 12
hsa04750 Inflammatory mediator regulation of
, respectively. As is shown in Figure 3, mRNA
levels of BIRC5, CCNE2 and MCM4 were significantly
upregulated in imatinib-resistant CML cell samples
than imatinib-sensitive CML cell samples (P < 0.01).
However, the mRNA levels of GAPDH showed no significant difference between two groups.
Discussion
In the present study, we analyzed 3 mRNA microarray
datasets to screen DEGs between imatinib-resistant
CML cells and imatinib-sensitive CML cells. GO and
KEGG enrichment analyses were performed to
explore interactions among the DEGs. The DEGs were
mainly enriched in insulin-like growth factor I
binding, inflammatory response, apoptotic process,
cell adhesion, and Transcriptional misregulation in
cancer. Previous studies have reported that misregulation of the insulin-like growth factor I binding and
apoptotic process play important roles in imatinibresistant CML cells [5,23,24]. Moreover, cell adhesion
often plays a major role in imatinib resistance and
Arsenic Trioxide can overcome cell adhesion mediated
drug resistance through down-regulating the
expression of β1-integrin in K562 chronic myelogenous
leukemia cell line [25]. In a word, all these studies are
consistent with our results.
We used the Cytoscape to obtain the most signifi-
cant module and selected 9 DEGs as hub genes with
degrees ≥ 10. Among these hub genes, BIRC5, CCNE2
and MCM4 were identified in survival analysis and
these genes alteration was significantly associated
with worse overall survival and disease-free survival.
BIRC5, also called survivin, is a member of the inhibitor
of apoptosis (IAP) family of proteins. It plays important
roles in both cell division and proliferation in a majority
of cancers [26,27]. BIRC5 is upregulated in haematological malignancies and was also shown to be overexpressed in CML-BP in comparison to CML-CP [28]. In
Figure 2. Survival analyses of hub genes were shown by using cBioPortal. (A) Overall survival analyses of hub genes. P < 0.05 was
considered statistically significant. (B) Disease-free survival analyses of hub genes. P < 0.05 was considered statistically significant.
Figure 3. The expression levels of mRNAs of three hub genes
in two groups of samples. The BIRC5, CCNE2, MCM4 and glyceraldehyde-3-phosphate dehydrogenase (GAPDH; as control)
transcripts in different cell lines. Error bars show mean ± s.d.,
*** mean the difference is statistically significant (P < 0.001).
HEMATOLOGY 411
the previous studies, BIRC5 has been shown to be correlated with G2M cell-cycle block, cell death and
shorter patient survival in imatinib-resistant CML
[29,30]. Thus, it is regarded as a target for anticancer
agents, such as YM155, shepherdin and FL118 [31].
On the basis of these findings, we suggest that
through inhibition of BIRC5 expression may be
increased sensitivity to imatinib in imatinib-resistant
patients. BIRC5 and BCL-2 genes are frequently coamplified to inhibit apoptosis which is one of the
important reasons for poor prognosis in patients [32].
Moreover, BIRC5 overexpression in BCR-ABL independent imatinib-resistant CML is associated with altered
expression of Bcl-2 [33]. In addition, BIRC5 overexpression has also been found in breast, thyroid and
lung cancers, and may be regarded as a valuable
biomarker for diagnosis, treatment and prognosis of
tumors [34–36].
CCNE2 (cyclin E2) regulates cell cycle progression by
binding to cyclin dependent kinase 2 (CDK2) to form a
serine/threonine kinase holoenzyme complex called
CCNE2-CDK2 [37]. Aberrant regulation of CCNE2 is
one of the biomarkers of tumorigenesis. CCNE2 overexpression has also been found in breast, ovarian,
lung and other cancers [38–40]. In addition, CCNE2
were found to be highly expressed in hematologic
malignancies including acute myeloid leukemia
(AML), chronic myeloid leukemia (CML), acute lymphocytic leukemia (ALL). Targeting peptides was found to
kill leukemia cells by binding nonameric peptides of
CCNE2 and inhibiting the expression of CCNE2 [41].
The overexpression of CCNE2 has been linked to
high cellular proliferation, we suggest that overexpression of CCNE2 in K562 cells might show the highest
resistance to the imatinib treatment. Moreover, a previous study has found that an indirubin derivative
AGM130 induced apoptosis of imatinib-resistant CML
cells by inhibited CCNE2-CDK2 activity [42].
MCM protein family is a class of highly conserved
proteins which were involved in DNA replication,
elongation and transcription. MCM4 is a key component of the minichromosome maintenance protein
complex that is necessary for the initiation of DNA
replication in eukaryotes. The aberrant expression of
MCM4 indicates the proliferation of malignant cells
and atypical cells including potential malignant cells.
Therefore, MCM4 can be used as an effective marker
for the diagnosis of tumors and precancerous lesions
[43,44]. Overexpression of MCM4 has also been
found in lung, breast and other cancers [45,46].
However, MCM4 has not been identified a biomarker
in imatinib-resistant CML cells by biological methods
and our work is first time to identify MCM4 as a biomarker in imatinib-resistant CML cells by Bioinformatics. In the previous study, DNA helicase activity of
MCM4-6-7 complex inhibited by the phosphorylation
by CCNE2-CDK2, indicating that the inhibition of
DNA replication because of phosphorylation of
MCM4 with CCNE2-CDK2 [44]. We speculate that the
effect of imatinib treatment in CML cells was upregulating MCM4 related to cell-cycle phase transition
and DNA replication to resistant apoptosis. Our
results suggest the combination of MCM4 inhibitor
and imatinib may show the synergistic effect in imatinib-resistant CML cells and the potential to decrease
tumor regression.
In addition, we performed hierarchical clustering for
others hub genes including HMMR, KIFC1, CDC25A,
ZWINT, CDKN3, PTTG1. Results showed that these
hub genes differentiated imatinib-resistant CML
samples from imatinib-sensitive CML samples, and
may be associated with the imatinib resistant of CML.
HMMR, a receptor for hyaluronate-mediated motility
(RHAMM), is an oncogene that leading to the neoplastic progression of human leukemias and solid tumors
[47]. KIFC1 plays essential roles in the segregation of
chromosomes in mitosis. It is overexpressed in breast
cancers and may be associated with the docetaxel
resistant of prostate cancer [48,49]. The protein
kinase CDC25A acts as an activator of cyclin E-CDK2
that regulates the G1-S and G2-M transitions in colon
cancer cells [50]. Previous studies found that the
CDC25A is crucial for the proliferation of breast and
lung cancer [51,52]. ZWINT is a centromere-complex
component required for the mitotic spindle checkpoint and involved in the cell growth. Recent research
has found that it can be a novel regulator of hepatocellular carcinoma by regulating cell-cycle-related proteins [53]. CDKN3 regulates mitosis, and high
expression of CDKN3 is involved in the progression
of ovarian cancer [54]. Overexpression of PTTG1 was
found to promote the proliferation of several cancers,
such as liver cancer, lung cancer and adrenocortical
cancer [55–57].
Conclusion
In conclusion, the present study was designed to identify DEGs that may be involved in the imatinib-resistant
CML cells. BIRC5, CCNE2, MCM4 were identified and
may be regarded as diagnostic biomarkers for imatinib-resistant CML cells. Further studies are needed to
elucidate the biological function of these genes in imatinib-resistant CML cells.
Disclosure statement
No potential conflict of interest was reported by the
author(s).
Funding
This work was supported by the National Natural Science
Foundation of China under Grant (81903462); the National
412 H. ZHANG ET AL.
Natural Science Foundation of China under Grant
(81430083); the China Postdoctoral Science Foundation
under Grant (2018M641694) and the Fundamental Research
Funds for the Central Universities under Grant (DUT17LK32).
References
[1] O’Brien SG, Guilhot F, Larson RA, et al. Imatinib compared with interferon and low-dose cytarabine for
newly diagnosed chronic-phase chronic myeloid leukemia. N Engl J Med. 2003;348:994–1004. doi:10.1056/
NEJMoa022457.
[2] Druker BJ. Five-year follow-up of patients receiving imatinib for chronic myeloid leukemia. N Engl J Med
2006;355:2408–2417. doi:10.1056/NEJMoa062867.
[3] Pocaly M, Lagarde V, Etienne G, et al. Overexpression of
the heat-shock protein 70 is associated to imatinib
resistance in chronic myeloid leukemia. Leukemia.
2006;21:93–101. doi:10.1038/sj.leu.2404463.
[4] Warsch W, Kollmann K, Eckelhart E, et al. High STAT5 levels
mediate imatinib resistance and indicate disease progression in chronic myeloid leukemia. Blood.
2011;117:3409–3420. doi:10.1182/blood-2009-10-248211.
[5] Ha SA, Kim S, Shin SM, et al. Gene expression signatures
associated with the resistance to imatinib. Leukemia.
2006;20:1542–1550. doi:10.1038/sj.leu.2404310.
[6] Falzone L, Lupo G, La Rosa GRM, et al. Identification of
novel MicroRNAs and their diagnostic and prognostic
significance in oral cancer. Cancers. 2019;11:610.
doi:10.3390/cancers11050610.
[7] Bao F, Polk P, Nordberg ML, et al. Comparative gene
expression analysis of a chronic myelogenous leukemia
cell line resistant to cyclophosphamide using oligonucleotide arrays and response to tyrosine kinase inhibitors. Leuk Res. 2007;31:1511–1520. doi:10.1016/j.
leukres.2007.03.002.
[8] Miyauchi M, Koya J, Arai S, et al. ADAM8 is an antigen of
tyrosine kinase inhibitor-resistant chronic myeloid leukemia cells identified by patient-derived induced pluripotent stem cells. Stem Cell Rep. 2018;10:1115–1130.
doi:10.1016/j.stemcr.2018.01.015.
[9] Avilés-Vázquez S, Chávez-González A, Hidalgo-Miranda
A, et al. Global gene expression profiles of hematopoietic stem and progenitor cells from patients with chronic
myeloid leukemia: the effect of in vitro culture with or
without imatinib. Cancer Med. 2017;6:2942–2956.
doi:10.1002/cam4.1187.
[10] Xu Y, Shen K. Identification of potential key genes
associated with ovarian clear cell carcinoma. Cancer
Manage Res. 2018;10:5461–5470. doi:10.2147/CMAR.
S187156.
[11] Li L, Lei Q, Zhang S, et al. Screening and identification of
key biomarkers in hepatocellular carcinoma: evidence
from bioinformatic analysis. Oncol Rep 2017;38:2607–
2618. doi:10.3892/or.2017.5946.
[12] Zhong M, Wu Y, Ou W, et al. Identification of key genes
involved in type 2 diabetic islet dysfunction: a bioinformatics study. Biosci Rep. 2019;39:5. doi:10.1042/
BSR20182172.
[13] Huang DW, Sherman BT, Tan Q, et al. The DAVID gene
functional classification tool: a novel biological
module-centric algorithm to functionally analyze large
gene lists. Genome Biol. 2007;8:R183. doi:10.1186/gb-
2007-8-9-r183.
[14] Li GM, Zhang CL, Rui RP, et al. Bioinformatics analysis of
common differential genes of coronary artery disease
and ischemic cardiomyopathy. Eur Sci Med Farmacol.
2018;22:3553–3569. doi:10.26355/eurrev_201806_
15182.
[15] Klukas C, Schreiber F. Dynamic exploration and editing
of KEGG pathway diagrams. Bioinformatics.
2007;23:344–350. doi:10.1093/bioinformatics/btl611.
[16] Eden E, Navon R, Steinfeld I, et al. GOrilla: a tool for discovery and visualization of enriched GO terms in ranked
gene lists. BMC Bioinf. 2009;10:48. doi:10.1186/1471-
2105-10-48.
[17] Franceschini A, Szklarczyk D, Frankild S, et al. STRING
v9.1: protein-protein interaction networks, with
increased coverage and integration. Nucleic Acids Res.
2013;41:D808–D815. doi:10.1093/nar/gks1094.
[18] Shannon P, Markiel A, Ozier O, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498–
2504. doi:10.1101/gr.1239303.
[19] Bandettini WP, Kellman P, Mancini C, et al. Multicontrast
delayed enhancement (MCODE) improves detection of
subendocardial myocardial infarction by late gadolinium enhancement cardiovascular magnetic resonance: a clinical validation study. J Cardiovasc Magn
2012;14:83. doi:10.1186/1532-429X-14-83.
[20] Maere S, Heymans K, Kuiper M. BiNGO: a cytoscape
plugin to assess overrepresentation of gene ontology
categories in biological networks. Bioinformatics.
2005;21:3448–3449. doi:10.1002/pssc.200778445.
[21] Kohl M, Wiese S, Warscheid B. Cytoscape: software for
visualization and analysis of biological networks. Mol
Biol 2011;696:291–303. doi:10.1007/978-1-60761-987-
1_18.
[22] Gao J, Aksoy BA, Dogrusoz U. Integrative analysis of
complex cancer genomics and clinical profiles using
the cBioPortal. Sci Signaling. 2013;6:11. doi:10.1126/
scisignal.2004088.
[23] Bozkurt S, Ozkan T, Ozmen F, et al. The roles of epigenetic modifications of proapoptotic BID and BIM
genes in imatinib-resistant chronic myeloid leukemia
cells. Hematology. 2013;18:217–223. doi:10.1179/
1607845412Y.0000000056.
[24] Juan L, Yujing Z, Aichun L, et al. Distinct dasatinibinduced mechanisms of apoptotic response and
exosome release in imatinib-resistant human chronic
myeloid leukemia cells. Int J Mol Sci. 2016;17:531.
doi:10.3390/ijms17040531.
[25] Guo-Bao W, Xiao-Qin C, Qi-Rong G, et al. Arsenic
Trioxide overcomes cell adhesion-mediated drug resistance through down-regulating the expression of β1-
integrin in K562 chronic myelogenous leukemia cell
line. Leuk Lymphoma. 2010;51:1090–1097. doi:10.
3109/10428191003746315.
[26] Altieri DC. Validating survivin as a cancer therapeutic
target. Nat Rev Cancer. 2003;3:46–54. doi:10.1038/nrc968.
[27] Ambrosini G, Adida C, Altieri DC. A novel anti-apoptosis
gene, survivin, expressed in cancer and lymphoma. Nat
Med. 1997;3:917–921. doi:10.1038/nm0897-917.
[28] Glodkowska-Mrowka E, Solarska I, Mrowka P, et al.
Differential expression of BIRC family genes in chronic
myeloid leukaemia BIRC3 and BIRC8 as potential new
candidates to identify disease progression. Brit J
Haematol. 2014;164:740–742. doi:10.1111/bjh.12663.
[29] Carter BZ. Regulation of survivin expression through
Bcr-Abl/MAPK cascade: targeting survivin overcomes
imatinib resistance and increases imatinib sensitivity
in imatinib-responsive CML cells. Blood.
2006;107:1555–1563. doi:10.1182/blood-2004-12-4704.
HEMATOLOGY 413
[30] Stella S, Tirro E, Conte E, et al. Suppression of survivin
induced by a BCR-ABL/JAK2/STAT3 pathway sensitizes
imatinib-resistant CML cells to different cytotoxic
drugs. Mol Cancer Ther. 2013;12:1085–1098. doi:10.
1158/1535-7163.MCT-12-0550.
[31] Soleimanpour E, Babaei E. Survivin as a potential target
for cancer therapy. Asian Pac J Cancer P. 2015;16:6187–
6191. doi:10.7314/APJCP.2015.16.15.6187.
[32] Kawasaki H, Altieri DC, Lu CD, et al. Inhibition of apoptosis by survivin predicts shorter survival rates in colorectal cancer. Cancer Res. 1998;58:50–71. Available from:
https://cancerres.aacrjournals.org/content/58/22/5071.
[33] Adebayo BO, Kuang-Tai K, Li-Ping Y, et al. HDAC inhibitor suppresses proliferation and tumorigenicity of drugresistant chronic myeloid leukemia stem cells through
regulation of hsa-miR-196a targeting BCR/ABL1. Exp
Cell Res. 2018;370:519–530. doi:10.1016/j.yexcr.2018.
07.017.
[34] Lv YG, Yu F, Yao Q, et al. The role of survivin in diagnosis, prognosis and treatment of breast cancer. J Thorac
Dis. 2010;2:100. Available from: https://www.ncbi.nlm.
nih.gov/pmc/articles/PMC3256445/?report=abstract.
[35] Waligrska-Stachura J, Sawicka-Gutaj N, Zabel M, et al.
Survivin DEx3 as a biomarker of thyroid cancers: A
study at the mRNA and protein level. Oncol Lett.
2017;13:24–37. doi:10.3892/ol.2017.5713.
[36] Mohamed S, Yasufuku K, Nakajima T, et al. Nuclear survivin in pN2 nonsmall cell lung cancer: prognostic and
clinical implications. Eur Respir J. 2009;33:127–133.
doi:10.1183/09031936.00068708.
[37] Gudas JM, Payton M, Thukral S, et al. Cyclin E2, a novel
G1 cyclin that binds Cdk2 and is aberrantly expressed in
human cancers. Mol Cell Biol. 1999;19:612–622. doi:10.
1128/MCB.19.1.612.
[38] Masamha CP, Benbrook DM. Cyclin D1 degradation is
sufficient to induce G1 cell cycle arrest despite constitutive expression of cyclin E2 in ovarian cancer cells.
Cancer Res. 2009;69:6565–6572. doi:10.1158/0008-
5472.CAN-09-0913.
[39] Payton M, Scully S, Chung G, et al. Deregulation of
cyclin E2 expression and associated kinase activity in
primary breast tumors. Oncogene. 2002;21:8529–8534.
doi:10.1046/j.1365-2958.2000.01764.x.
[40] Müller-Tidow C, Metzger R, Kügler K, et al. Cyclin E is the
only cyclin-dependent kinase 2-associated cyclin that
predicts metastasis and survival in early stage nonsmall cell lung cancer. Cancer Res. 2001;61:647–653.
doi:10.1016/S0165-4608(00)00343-5.
[41] He H, Kondo Y, Ishiyama K, et al. Two unique HLA-A*
0201 restricted peptides derived from cyclin E as immunotherapeutic targets in leukemia. Leukemia. 2020: 1–
11. doi:10.1038/s41375-019-0698-z.
[42] Kim WS, Lee MJ, Kim DH, et al. 5′
-OH-5-nitro-Indirubin
oxime (AGM130), an Indirubin derivative, induces apoptosis of imatinib-resistant chronic myeloid leukemia
cells. Leuk Res. 2013;37:427–433. doi:10.1016/j.leukres.
2012.12.017.
[43] Ishimi Y. A DNA helicase activity is associated with an
MCM4, -6, and -7 protein complex. J Biol Chem.
1997;272:24508–24513. doi:10.1074/jbc.272.39.24508.
[44] Ishimi Y, Komamura-Kohno Y, Kwon HJ, et al.
Identification of MCM4 as a target of the DNA replication block checkpoint system. J Biol Chem.
2003;278:24644–24650. doi:10.1074/jbc.M213252200.
[45] Kikuchi J, Kinoshita I, Shimizu Y, et al. Minichromosome
maintenance (MCM) protein 4 as a marker for proliferation and its clinical and clinicopathological signifi-
cance in non-small cell lung cancer. Lung Cancer.
2011;72:229–237. doi:10.1016/j.lungcan.2010.08.020.
[46] Kwok HF, Zhang SD, McCrudden CM, et al. Prognostic
significance of minichromosome maintenance proteins
in breast cancer. Am J Cancer Res. 2015;5:52. Available
from: https://pubmed.ncbi.nlm.nih.gov/25628920/.
[47] Tilghman J, Wu H, Sang Y, et al. HMMR maintains the
stemness and tumorigenicity of glioblastoma stemlike cells. Cancer Res. 2014;74:3168–3179. doi:10.1158/
0008-5472.CAN-13-2103.
[48] Fu X, Zhu Y, Zheng B, et al. KIFC1, a novel potential
prognostic factor and therapeutic target in hepatocellular carcinoma. Int J Oncol. 2018;52:1912–1922.
doi:10.3892/ijo.2018.4348.
[49] Sekino Y, Oue N, Shigematsu Y, et al. KIFC1 induces
resistance to docetaxel and is associated with survival
of patients with prostate cancer. Urol Oncol.
2017;35:1–8. doi:10.1016/j.urolonc.2016.08.007.
[50] Wang P, Zou F, Zhang X. microRNA-21 negatively regulates Cdc25A and cell cycle progression in colon cancer
cells. Cancer Res. 2009;69:8157–8165. doi:10.1158/
0008-5472.CAN-09-1996.
[51] Cangi MG, Pagano M, Loda M. Role of the Cdc25A phosphatase in human breast cancer. J Clin Invest.
2000;106:753–761. doi:10.1172/JCI9174.
[52] Wu W, Fan YH, Kemp BL, et al. Overexpression of
cdc25A and cdc25B is frequent in primary non-small
cell lung cancer but is not associated with overexpression of c-myc. Cancer Res. 1998;58:4082–4085.
Available from: https://cancerres.aacrjournals.org/
content/58/18/4082.
[53] Hanning Y, Zhiyao X, Mingming C, et al. Overexpression
of zwint predicts poor prognosis and promotes the proliferation of hepatocellular carcinoma by regulating
cell-cycle-related proteins. OncoTargets Ther.
2018;11:689–702. doi:10.2147/OTT.S152138.
[54] Li T, Xue H, Guo Y, et al. CDKN3 is an independent prognostic factor and promotes ovarian carcinoma cell proliferation in ovarian cancer. Oncol Rep. 2014;31:1825–
1831. doi:10.3892/or.2014.3045.
[55] Jung CR, Yoo J, Jang YJ, et al. Adenovirus-mediated
transfer of siRNA against PTTG1 inhibits liver cancer
cell growth in vitro and in vivo. Hepatology.
2016;43:1042–1052. doi:10.1002/hep.21137.
[56] Li H, Yin C, Zhang B, et al. PTTG1 promotes migration
and invasion of human non-small cell lung cancer
cells and is modulated by miR-186. Carcinogenesis.
2013;34:2145–2155. doi:10.1093/carcin/bgt158.
[57] Demeure MJ, Coan KE, Grant CS, et al. PTTG1 overexpression in adrenocortical cancer is associated with
poor survival and represents a potential therapeutic
target. Surgery. 2013;154:1405–1416. doi:10.1016/j.
surg.2013.06.058.
414 H. ZHANG ET AL.