Abstract

   Glucocorticoids (GC) are the foundation of the chemotherapy regimen in
   acute lymphoblastic leukemia (ALL). However, resistance to GC is
   observed more frequently than resistance to other chemotherapy agents
   in patients with ALL relapse. Moreover, the mechanism underlying the
   development of GC resistance in ALL has not yet been fully uncovered.
   In this study, we used bioinformatic analysis methods to integrate the
   candidate genes and pathways participating in GC resistance in ALL and
   subsequently verified the bioinformatics findings with in vitro cell
   experiments. Ninety‐nine significant common differentially expressed
   genes (DEGs) associated with GC resistance were determined by
   integrating two gene profile datasets, including GC‐sensitive and
   ‐resistant samples. Using Kyoto Encyclopedia of Genes and Genomes
   (KEGG) and REACTOME pathways analysis, the signaling pathways in which
   DEGs were significantly enriched were clustered. The GC
   resistance‐related biologically functional interactions were visualized
   as DEG‐associated Protein–Protein Interaction (PPI) network complexes,
   with 98 nodes and 127 edges. MYC, a node which displayed the highest
   connectivity in all edges, was highlighted as the core gene in the PPI
   network. Increased C‐MYC expression was observed in
   adriamycin‐resistant BALL‐1/ADR cells, which we demonstrated was also
   resistant to dexamethasone. These results outlined a panorama in which
   the solitary and scattered experimental results were integrated and
   expanded. The potential promising target of the candidate pathways and
   genes involved in GC resistance of ALL was concomitantly revealed.

   Keywords: acute lymphoblastic leukemia, bioinformatic analysis,
   glucocorticoid resistance, MYC, signaling pathway
     __________________________________________________________________

   These results outlined a panorama in which the solitary and scattered
   experimental results were integrated and extended. The potential
   promising target of the candidate pathways and genes in GC resistant
   acute lymphoblastic leukemia was concomitantly revealed.

   graphic file with name CAM4-9-2918-g007.jpg

1. INTRODUCTION

   Acute lymphoblastic leukemia comprises a group of aggressive aberrant
   hematopoietic malignancies, including acute/chronic lymphoblastic
   leukemia (ALL/CLL) and lymphoma. Combination chemotherapy regimen based
   on vincristine and prednisolone plus other classical chemotherapeutics
   and/or novel targeting agents is regarded as the essential care for
   patients with ALL.[46]1, [47]2, [48]3 Although the advances in
   treatment strategies facilitated favorable clinical outcomes, a portion
   of patients with ALL unfortunately suffer single or multiple
   chemotherapeutic resistance and fall into relapse and/or refractory
   disease with dismal prognosis.[49]4 Resistance to prednisolone, a
   glucocorticoid (GC) agent that is fundamental in the current ALL
   chemotherapy scheme, is more frequently observed than resistance to
   other drugs and hampers the possibility of prolonged survival.[50]5

   GC agonists inhibit leukemogenesis by binding the GC receptor and
   subsequently activating or suppressing transcription of target genes,
   which are related to various cellular processes such as cell cycle
   arrest and apoptosis.[51]6, [52]7 Several studies have recently
   suggested candidate molecules and pathways that may be responsible for
   the development of GC resistance, including MLL‐rearrangement, and
   PI3K/AKT pathway and MAPK signaling cascades hyperactivation.[53]8,
   [54]9, [55]10, [56]11, [57]12, [58]13, [59]14 Despite the increasing
   insights into several aspects of this mechanism, a comprehensive
   overview of the integrated biological landscape underlying GC
   resistance in ALL is currently missing.

   Recently, high‐throughput sequencing and microarrays of human disease
   samples have generated massive bioinformatics data, which facilitated
   understanding the molecular mechanisms involved in the related
   biological process. In this study, we identified and integrated the
   common differentially expressed genes (DEGs) from two gene expression
   profiles of GC‐sensitive and ‐resistant ALL samples. Subsequently, we
   verified those bioinformatics findings with in vitro cell experiments.
   Our findings integrate individual experimental results from previous
   studies to illustrate the landscape of biological processes that lead
   to acquisition of GC resistance. Based on these results, novel
   treatment opportunities and therapeutic targets can be envisaged.

2. METHODS

2.1. Derivation of genetic data

   Microarray datasets of gene expression profiles with the accession
   numbers [60]http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE5820
   and [61]http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE19143,
   both unique, were downloaded from the NCBI‐gene expression omnibus
   (NCBI‐GEO) ( [62]://www.ncbi.nlm.nih.gov/geo/), a public functional
   genomics data repository.[63]15, [64]16 The microarray of
   [65]http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE5820 was
   performed on [66]GPL96 Affymetrix Human Genome U133 platform and
   included 13 GC‐sensitive and 16 GC‐resistant samples (Submission date:
   Sep 13, 2006, last update date: Aug 10, 2018). Regarding the
   [67]http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE19143 data,
   total RNA of 14 non‐infant GC‐sensitive and 13 non‐infant GC‐resistant
   samples were isolated and mRNA array was performed on [68]GPL96
   Affymetrix Human Genome U133A Array (Submission date: Nov 23, 2009,
   last update date: Aug 10, 2018). GC‐resistant samples were those with
   poor response to prednisolone, defined as failure to show effective
   cytoreduction after 7 days of GC therapy.[69]17

2.2. Differentially expressed gene identification

   Differentially expressed genes between GC‐sensitive and ‐resistant
   samples were identified by analyzing raw data with GEO2R on GEO
   database. A Log[2] transformation was performed, and the Log[2]‐fold
   change (Log[2]FC) was calculated in the preprocessed microarray data
   derived from Series Matrix File on GEO database for integrated
   analysis. P < .05 and |Log[2]FC| > 1 was considered as the cut‐off
   criterion for significantly DEG screening.

2.3. Gene ontology and pathway enrichment analyses

   GO enrichment analysis was performed using the DAVID
   ([70]://david.ncifcrf.gov/conversion.jsp), a web‐based platform for
   gene functional annotations and biological meaning elucidation. Pathway
   analysis allows to understand molecular interactions in the pathway
   maps. Pathways enrichment analysis was generated by the Kyoto
   Encyclopedia of Genes and Genomes (KEGG) and REACTOME, which were
   included in the DAVID website, with P < .05 as the threshold value.

2.4. Integration of protein–protein interaction (PPI) network

   String JAVA platform ([71]http://string-db.org/), an online
   protein–association network platform, was utilized to expand the
   DEG‐encoded proteins and protein‐protein interaction (PPI) network.
   Further, the PPI network was imported into Cytoscape software for
   analyzing protein interaction links and modules, and accessing the
   interactions of the candidate DEG‐encoding proteins associated with GC
   resistance of ALL. Hub genes were identified by cytoHubba analysis in
   Cytoscape.

2.5. Hub gene validation and genetic alteration

   The mRNA expression level of the hub genes was validated on the
   Oncomine database ([72]://www.oncomine.org/resource/login.html). The
   genetic alterations of hub genes were investigated on the cBio Cancer
   Genomics Portal ([73]://cbioportal.org).

2.6. GC resistance‐related miRNAs and predictions of miRNA targets

   GC resistance‐related miRNAs were identified by searching for the terms
   “GC resistant AND miRNA” in PubMed. Based on the papers obtained by the
   search, hsa‐miR‐142‐3p and hsa‐miR‐17‐5p were selected as the miRNAs
   associated with GC resistance.[74]18 Target genes were predicted by the
   miRNA‐target gene prediction databases miRwalk
   ([75]http://mirwalk.umm.uni-heidelberg.de) and TargetScan
   ([76]http://www.targetscan.org/vert_71/). The target genes
   authenticated by miRwalk and TargetScan were analyzed by intersection.
   Target genes‐miRNA interaction network was constructed using Cytoscape
   software.

2.7. Generation of target genes‐TFs interaction network

   The genes which overlapped between GC‐resistant genes and human TFs
   (transcript factors) were considered the GC‐resistance TFs. The
   targeted genes were predicted on the Cistrome Data Browser
   ([77]://cistrome.org/db/). The target genes‐TF interaction network was
   constructed using Cytoscape software.

2.8. Cell culture

   Due to lack of dexamethasone‐sensitive and ‐resistant ALL cell lines,
   BALL‐1 and adriamycin (ADR)‐resistant cells BALL‐1/ADR were employed to
   evaluate dexamethasone sensitivity first and for subsequent
   experiments. Cells were preserved in our laboratory (Fujian Institute
   of Hematology, Fuzhou, China) and cultured in RPMI 1640 medium
   (Hyclone, UT, USA) supplemented with 10% heat‐inactivated fetal bovine
   serum (FBS, Gibco, UT) in a humidified incubator (Thermo, USA)
   maintained at 37°C with 5% CO[2]. Then, 0.5 μg/mL ADR (Sigma, MO) was
   added to BALL‐1 cell culture medium to maintain drug resistance. The
   medium was changed every other day. Cells in logarithmic growth phase
   were used in experiments. BALL‐1 cells were incubated in ADR‐free
   medium for 2 weeks for further experiments.

2.9. Dexamethasone sensitivity assay

   BALL‐1 and BALL‐1/ADR cells were incubated with serially diluted
   dexamethasone solution ranging from 0 to 200 μg/mL for 72 hours. The
   3‐(4,5‐dimethylthiazol‐2‐yl)‐2,5 diphenyltetra‐zolium bromide (MTT,
   Sigma, MO, USA) assay was performed to detect cell proliferation as
   previously described.[78]19 Absorbance was measured with an Elx808
   Absorbance Microplate spectrophotometer (BioTek, UT) at reference
   wavelengths of 490 and 630 nm. The proliferation inhibitory rate (%)
   was calculated by: [1‐(absorbance of experimental well/absorbance of
   control well)] × 100%.

2.10. Quantitative real‐time PCR (qPCR)

   Total RNA was extracted using TRIzol reagent (Life Tech Invitrogen, CA,
   USA) and was subjected to reverse transcription with GoScript^TM
   Reverse Transcription System kit according to the manufacturer's
   protocol (Promega, Madison, USA). cDNA was amplified using SYBR Green
   PCR master mix (Life Tech Invitrogen, CA, USA). PCR was performed on
   ABI7500 Real Time PCR system (Applied Biosystems, CA, USA). Results
   were analyzed using the ∆CT and 2^−∆∆CT quantification method. The
   nucleotide sequences of the primers were as follows: 18s: sense:
   5′‐GACACGGACAGGATTGACAGATTG‐3′, antisense: 5′‐TGCCAGAGTCTCGTTC.

   GTTATCG‐3′; c‐myc: sense: 5′‐TCCTGGCAAAAGGTCAGAGT‐3′, anti‐sense:
   5′‐TCTGACACTGTCCAACTTGAC‐3′.

2.11. Western blotting

   Western blot analysis was performed as described in our previous
   study.[79]20 The primary antibodies used for immunoblotting were GADPH
   and c‐myc (Cell Signaling Technology, Danvers, MA, USA),
   Immunoreactivity was detected by chemiluminescence reaction with an
   enhanced chemiluminescence (ECL) kit (Pierce/Thermo Fisher Scientific,
   Rockford, IL, USA).

2.12. Statistics

   Experiments were performed in triplicate. Results of measurement data
   were expressed as means ± SEM Student's t test and one‐way analysis of
   variance (ANOVA) test were used to assess significant differences
   between groups according to data characteristics. Significance was set
   at a P < .05. All data were analyzed using SPSS statistics software
   23.0 or GraphPad Prism Software 6.0.

3. RESULTS

3.1. Identification of GC resistance‐associated DEGs in ALL

   GC‐sensitive and ‐resistant ALL gene expression profiles
   [80]http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE5820 and
   [81]http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE19143 were
   downloaded from NCBI‐GEO. From the two above GSE datasets, 363 and 611
   DEGs were extracted, respectively, with P < .05 and |Log[2]FC| > 1 as
   threshold values. DEGs are listed in Table [82]1. The volcano plot of
   the two GSE profiles was generated (Figure [83]1A,B). Subsequently, 99
   common DEGs were screened out from the two datasets using integrated
   bioinformatics assay, which included 52 upregulated genes and 47
   downregulated genes in GC‐resistant ALL samples compared with
   GC‐sensitive samples (Figure [84]1C; Table [85]1). The boxplots of the
   top two common upregulated (CREM, CXCL2) and downregulated (SOX11,
   SPON1) genes are shown in Figure [86]1D,E. To visualize the prominent
   different distribution of the 99 common DEGs, heat maps of the two GSE
   datasets were generated using Morpheus software (Figure [87]S1).

Table 1.

   Commonly changed differentially expressed genes (DEGs) between
   glucocorticoids (GC) sensitive and resistant samples in acute
   lymphoblastic leukemia (ALL)
   Common DEGs Gene name
   Up‐regulated (n = 52) ELF4, CXCL11, CXCL8, SPAG9, SPRY1, CCND2, MAPK7,
   CHN2, PDE4B, CD58, MECP2, SLC2A3, TCF7L2, HSPA1B, HSPA1A, CPD, ADM,
   SLC2A14, CDC42EP3, NUDT6, SLC39A8, SRSF8, KPNA4, ACSL1, MYC, MCL1,
   MAFF, GCH1, RIPK2, TIMP1, DAPK1, EIF5, PRKAR2B, EMP1, STAB1, BCL2A1,
   FERMT2, ITGA6, SERPINE1, LPAR6, GNA13, ATF3, NUP50, PON2, NEU1, NR4A2,
   NR4A3, ZNF165, FOSL2, HCAR3, CREM, CXCL2
   Down‐regulated (n = 47) SPON1, SOX11, ID4, NRTN, S100A9, LHPP, MARCKS,
   S100A8, PTPRM, CHST2, PCDH9, LCN2, FGF12, SLIT3, OLFM4, HRK, RPL35A,
   IRS1, BASP1, PDGFRA, AEBP1, VASH2, RFWD3, ZNF611, TMCC1, IGLC1, ORAI2,
   PAX5, PRG2, TBL1X, SAC3D1, ADAM3B, RPL37A, MRPS12, RECQL5, HP, PACS1,
   BAHCC1, CD79B, GTSE1, RHOBTB1, HNRNPM, IQCK, TCF3, TMSB15B, TMSB15A,
   KIAA0226L
   [88]Open in a new tab

Figure 1.

   Figure 1
   [89]Open in a new tab

   Identification of glucocorticoids (GC) resistance‐associated
   differentially expressed genes (DEGs) in acute lymphoblastic leukemia.
   Volcano plot of
   [90]http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE5820 (A) and
   [91]http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE19143 (B). Red
   color represents upregulated genes, green color represents
   downregulated genes. (C) Identification of 99 commonly changed GC
   resistance‐related DEGs from two cohort profile datasets and using
   Venny website ([92]://bioinfogp.cnb.csic.es/tools/venny/index.html).
   Blue color areas represent
   [93]http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE5820 dataset,
   yellow color areas represent
   [94]http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE19143, and the
   cross area represents the commonly changed DEGs. (D, E) The boxplot of
   the top two common up‐ (CREM, CXCL2) and downregulated (SOX11, SPON1)
   genes

3.2. GO enrichment analysis

   To understand the functional changes leading to GC resistance in ALL,
   the 99 candidate common DEGs were mapped on the DAVID database for GO
   analysis. As shown in Table [95]2, the primary GO terms enriched in
   upregulated DEGs were response to stimulus, positive regulation of
   biological process, and positive regulation of cellular process.
   Additionally, downregulated DEGs were predominantly enriched in
   negative regulation of transcription from RNA polymerase II promoter,
   defense response to bacterium, and innate immune response. Taken
   together, results of GO analysis suggested that the GC
   resistance‐associated DEGs were mainly centered in positive regulation
   of biological process, response to stress, and positive regulation of
   cellular process (−Log P value methods, Figure [96]2).

Table 2.

   GO analysis for up‐ and down‐regulated differentially expressed genes
   (top 5)
   Term Description Gene count P value
   Up‐regulated
   GO:0 050 896 Response to stimulus 40 1.29E‐05
   GO:0 048 518 Positive regulation of biological process 35 1.11E‐06
   GO:0 048 522 Positive regulation of cellular process 32 9.49E‐06
   GO:0 051 716 Cellular response to stimulus 31 2.75E‐02
   GO:0 023 052 Signaling 29 8.13E‐03
   Down‐regulated
   GO:0 000 122 Negative regulation of transcription from RNA polymerase
   II promoter 6 3.00E‐02
   GO:0 042 742 defense response to bacterium 5 4.20E‐04
   GO:0 045 087 Innate immune response 5 2.00E‐02
   GO:0 006 412 Translation 4 2.40E‐02
   GO:0 050 853 B cell receptor signaling pathway 3 7.70E‐03
   [97]Open in a new tab

Figure 2.

   Figure 2
   [98]Open in a new tab

   GO analysis. (A) GO analysis and significantly enriched GO terms of
   differentially expressed genes (DEGs) between glucocorticoids
   (GC)‐sensitive and ‐resistant acute lymphoblastic leukemia (ALL).
   Visualization of the significantly changed GO terms in the three
   functional groups. (B) Significantly enriched GO terms of DEGs in
   GC‐resistant ALL based on their functions. Differentially expressed
   genes functional enrichment was conducted using GO analysis on DAVID
   database

3.3. Pathway enrichment analysis

   Pathway enrichment analysis was performed to determine the candidate
   pathways connected to GC resistance‐associated DEGs and analyze their
   alterations. Downregulated DEGs were mainly enriched in “Ribosome
   pathway” in KEGG_PATHWAY analysis, and “Antigen activates B Cell
   Receptor (BCR) leading to generation of second messengers” and “RAF/MAP
   kinase cascade” in REACTOME_PATHWAY analysis. Upregulated DEGs were
   predominantly enriched in “Legionellosis,” “Transcriptional
   misregulation in cancer” and “Pathways in cancer” in KEGG_PATHWAY
   analysis, and “Viral RNP Complexes in the Host Cell Nucleus”,
   “Chemokine receptors bind chemokines,” and “Binding of TCF/LEF:CTNNB1
   to target gene promoters” in REACTOME_PATHWAY analysis (Table [99]3).

Table 3.

   Significantly changed pathways in glucocorticoids (GC) resistant acute
   lymphoblastic leukemia (ALL)
   Category Term P value Genes FDR
   Down‐regulated DEGs
   KEGG hsa03010:Ribosome .058451099 RPL35A, MRPS12, RPL37A 43.24428955
   REACTOME R‐HSA‐983695:R‐HSA‐983695 .022857593 ORAI2, CD79B, IGLC1
   21.28229012
   REACTOME R‐HSA‐5673001:R‐HSA‐5673001 .034409189 NRTN, PDGFRA, IRS1
   30.39798248
   Up‐regulated DEGs
   KEGG hsa05134:Legionellosis .001930947 CXCL2, CXCL8, HSPA1A, HSPA1B
   2.122449312
   KEGG hsa05202:Transcriptional misregulation in cancer .007090346 CCND2,
   BCL2A1, CXCL8, NR4A3, MYC 7.594024245
   KEGG hsa05200:Pathways in cancer .008539708 GNA13, ITGA6, LPAR6, CXCL8,
   MYC, TCF7L2, DAPK1 9.080184539
   KEGG hsa05219:Bladder cancer .015355087 CXCL8, MYC, DAPK1 15.78132383
   KEGG hsa04621:NOD‐like receptor signaling pathway .027619574 CXCL2,
   CXCL8, RIPK2 26.71925927
   KEGG hsa04390:Hippo signaling pathway .032309078 CCND2, SERPINE1, MYC,
   TCF7L2 30.54775066
   KEGG hsa04151:PI3K‐Akt signaling pathway .074007029 ITGA6, MCL1, CCND2,
   LPAR6, MYC 57.40416186
   KEGG hsa05145:Toxoplasmosis .092188264 ITGA6, HSPA1A, HSPA1B
   65.81931717
   REACTOME R‐HSA‐168330:R‐HSA‐168330 .008577044 HSPA1A, HSPA1B
   9.166509374
   REACTOME R‐HSA‐380108:R‐HSA‐380108 .026397272 CXCL2, CXCL8, CXCL11
   25.81297847
   REACTOME R‐HSA‐4411364:R‐HSA‐4411364 .029707264 MYC, TCF7L2 28.57986805
   REACTOME R‐HSA‐3371453:R‐HSA‐3371453 .036177342 NUP50, HSPA1A, HSPA1B
   33.71879102
   REACTOME R‐HSA‐3371568:R‐HSA‐3371568 .058553978 HSPA1A, HSPA1B
   49.00493226
   REACTOME R‐HSA‐3371571:R‐HSA‐3371571 .078645388 HSPA1A, HSPA1B
   59.91663333
   REACTOME R‐HSA‐418594:R‐HSA‐418594 .088167851 CXCL2, CXCL8, CXCL11,
   HCAR3 64.30509097
   [100]Open in a new tab

3.4. Key candidate pathways identification with DEGs PPI network

   String database and Cytoscape software were employed to identify and
   visualize the PPI network derived from candidate DEGs. A total of 98
   out of the 99 common DEGs were inserted in the GC resistance‐associated
   PPI network, which comprised 98 nodes and 127 edges (molecular
   interaction) with PPI enrichment P value of 9.84E‐13, indicating a
   reliable GC‐resistant PPI network (Figure [101]3A). The topological
   properties are listed in Table [102]S1. By Cytoscape MCODE analysis, 6
   significant modules were screened from the PPI network (Figure
   [103]3B). Of these, module 1 included 5 nodes and 8 edges, and module 2
   included 4 nodes and 5 edges. Using cytoHubba analysis, among the 98
   nodes, MYC, CXCL8, ATF3, the three central node genes with degree > 10,
   were considered as hub genes in the PPI network. MYC, for which the
   nodes showed the highest connectivity in all interactions, was
   highlighted as the core gene connected to GC resistance‐related PPI
   network (Figure [104]3C).

Figure 3.

   Figure 3
   [105]Open in a new tab

   Differentially expressed genes (DEGs) protein–protein interaction (PPI)
   network complex based on String website and integrated by Cytoscape
   software. In this picture, each circle represents a gene (node) and
   each connection represents a direct or indirect connection (edge). (A)
   Protein–Protein Interaction network marked for separating up‐ and
   downregulated DEGs. Yellow color represents upregulated genes, and blue
   color represents downregulated genes. (B) Modules analysis in PPI
   network. Clusters were extracted by MCODE and presented with different
   colors. (C) The top 10 of genes with highest degrees identified by
   cytoHubba analysis. MYC displayed the highest connectivity in all
   interactions

3.5. Target genes‐miRNA interaction and TF‐target genes network

   The 64 and 194 target genes of hsa‐miR‐142‐3p and hsa‐miR‐17‐5p,
   respectively, were verified using the two databases miRwalk and
   TargetScan (Table [106]S2). The genes overlapping between DEGs and
   hsa‐miR‐142‐3p were MARCKS, EIF5, SPAG9, ACSL1, KPNA4, PDE4B, RFWD3,
   TBL1X, and SOX11. The genes overlapping between DEGs and hsa‐miR‐17‐5p
   were NR4A3 and CCND2. Then, the target genes‐miRNA network was
   constructed using Cytoscape software (Figure [107]4A).

Figure 4.

   Figure 4
   [108]Open in a new tab

   The target genes‐miRNA network and the transcript factors (TF)‐target
   genes network. (A) Target genes‐miRNA network integrated by Cytoscape
   software. Green color represents miRNA. Blue color represents target
   genes. (B) Green color represents results integrated by Cytoscape
   software. Red color represents TFs. Green color represents target genes

   CREM, ATF3, ELF4, and FOSL2, the four genes overlapping between TFs and
   GC‐resistant DEGs, were subjected to target gene prediction. Table
   [109]S3 lists the genes overlapping between the target genes of the TFs
   and DEGs. The generated TF‐target genes network is shown in Figure
   [110]4B.

3.6. Validation and genetic alteration of hub Genes

   MYC, CXCL8, and ATF3 were considered the hub genes and queried on
   Oncomine database and cBio portal platform to investigate their gene
   expression and genetic alterations in lymphoblastic leukemia. The mRNA
   expression level of MYC, CXCL8 and ATF3 exhibited a 2.564, 4.446,
   3.605‐fold change, respectively, between lymphoblastic leukemia and
   normal samples in the examined study[111]21, [112]22 (Figure [113]5A).
   The alterations for the 3 queried genes were calculated to be between
   0% and 2.1% in the examined ALL samples. Genetic mutations of MYC and
   ATF3 were 2.1% and 0.7%, respectively. No CXCL8 alteration was observed
   in the examined study[114]23 (Figure [115]5B).

Figure 5.

   Figure 5
   [116]Open in a new tab

   Validation and genetic alteration of hub genes. (A) mRNA expression of
   MYC, ATF3, and CXCL8 in acute lymphoblastic leukemia (ALL) samples and
   normal samples on Oncomine database. MYC, CXCL8, and ATF3 exhibited a
   2.564, 4.446, 3.605‐fold change, respectively, between lymphoblastic
   leukemia and normal samples. (B) Genetic alterations in the ALL
   samples. About 0% to 2.1% of alterations for the 3 queried genes were
   calculated in the examined ALL studies. Genetic mutations of MYC and
   ATF3 were 2.1% and 0.7% respectively. No CXCL8 alteration was observed

3.7. Confirmation of C‐MYC by in vitro cell experiments

   For confirmation, we first evaluated cytotoxicity of dexamethasone in
   BALL‐1 and BALL‐1/ADR cells. Using MTT assay, we observed markedly
   lower dexamethasone sensitivity in BALL‐1/ADR cells than in the
   parental cells BALL‐1. The IC50 of dexamethasone in BALL‐1 cells and
   BALL‐1/ADR cells was 5.57 ± 1.32 μg/mL and 32.40 ± 1.33 μg/mL,
   respectively.

   Based on the MTT results, we examined C‐MYC expression at the mRNA and
   protein level using qRT‐PCR and western blot (Figure [117]6A,B). A
   significantly increased expression of C‐MYC mRNA and protein in
   BALL‐1/ADR cells was observed.

Figure 6.

   Figure 6
   [118]Open in a new tab

   Validation of C‐MYC. (A) quantitative real‐time‐PCR was performed to
   examine the expression of C‐MYC mRNA. Ribosomal RNA 18S was used as an
   internal control and for normalization of the data. Upregulation of
   C‐MYC mRNA was observed in BALL‐1/ADR cells, with 2^−∆∆Ct values equal
   to 3.23 ± 0.22, relative to parental control cells (2^−∆∆Ct equal to
   1). (B) Detection of the C‐MYC protein

   Taken together, using MTT, qRT‐PCR, and western blot assays, we
   validated the potential molecular mechanism of GC resistance in
   lymphoid tumor cells.

4. DISCUSSION

   Despite the reassuring achievements in the treatment of patients with
   ALL, resistance to GC agonists has been a major obstacle in eliminating
   leukemia cells and maximizing cure.[119]24 Several studies on GC
   resistance have elucidated that GC‐stimulated apoptosis is not induced
   in leukemia cells with poor response to GC agents.[120]25, [121]26
   Recently published articles partly expanded our understanding about the
   molecular mechanism of GC resistance, wherein the role of some
   molecules and signaling pathways including MLL rearrangement and
   JAK/SAT pathway alteration were determined. Nevertheless, limited by
   scattered information obtained from individual studies, a comprehensive
   internal mechanistic basis underlying the development of GC resistance
   remains to be uncovered.

   In this study, we generated an integrated, biologically functional
   landscape of GC resistance in ALL using bioinformatic analysis. First,
   a total of 99 common DEGs (52 upregulated and 47 downregulated) were
   identified between GC‐sensitive and ‐resistant samples by integrating
   two gene profile datasets from two different researches and analyzing
   the datasets with bioinformatic methods. Further, the 99 candidate DEGs
   were enriched and categorized into 3 groups: biological process,
   molecular functions, and cellular component terms using GO functional
   analysis. Finally, DEG‐related PPI network was expanded, and 98 nodes
   were identified with 127 edges, of which one node exhibited the highest
   connectivity in all edges and was regarded as the core gene in the PPI
   network.

   The upregulated DEGs were mainly enriched in the BP terms related to
   response to stimulus, positive regulation of biological process, and
   positive regulation of cellular process. The downregulated DEGs were
   instead mainly enriched in BP terms connected with negative regulation
   of transcription from RNA polymerase II promoter, defense response to
   bacterium, and innate immune response.

   Transcriptional misregulation in cancer (map05202 on KEGG database:
   [122]://www.genome.jp/kegg/pathway.html) pathway was one of the top
   pathways identified, which derived from multiple TF alterations,
   including genes fusion, amplification, mutation, deletion,
   rearrangements and translocation, resulting in various oncogenic
   processes such as differentiation resistance, inhibition of apoptosis,
   and cellular invasion. Chromosomal translocations and rearrangements of
   mixed lineage leukemia (MLL) gene, which is located at 11q23, are
   frequently represented as t (4;11) (q21; q23), t (11;19) (q23; p13.3)
   and t (9;11) (p22; q23), result in fusion proteins and subsequently
   lead to aberrant gene expression associated with leukemogenesis and
   chemotherapeutics resistance. Patients with ALL bearing
   MLL‐rearrangements show poor survival also due to cellular resistance
   to various chemotherapeutics, especially to GC, which is in accordance
   with the results of KEGG analysis.[123]27 Notably, CCND2 is upregulated
   and hyperactivated by the amplification of transcription factors with
   oncogene properties, and thereby accelerates cell cycle by promoting
   G1/S phase transformation and increasing cell proliferation, which is
   considered the critical oncogenesis process in leukemia and lymphoma.
   Real et al proposed that upregulation of CCND2 is involved in the
   protection against Gamma‐secretase inhibitors (GSIs), which are Notch
   pathway inhibitors used in GC‐resistant T‐cell acute lymphoblastic
   leukemia (T‐ALL).[124]28 Additionally, reliability of the top enriched
   pathway associated with GC resistance, Transcriptional misregulation in
   cancer, is further supported by studies reporting that BCL2A1, PAX5,
   and CXCL8 play important roles in GC‐resistant ALL.[125]29, [126]30
   Moreover, Pathways in cancer (map05200 on KEGG website) comprised
   various oncogenesis signaling pathways including MAPK, JAT/STAT, and
   PI3K‐AKT signaling pathways, which were confirmed to promote
   development of GC resistance in leukemia by multiple independent
   studies.[127]7, [128]8, [129]19, [130]31, [131]32 Collectively, results
   of enriched KEGG pathway analysis were positively correlated with
   experimental findings. However, further investigations are needed to
   explore and confirm the potentially significant pathways for GC
   resistance in ALL and to achieve a comprehensive understanding of this
   process.

   MYC, which was identified as the core gene in GC resistance‐associated
   PPI network and is the node that showed the highest connectivity
   degrees therein, is also a modulator of numerous cell processes by
   transcriptional regulation of its target genes. We did not perform
   survival analysis due to lack of data on ALL survival in the two gene
   expression profiles, in the TCGA, and in Oncomine database. However,
   the role of MYC in lymphoid malignancy was elucidated. MYC
   upregulation, caused by deregulated activity of MYC transcriptional
   network, enhances cancer proliferation and cellular drug resistance
   according to recent researches. Schubbert S et al revealed that
   targeting MYC and PI3K pathway could eliminate leukemia‐initiating
   cells in T‐ALL.[132]33 Moyo et al showed that MYC is overexpressed in
   pre‐malignant B cells with activation of PI3K/AKT signaling pathway.
   Moreover, MYC overexpression promoted resistance to Btk inhibition in B
   cell malignancy.[133]34 Notably, the GC resistance‐promoting role of
   MYC in ALL was firstly elucidated by Renner K.[134]35 Han et al
   revealed that MYC displays constitutive activation in B‐ALL cell lines,
   especially GC‐resistant B‐ALL, indicating the critical role of MYC in
   evolution of GC resistance.[135]26 The positive impact of MYC on
   progression of GC resistance in ALL was subsequently strengthened by
   other studies.[136]36, [137]37 Hence, the results of PPI network were
   in accordance with experimental results on GC resistance in ALL. The
   role of multiple genes in the network were determined, such as MYC,
   MCL1 and CCND2, which expanded our knowledge on development of GC
   resistance in ALL.

   In this study, to validate the reliability and applicability of
   bioinformatics analysis, we investigated C‐MYC in BALL‐1 and BALL‐1/ADR
   cells, which displayed dexamethasone sensitivity and resistance,
   respectively. Increased expression of C‐MYC was observed in BALL‐1/ADR
   cells, which is consistent with bioinformatics analysis results.

   In summary, bioinformatics analysis was used to identify the biological
   networks and in vitro experiments were performed to verify the
   bioinformatics findings. Nevertheless, validation in a larger group of
   patients and in‐depth experiments in vivo and in vitro are needed to
   improve and examine the accuracy of the bioinformatics analysis.

   Taken together, the bioinformatic analysis employed here proved to be a
   convenient and valid tool that offered us a novel and systematic
   insight into the mechanism of GC resistance in ALL. Several pathways
   were identified which are potentially involved in GC resistance in ALL.
   Importantly, our data revealed a comprehensive interaction network
   which highlighted MYC as a hub gene among various candidate genes. Our
   findings integrate individual experimental results from previous
   studies to illustrate the landscape of biological processes that lead
   to acquisition of GC resistance. Based on these results, novel
   treatment opportunities and therapeutic targets can be envisaged.

CONFLICT OF INTERESTS

   There are no conflict of interest to disclose.

AUTHORS' CONTRIBUTION

   Jianda Hu, Yanxin Chen conceived and designed the experiment; Peifang
   Jiang, Jingjing Wen, Donghui Gan and Yingyu Chen operated the
   experiment, Jiazheng Li, Yuwen Chen, Ting Yang analyzed the data,
   Yanxin Chen, Liangyan Wang, Zhengjun Wu and Jianda Hu wrote the paper.
   Ting Yang, Minhui Lin and Jianda Hu reviewed the paper.

ETHICS APPROVAL AND CONSENT TO PARTICIPATE

   This article does not contain any studies with human participants or
   animals performed by any of the authors.

Supporting information


   [138]Click here for additional data file.^ (1.2MB, pdf)


   [139]Click here for additional data file.^ (49.2KB, docx)

   Chen Y, Jiang P, Wen J, et al. Integrated bioinformatics analysis of
   the crucial candidate genes and pathways associated with glucocorticoid
   resistance in acute lymphoblastic leukemia. Cancer Med.
   2020;9:2918–2929. 10.1002/cam4.2934

   Yanxin Chen, Peifang Jiang, and Jingjing Wen are contributed equally to
   this work.

   Funding information

   This work was supported by National Natural Science Foundation of China
   (81870135), Construction Project of Fujian Medical CENTER of Hematology
   (Min201704), Special Fund from Fujian Provincial Department of Finance
   (2016B041), the Cooperation Project of University and Industry in
   Fujian Province (2017Y4005), Joint Research Project of Health and
   Education (WKJ2016‐2‐06), Joint Funds for the Innovation of Science and
   Technology, Fujian province (2016Y9029, 2016Y9032) and National Key R＆D
   Program of China (2016YFC0902800).

Contributor Information

   Minhui Lin, Email: linminhui959@163.com.

   Jianda Hu, Email: drjiandahu@163.com.

DATA AVAILABILITY STATEMENT

   The data that support the findings of this study are available from the
   corresponding author upon reasonable request.

REFERENCES