Bioinformatics analysis of the CDK2 functions in neuroblastoma

  • Authors:
    • Lijuan Bo
    • Bo Wei
    • Zhanfeng Wang
    • Daliang Kong
    • Zheng Gao
    • Zhuang Miao
  • View Affiliations

  • Published online on: December 29, 2017     https://doi.org/10.3892/mmr.2017.8368
  • Pages: 3951-3959
Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )
Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )


Abstract

The present study aimed to elucidate the potential mechanism of cyclin-dependent kinase 2 (CDK2) in neuroblastoma progression and to identify the candidate genes associated with neuroblastoma with CDK2 silencing. The microarray data of GSE16480 were obtained from the gene expression omnibus database. This dataset contained 15 samples: Neuroblastoma cell line IMR32 transfected with CDK2 shRNA at 0, 8, 24, 48 and 72 h (n=3 per group; total=15). Significant clusters associated with differentially expressed genes (DEGs) were identified using fuzzy C‑Means algorithm in the Mfuzz package. Gene ontology and pathway enrichment analysis of DEGs in each cluster were performed, and a protein‑protein interaction (PPI) network was constructed. Additionally, functional annotation of DEGs in clusters was performed for the detection of transcription factors and tumor‑associated genes. A total of 4 clusters with significant change tendency and 1,683 DEGs were identified. The hub nodes of the PPI network constructed by DEGs in Cluster 1, Cluster 2, Cluster 3 and Cluster 4 were MDM2 oncogene, E3 ubiquitin protein ligase (MDM2), cyclin‑dependent kinase 1 (CDK1), proteasome (prosome, macropain) 26S subunit, non‑ATPase, 14 (PSMD14) and translocator protein (18 kDa) (TSPO) respectively. These genes were significantly enriched in the p53 signaling pathway, cell cycle, proteasome and systemic lupus erythematosus pathways. MDM2, CDK1, PSMD14 and TSPO may be key target genes of CDK2. CDK2 may have key functions in neuroblastoma progression by regulating the expression of these genes.

Introduction

Neuroblastoma is an embryonal tumor that arises from the sympathetic nervous system, accounts for ~15% of childhood cancer mortality (1,2). Despite intensive myeloablative chemotherapy, survival rates for neuroblastoma have not substantively improved; relapse is common and frequently leads to mortality (3,4). Like most human cancers, this childhood cancer can be inherited; however, the genetic aetiology remains to be elucidated (3). Therefore, an improved understanding of the genetics and biology of neuroblastoma may contribute to further cancer therapies.

In terms of genetics, neuroblastoma tumors from patients with aggressive phenotypes often exhibit significant MYCN proto-oncogene, bHLH transcription factor (MYCN) amplification and are strongly associated with a poor prognosis (5). MYCN, a member of MYC proto-oncogene family, functions as a transcriptional factor, which controls cell growth and proliferation and thus has an important role in driving tumorigenesis of neuroblastoma cells (6,7). Additionally, the overexpression of MYC genes in non-MYC-amplified cells may induce apoptosis (8). A previous study by Molenaar et al (9) confirmed that inactivation of cyclin-dependent kinase 2 (CDK2) was synthetically lethal to neuroblastoma cells with MYCN-amplification and overexpression (9). The CDK2 gene encodes a protein that is member of serine/threonine protein kinase family that is involved in cell cycle regulation (10). Additionally, CDK2 has been demonstrated to regulate the progression through the cell cycle (11). A previous study also has determined that the targeting of aberrant cell cycle checkpoints in cancer cells may inhibit tumor growth and induce cell death (12). CDK2 is a vital regulator of S-phase progression and is deemed to be an anticancer drug target (9,13). Additionally, CDK2 inhibitors may act as potential MYCN-selective cancer therapeutics in the treatment of neuroblastoma (9). However, the molecular mechanism of CDK2 in the genesis of childhood cancer neuroblastoma remains to be fully elucidated.

In a previous study, microarray data from GSE16480 was used for identification of the upregulated genes following CDK2 silencing. The findings revealed that these upregulated genes were target genes of p53, and silencing of p53 protected the cells from MYCN-driven apoptosis (9). However, to the best of our knowledge, there was no systematic and comprehensive analysis for this expression profile. The present study downloaded the microarray data of GSE16480 and then identified significant clusters associated with differentially expressed genes (DEGs) following CDK2 silencing. Gene ontology (GO) and pathway enrichment analysis of DEGs in each cluster were performed, and protein-protein interaction (PPI) network was constructed. Additionally, a functional annotation of DEGs in the clusters was performed. The present study aimed to identify key genes and biological pathways underlying the progression of neuroblastoma with CDK2 silencing by means of comprehensive bioinformatics analysis to further elucidate the function of CDK2 in neuroblastoma progression and determine potential targets for future cancer therapies.

Materials and methods

Source of data

The microarray data GSE16480 was downloaded from Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) database based on the platform of GPL570 (Affymetrix Human Genome U133 Plus 2.0 Array), which was deposited by Molenaar et al (9). This dataset contained 15 samples: Neuroblastoma cell line IMR32 was transfected with CDK2 shRNA at 0, 8, 24, 48 and 72 h (n=3 per group; total=15).

Data preprocessing

Background correction, quartile normalization and probe summarization were performed to normalize the gene expression intensities obtained from the raw dataset using robust multi-array average algorithm (14), and the gene expression time-course matrix of samples was acquired.

Soft clustering analysis

Noise robust soft clustering of gene expression time-course data was implemented using the fuzzy C-Means algorithm (15) in the Mfuzz package (15,16). The following parameters were set: Minimum Standard Deviation=0.4, score=0.7. This method may assign genes into several clusters according to the expression pattern of DEGs. Then, clusters with significant change tendency were screened for further analysis.

PPI network construction

The Search Tool for the Retrieval of Interacting Genes (STRING) database (17) is a database for the exploration and analysis of known and predicted protein interactions, including both experimental and predicted interaction information. The present study used the STRING online tool to analyze the PPIs of up and downregulated genes with required confidence (combined score) >0.4. The hub proteins were subsequently identified from the PPI network based on connectivity degree analysis.

GO and pathway enrichment analysis

GO (18) is widely used for the studies of large-scale genomic or transcriptomic data in function. Kyoto Encyclopedia of Genes and Genomes (KEGG) (19) is an online pathway database, which deals with genomes, enzymatic pathways and biological chemicals. The present study identified over-represented GO categories in biological processes and significant KEGG pathways of the DEGs in each cluster. The P-value of the default hypergeometric test of >0.05 was selected as the threshold.

Functional annotation of DEGs in each cluster

The tumor suppressor gene (TSGene) (20) database provides detailed annotation for each tumor suppressor gene (TSGs), such as transcription factors (TF) regulations. The tumor-associated gene (TAG) database (21) summarizes attributes for a specific entity associated with the TAGs.

Functional annotations of DEGs in clusters were performed for the detection of TFs and TAGs and both databases, TSGene and TAG database, were used to identify oncogenes and tumor suppressor genes.

Results

Soft clustering analysis

Soft clustering analysis of gene expression time-course data identified 4 clusters with significant change tendency (Fig. 1). Cluster 1 presented an increasing trend (Fig. 1A). Specifically, the expression levels of genes exhibited an increase from 0 to 8 h; subsequently the levels increased significantly from 8 to 48 h and remained constant from 48 to 72 h. It is of note that the change tendency of gene expression in Cluster 3 (Fig. 1C) at different time points is evidently opposite to those observed in Cluster 1. The expression levels of genes in Cluster 3 decreased slightly from 0 to 8 h, subsequently the levels decreased significantly from 8 to 48 h and remained constant from 48 to 72 h. In addition, the change tendency of gene expression in Cluster 2 (Fig. 1B) at different time points was evidently opposite to those observed in Cluster 4 (Fig. 1D). The expression levels of genes in Cluster 2 decreased from 0 to 24 h and subsequently decreased significantly from 24 to 72 h, whereas in Cluster 4 this trend was reversed.

Additionally, DEGs with the same expression pattern as change tendency of clusters was screened. A total of 1,683 DEGs were identified, including 337 upregulated genes in Cluster 1, 649 downregulated genes in Cluster 2, 387 downregulated genes in Cluster 3, and 387 upregulated genes in Cluster 4.

PPI network construction

The PPI networks of DEGs in Cluster 1 (Fig. 2A), 2 (Fig. 2B), 3 (Fig. 2C) and 4 (Fig. 2D) included 86, 18,875, 239 and 109 interactions, respectively. Based on connectivity degree, the hub genes with the highest degrees in the four clusters were: MDM2 oncogene, E3 ubiquitin protein ligase (MDM2), cyclin-dependent kinase 1 (CDK1), proteasome (prosome, macropain) 26S subunit, non-ATPase, 14 (PSMD14), translocator protein (18 kDa) (TSPO), respectively (Table I).

Table I.

Top 10 nodes in the protein-protein interaction network.

Table I.

Top 10 nodes in the protein-protein interaction network.

A, Cluster 1

GeneDegree
MDM210
CDKN1A  8
TNFRSF10B  5
CRB1  4
MPDZ  4
NTPCR  4
RAD50  4
RTN1  4
ABCA1  3
ADCY6  3

B, Cluster 2

GeneDegree

CDK1253
MAD2L1251
RFC4248
BUB1241
NCAPG239
CCNA2238
CHEK1236
CCNB1233
NDC80232
PBK232

C, Cluster 3

GeneDegree

PSMD1432
PSMC223
PSMD1017
PSMD715
PSMC114
PSMD1114
ADRM114
BLMH13
PSMD313
PSMD113

D, Cluster 4

GeneDegree

TSPO17
VEGFA11
RET  9
CDH2  8
SHC1  6
NRP1  6
CXCR4  5
HDAC9  5
SCG2  4
CHGB  4
GO and pathway enrichment analysis

The present study performed GO and KEGG pathway analysis for DEGs in 4 clusters. The over-represented GO terms of DEGs in Cluster 1, 2, 3 and 4 were response to DNA damage stimulus, cell cycle, antigen processing and presentation of peptide antigen via MHC class I, and cell surface receptor signaling pathway, respectively (Table II). The significantly enriched KEGG pathways of cluster genes in Cluster 1, 2, 3 and 4 were the p53 signaling pathway, cell cycle, proteasome, and systemic lupus erythematosus, respectively (Table III).

Table II.

The top 10 enriched GO-biological process terms of cluster genes.

Table II.

The top 10 enriched GO-biological process terms of cluster genes.

A, Cluster 1

IDDescriptionCountP-value
GO:0006974Response to DNA damage stimulus15 2.71×10−6
GO:0048699Generation of neurons18 4.06×10−5
GO:0009411Response to UV  6 2.49×10−5
GO:0097202Activation of cysteine-type endopeptidase activity  5 1.39×10−4
GO:0051050Positive regulation of transport11 1.40×10−4
GO:0032270Positive regulation of cellular protein metabolic process14 2.35×10−4
GO:0007267Cell-cell signaling16 3.86×10−4
GO:0045937Positive regulation of phosphate metabolic process11 1.30×10−3
GO:0050877Neurological system process16 1.56×10−3
GO:0051146Striated muscle cell differentiation  6 3.94×10−3

B, Cluster 2

ID DescriptionCountsP-value

GO:0007049Cell cycle2310
GO:0006281DNA repair  810
GO:0006260DNA replication  720
GO:0006310DNA recombination  510
GO:0000082G1/S transition of mitotic cell cycle  410
GO:0000075Cell cycle checkpoint  380
GO:0007051Spindle organization  350
GO:0007126Meiosis  320
GO:0007088Regulation of mitosis  260
GO:0000086G2/M transition of mitotic cell cycle  28 2.22×10−16

C, Cluster 3

ID DescriptionCountP-value

GO:0002474Antigen processing and presentation of peptide antigen via MHC class I13 3.21×10−13
GO:0006521Regulation of cellular amino acid metabolic process11 3.08×10−13
GO:0006977DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest11 1.38×10−12
GO:0031145Anaphase-promoting complex-dependent proteasomal ubiquitin-dependent protein catabolic process11 1.88×10−11
GO:0000209Protein polyubiquitination14 2.32×10−11
GO:0016071mRNA metabolic process19 1.07×10−7
GO:0043248Proteasome assembly  3 2.04×10−5
GO:0006406mRNA export from nucleus  4 1.35×10−3
GO:0006369Termination of RNA polymerase II transcription  3 4.53×10−3
GO:0006446Regulation of translational initiation  3 1.18×10−2

D, Cluster 4
ID DescriptionCountP-value

GO:0007166Cell surface receptor signaling pathway38 1.79×10−8
GO:0001525Angiogenesis12 2.81×10−6
GO:0045773Positive regulation of axon extension  4 8.88×10−6
GO:0009968Negative regulation of signal transduction15 3.97×10−5

D, Cluster 4

ID DescriptionCountP-value

GO:0006334Nucleosome assembly  6 5.21×10−5
GO:0040013Negative regulation of locomotion  6 4.04×10−4
GO:0001657Ureteric bud development  5 4.23×10−4
GO:0042391Regulation of membrane potential  8 5.87×10−4
GO:0090090Negative regulation of canonical Wnt receptor signaling pathway  4 1.60×10−3
GO:0071495Cellular response to endogenous stimulus12 3.16×10−3

[i] GO, gene ontology.

Table III.

Enriched KEGG pathways of cluster genes.

Table III.

Enriched KEGG pathways of cluster genes.

A, Cluster 1

IDDescriptionCountP-value
115p53 signaling pathway11 1.24×10−12
5200Pathways in cancer  7 9.90×10−3
4510Focal adhesion  6 3.48×10−3
230Purine metabolism  5 6.85×10−3
5214Glioma  4 1.33×10−3
5218Melanoma  4 1.85×10−3
4210Apoptosis  4 3.89×10−3
5215Prostate cancer  4 4.22×10−3
5210Colorectal cancer  3 1.09×10−2
5220Chronic myeloid leukemia  3 1.70×10−2
4512ECM-receptor interaction  3 2.53×10−2
4012ErbB signaling pathway  3 2.69×10−2
240Pyrimidine metabolism  3 3.74×10−2
5219Bladder cancer  2 3.91×10−2
3420Nucleotide excision repair  2 4.26×10−2

B, Cluster 2

ID DescriptionCountP-value

4110Cell cycle380
3030DNA replication230
4114Oocyte meiosis19 6.61×10−10
230Purine metabolism16 2.72×10−5
240Pyrimidine metabolism14 1.33×10−6
3430Mismatch repair11 1.44×10−11
3420Nucleotide excision repair11 4.70×10−8
4914 Progesterone-mediated oocyte maturation11 4.95×10−5
3410Base excision repair10 2.58×10−8
3440Homologous recombination  9 7.45×10−8
4115p53 signaling pathway  9 1.87×10−4
3018RNA degradation  6 2.01×10−2
310Lysine degradation  5 1.01×10−2
790Folate biosynthesis  2 4.20×10−2

C, Cluster 3

ID DescriptionCountP-value

3050Proteasome10 7.56×10−12
3013RNA transport  6 3.52×10−3
3030DNA replication  3 5.19×10−3
4141Protein processing in endoplasmic reticulum  6 5.44×10−3
3410Base excision repair  2 4.17×10−2
270Cysteine and methionine metabolism  2 4.88×10−2

D, Cluster 4

ID DescriptionCountP-value

5322Systemic lupus erythematosus7 7.73×10−5
4144Endocytosis6 4.47×10−3
4360Axon guidance5 3.16×10−3
4514Cell adhesion molecules (CAMs)5 3.60×10−3
5100Bacterial invasion of epithelial cells3 1.71×10−2
360Phenylalanine metabolism2 7.58×10−3
260Glycine, serine and threonine metabolism2 2.57×10−2
350Tyrosine metabolism2 4.06×10−2
5219Bladder cancer2 4.24×10−2

[i] KEGG, Kyoto Encyclopedia of Genes and Genomes.

Functional annotation of DEGs in each cluster

As presented in Table IV the present study revealed that with increased time 5 TFs and 13 TAGs in Cluster 1 were upregulated, 17 TFs and 49 TAGs in Cluster 2 were downregulated, 3 TFs and 3 TAGs in Cluster 3 were downregulated, 3 TFs and 15 TAGs in Cluster 4 were upregulated.

Table IV.

The functional annotation of cluster genes in PPI network.

Table IV.

The functional annotation of cluster genes in PPI network.

Cluster no.TFTS genesOncogeneOther
Cluster 1HAND2, RFX5, HOXD10, PBX1, ISL1CDKN1A, ISG15, TNFRSF10B, HBP1, YPEL3, FBXW7, BTG2IRF2, MDM2, AKT3, PDGFRB, PBX1BAX
Cluster 2HMGB2, BRCA1, BRIP1, EZH2, MYBL2, SSRP1, FOXM1, E2F7, HMGB1, BRCA2, TEAD1, TAF5, RBL1, GLI3, GTF2A1, MYBL1, LHX2CHEK1, RBBP7, BRCA1, BARD1, BLM, FANCD2, BUB1B, CHEK2, FANCG, E2F1, LPL, GADD45G, BRCA2, RBL1, CDK2AP1, MLH1, ANP32A, LOX, RBM14, FH, CDKN2CPTTG1, AURKA, CEP55, CCNA2, WHSC1, MYBL2, DEK, HMMR, CSE1L, RRAS, MYB, FGFR2, TEAD1, KIT, NTRK1, MYBL1DNMT3B, BUB1, BIRC5, TACC3, PLK1, CCNE2, RAD54B, YEATS4, ANP32B, ANXA5, NIF3L1, LHX2
Cluster 3TFAM, KLF4, ID1TIMP3, IGFBP5 KLF4
Cluster 4SOX9, MEIS1, ETS2SPRY2, LGI1, BAI3, NRCAM, BLCAP, DUSP6, PTPRK, SIRT2, PRICKLE1VEGFA, RET, MEIS1, ETS2SHC1, CBLB

[i] TF, transcription factor; TS, tumor suppressor.

Discussion

The present study identified significant DEGs in a neuroblastoma cell line with CDK2 silencing, including MDM2, CDK1, PSMD14 and TSPO. The genes with higher degrees in the PPI network were significantly enriched in the p53 signaling pathway, cell cycle and proteasome.

MDM2 with the highest connectivity degrees in Cluster 1 was significantly upregulated in the neuroblastoma samples. The MDM2 gene encodes a nuclear-localized E3 ubiquitin ligase, which is a critical effector of the MYCN oncogene in tumorigenesis and is a transcriptional target of MYCN in neuroblastoma (7,22). Elevated MDM2 levels increase MYCN-induced genomic instability via regulating centrosome replication in the neuroblastoma (23). In addition, MDM2 may bind to p53 at its transactivation domain with high affinity for negatively modulating its transcriptional activity and stability (24). A previous study favored the idea that the MDM2-p53 interaction was effectively involved in cellular processes via the p53 pathway (25). The p53 signaling pathway and its inactivation has a key regulatory role in neuroblastoma progression (26). Additionally, phosphorylation of MdmX by CDK2/Cdc2p34 effectively regulates the nuclear export of MDM2, and thus has an important role in the regulation of p53 transcription and stability (27). Inhibition of p53-mediated apoptosis is a prerequisite for MYC-driven tumorigenesis in neuroblastoma (7). This may be the reason behind the upregulated expression of MDM2 in neuroblastoma cells following CDK2 silencing. In the current study, MDM2 was significantly enriched in the p53 signaling pathway. Therefore, the findings of the current study suggest that MDM2 may function as an oncogene for promoting neuroblastoma progression via the p53 signaling pathway, and CDK2 may inhibit MYC-driven tumorigenesis in neuroblastoma by targeting MDM2 and activating the p53 signaling pathway.

PSMD14 is the hub gene in Cluster 3 with the higher degrees. This gene encodes a component of the 26S proteasome, which catalyzes the degradation of ubiquitinated intracellular proteins (28). The 26S proteasome may mediate the degradation of N-myc in neuroblastoma cells in vivo (29). Increased expression of the proteasome has an important role in the protective effects of sulforaphane against hydrogen peroxide-mediated cytotoxicity in neuroblastoma cells (30). Additionally, a PSMD14 knockdown may restore sensitivity of Mcl1-dependent neuroblastoma to ABT-737 (a small molecule inhibitor of Bcl2, BclXL and BclW), thus decreasing the activity of Bcl2, BclXL and BclW (31). Bcl2 family proteins have important roles in neutralizing activated BCL2 like 11 and evading apoptosis in neuroblastoma cells (32,33). Therefore, the findings of the current study suggest that PSMD14 may contribute to neuroblastoma progression via the proteasome. It is of note that the clustering analysis performed in the current study revealed that the expression pattern of genes in Cluster 3 at different time points was evidently opposite to the one observed in Cluster 1. Therefore, it is possible that a synthetic suppression effect occurred between these genes in Cluster 3 and Cluster 1 to some extent. Liang et al (34) demonstrated that downregulation of PSMD14 was involved in the activation of p53-regulated pro-apoptotic signaling pathways and the activity of p53 was associated with MDM2 expression (34). Additionally, p53 regulates the expression of cyclin dependent kinase inhibitor 1A, which mediates the p53-dependent cell cycle arrest at the G1 phase via binding and thus inhibiting the activity of CDK2. Therefore, the findings of the present study also suggest that CDK2 may have a key role in neuroblastoma progression by regulating the expression of p53, which may be due to the synthetically lethal relationship between MDM2 and PSMD14.

CDK1 has an important role in cell cycle regulation by governing the transition from G2 to M phase and cell cycle regulation is important for cell proliferation (35,36). The CDK1 inhibitors induce G2 arrest in various cell types and effectively downregulate the expression of MYCN, which in turn reduce the transcriptional activation of MYCN on the survivin promoter in neuroblastoma cells (37). In the present study, CDK1 was significantly involved in cell cycle. Therefore, CDK1 may be involved in neuroblastoma progression through the cell cycle. However, previous studies have confirmed that CDK1 alone is sufficient to drive the mammalian cell cycle and the genetic ablation of CDK2 may be compensated for by CDK1 (38,39). A previous study determined that despite CDK2 inhibition, the proliferation of cancer cells was due to the expression of CDK1 to some extent (39). In the current study, the expression of CDK1 was downregulated following CDK2 silencing; therefore, it is possible for CDK2 to contribute to neuroblastoma progression via regulation of CDK1 expression.

TSPO is a transmembrane protein associated with the mitochondrial permeability pore, mitochondrial transport has an important role in the initiation of the apoptotic cascade (40). A previous study revealed that TSPO ligands are capable of inducing apoptosis in various types of cancers, such as hepatocellular carcinoma, colorectal cancer, esophageal cancer and glioma (41). TSPO ligand PK11195 induces apoptosis and leads to cell cycle arrest in neuroblastoma cell lines at micromolar concentrations (42). Therefore, TSPO may induce apoptosis in neuroblastoma cells and is involved in cell cycle. However, with CDK2 silencing, the expression of TSPO has been observed to be upregulated. Therefore, CDK2 may promote neuroblastoma progression by reducing TSPO expression. Due to the effect of synthetic suppression observed between TSPO and CDK1 in the present study, it is possible that CDK2 may be involved in neuroblastoma progression via regulation of the interaction of TSPO and CDK1 in the cell cycle.

However, the relatively small sample size is a limitation of the current study. In addition, there is no experimental evaluation of the present study. Additional experiments, such as expression validation or knockdown assay are required to confirm the current observations.

In conclusion, MDM2, CDK1, PSMD14 and TSPO may be key target genes of CDK2, and CDK2 may play an important role in neuroblastoma progression by targeting these genes. MDM2 may function as an oncogene that promotes neuroblastoma tumorigenesis via the p53 signaling pathway. PSMD14 may allow neuroblastoma cells to evade apoptosis in via proteasome. TSPO and CDK1 may be involved in neuroblastoma progression by regulating the cell cycle. CDK2 may promote neuroblastoma progression by regulating the expression of MDM2, PSMD14, CDK1 and TSPO.

Acknowledgements

The present study was supported by The Natural Science Foundation of Jilin Province (grant nos. 20150204086SF and 20150101160JC) and The High Technology Research and Development Program of Jilin Province of China (grant nos: 2015Y032-4 and 2014G074).

References

1 

Reck M, Popat S, Reinmuth N, De Ruysscher D, Kerr KM and Peters S; ESMO Guidelines Working Group, : Metastatic non-small-cell lung cancer (NSCLC): ESMO clinical practice guidelines for diagnosis, treatment and follow-up. Ann Oncol. 3 Suppl 25:iii27–iii39. 2014. View Article : Google Scholar

2 

George RE, Sanda T, Hanna M, Fröhling S, Luther W II, Zhang J, Ahn Y, Zhou W, London WB, McGrady P, et al: Activating mutations in ALK provide a therapeutic target in neuroblastoma. Nature. 455:975–978. 2008. View Article : Google Scholar : PubMed/NCBI

3 

Mossé YP, Laudenslager M, Longo L, Cole KA, Wood A, Attiyeh EF, Laquaglia MJ, Sennett R, Lynch JE, Perri P, et al: Identification of ALK as a major familial neuroblastoma predisposition gene. Nature. 455:930–935. 2008. View Article : Google Scholar : PubMed/NCBI

4 

George RE, Li S, Medeiros-Nancarrow C, Neuberg D, Marcus K, Shamberger RC, Pulsipher M, Grupp SA and Diller L: High-risk neuroblastoma treated with tandem autologous peripheral-blood stem cell-supported transplantation: Long-term survival update. J Clin Oncol. 24:2891–2896. 2006. View Article : Google Scholar : PubMed/NCBI

5 

Schwab M, Varmus HE, Bishop JM, Grzeschik KH, Naylor SL, Sakaguchi AY, Brodeur G and Trent J: Chromosome localization in normal human cells and neuroblastomas of a gene related to c-myc. Nature. 308:288–291. 1984. View Article : Google Scholar : PubMed/NCBI

6 

Cole MD and Cowling VH: Transcription-independent functions of MYC: Regulation of translation and DNA replication. Nat Rev Mol Cell Biol. 9:810–815. 2008. View Article : Google Scholar : PubMed/NCBI

7 

Slack A, Chen Z, Tonelli R, Pule M, Hunt L, Pession A and Shohet JM: The p53 regulatory gene MDM2 is a direct transcriptional target of MYCN in neuroblastoma. Proc Natl Acad Sci USA. 102:pp. 731–736. 2005; View Article : Google Scholar : PubMed/NCBI

8 

Meyer N, Kim SS and Penn LZ: The Oscar-worthy role of Myc in apoptosis. Semin Cancer Biol. 16:275–287. 2006. View Article : Google Scholar : PubMed/NCBI

9 

Molenaar JJ, Ebus ME, Geerts D, Koster J, Lamers F, Valentijn LJ, Westerhout EM, Versteeg R and Caron HN: Inactivation of CDK2 is synthetically lethal to MYCN over-expressing cancer cells. Proc Natl Acad Sci USA. 106:pp. 12968–12973. 2009; View Article : Google Scholar : PubMed/NCBI

10 

Vermeulen K, Van Bockstaele DR and Berneman ZN: The cell cycle: A review of regulation, deregulation and therapeutic targets in cancer. Cell Prolif. 36:131–149. 2003. View Article : Google Scholar : PubMed/NCBI

11 

Malumbres M and Barbacid M: Mammalian cyclin-dependent kinases. Trends Biochem Sci. 30:630–641. 2005. View Article : Google Scholar : PubMed/NCBI

12 

Shapiro GI: Cyclin-dependent kinase pathways as targets for cancer treatment. J Clin Oncol. 24:1770–1783. 2006. View Article : Google Scholar : PubMed/NCBI

13 

Hall C, Nelson DM, Ye X, Baker K, DeCaprio JA, Seeholzer S, Lipinski M and Adams PD: HIRA, the human homologue of yeast Hir1p and Hir2p, is a novel cyclin-cdk2 substrate whose expression blocks S-phase progression. Mol Cell Biol. 21:1854–1865. 2001. View Article : Google Scholar : PubMed/NCBI

14 

Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U and Speed TP: Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics. 4:249–264. 2003. View Article : Google Scholar : PubMed/NCBI

15 

Futschik ME and Carlisle B: Noise-robust soft clustering of gene expression time-course data. J Bioinform Comput Biol. 3:965–988. 2005. View Article : Google Scholar : PubMed/NCBI

16 

Kumar L and Futschik E M: Mfuzz: A software package for soft clustering of microarray data. Bioinformation. 2:5–7. 2007. View Article : Google Scholar : PubMed/NCBI

17 

von Mering C, Huynen M, Jaeggi D, Schmidt S, Bork P and Snel B: STRING: A database of predicted functional associations between proteins. Nucleic Acids Res. 31:258–261. 2003. View Article : Google Scholar : PubMed/NCBI

18 

Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, et al: Gene ontology: Tool for the unification of biology. The gene ontology consortium. Nat Genet. 25:25–29. 2000. View Article : Google Scholar : PubMed/NCBI

19 

Kanehisa M and Goto S: KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28:27–30. 2000. View Article : Google Scholar : PubMed/NCBI

20 

Zhao M, Sun J and Zhao Z: TSGene: A web resource for tumor suppressor genes. Nucleic Acids Res. 41(Database Issue): D970–D976. 2013. View Article : Google Scholar : PubMed/NCBI

21 

Chen JS, Hung WS, Chan HH, Tsai SJ and Sun HS: In silico identification of oncogenic potential of fyn-related kinase in hepatocellular carcinoma. Bioinformatics. 29:420–427. 2013. View Article : Google Scholar : PubMed/NCBI

22 

Slack A and Shohet JM: MDM2 as a critical effector of the MYCN oncogene in tumorigenesis. Cell Cycle. 4:857–860. 2005. View Article : Google Scholar : PubMed/NCBI

23 

Slack AD, Chen Z, Ludwig AD, Hicks J and Shohet JM: MYCN-directed centrosome amplification requires MDM2-mediated suppression of p53 activity in neuroblastoma cells. Cancer Res. 67:2448–2455. 2007. View Article : Google Scholar : PubMed/NCBI

24 

Vassilev LT, Vu BT, Graves B, Carvajal D, Podlaski F, Filipovic Z, Kong N, Kammlott U, Lukacs C, Klein C, et al: In vivo activation of the p53 pathway by small-molecule antagonists of MDM2. Science. 303:844–848. 2004. View Article : Google Scholar : PubMed/NCBI

25 

Meek DW and Knippschild U: Posttranslational modification of MDM2. Mol Cancer Res. 1:1017–1026. 2003.PubMed/NCBI

26 

Tweddle DA, Pearson AD, Haber M, Norris MD, Xue C, Flemming C and Lunec J: The p53 pathway and its inactivation in neuroblastoma. Cancer Lett. 197:93–98. 2003. View Article : Google Scholar : PubMed/NCBI

27 

Elias B, Laine A and Ronai Z: Phosphorylation of MdmX by CDK2/Cdc2(p34) is required for nuclear export of Mdm2. Oncogene. 24:2574–2579. 2005. View Article : Google Scholar : PubMed/NCBI

28 

Tanaka K: The proteasome: Overview of structure and functions. Proc Jpn Acad Ser B Phys Biol Sci. 85:pp. 12–36. 2009; View Article : Google Scholar : PubMed/NCBI

29 

Bonvini P, Nguyen P, Trepel J and Neckers LM: In vivo degradation of N-myc in neuroblastoma cells is mediated by the 26S proteasome. Oncogene. 16:1131–1139. 1998. View Article : Google Scholar : PubMed/NCBI

30 

Kwak MK, Cho JM, Huang B, Shin S and Kensler TW: Role of increased expression of the proteasome in the protective effects of sulforaphane against hydrogen peroxide-mediated cytotoxicity in murine neuroblastoma cells. Free Radic Biol Med. 43:809–817. 2007. View Article : Google Scholar : PubMed/NCBI

31 

Laetsch T, Liu X, Vu A, Sliozberg M, Vido M, Elci OU, Goldsmith KC and Hogarty MD: Multiple components of the spliceosome regulate Mcl1 activity in neuroblastoma. Cell Death Dis. 5:e10722014. View Article : Google Scholar : PubMed/NCBI

32 

Goldsmith KC, Lestini BJ, Gross M, Ip L, Bhumbla A, Zhang X, Zhao H, Liu X and Hogarty MD: BH3 response profiles from neuroblastoma mitochondria predict activity of small molecule Bcl-2 family antagonists. Cell Death Differ. 17:872–882. 2010. View Article : Google Scholar : PubMed/NCBI

33 

Goldsmith KC, Gross M, Peirce S, Luyindula D, Liu X, Vu A, Sliozberg M, Guo R, Zhao H, Reynolds CP and Hogarty MD: Mitochondrial Bcl-2 family dynamics define therapy response and resistance in neuroblastoma. Cancer Res. 72:2565–2577. 2012. View Article : Google Scholar : PubMed/NCBI

34 

Liang J, Parchaliuk D, Medina S, Sorensen G, Landry L, Huang S, Wang M, Kong Q and Booth SA: Activation of p53-regulated pro-apoptotic signaling pathways in PrP-mediated myopathy. BMC Genomics. 10:2012009. View Article : Google Scholar : PubMed/NCBI

35 

Goga A, Yang D, Tward AD, Morgan DO and Bishop JM: Inhibition of CDK1 as a potential therapy for tumors over-expressing MYC. Nat Med. 13:820–827. 2007. View Article : Google Scholar : PubMed/NCBI

36 

Bettayeb K, Oumata N, Echalier A, Ferandin Y, Endicott JA, Galons H and Meijer L: CR8, a potent and selective, roscovitine-derived inhibitor of cyclin-dependent kinases. Oncogene. 27:5797–5807. 2008. View Article : Google Scholar : PubMed/NCBI

37 

Chen Y, Tsai YH and Tseng SH: Inhibition of cyclin-dependent kinase 1-induced cell death in neuroblastoma cells through the microRNA-34a-MYCN-survivin pathway. Surgery. 153:4–16. 2013. View Article : Google Scholar : PubMed/NCBI

38 

Santamaría D, Barrière C, Cerqueira A, Hunt S, Tardy C, Newton K, Cáceres JF, Dubus P, Malumbres M and Barbacid M: Cdk1 is sufficient to drive the mammalian cell cycle. Nature. 448:811–815. 2007. View Article : Google Scholar : PubMed/NCBI

39 

Tetsu O and McCormick F: Proliferation of cancer cells despite CDK2 inhibition. Cancer Cell. 3:233–245. 2003. View Article : Google Scholar : PubMed/NCBI

40 

Casellas P, Galiegue S and Basile AS: Peripheral benzodiazepine receptors and mitochondrial function. Neurochem Int. 40:475–486. 2002. View Article : Google Scholar : PubMed/NCBI

41 

Santidrián AF, Cosialls AM, Coll-Mulet L, Iglesias-Serret D, de Frias M, González-Gironès DM, Campàs C, Domingo A, Pons G and Gil J: The potential anticancer agent PK11195 induces apoptosis irrespective of p53 and ATM status in chronic lymphocytic leukemia cells. Haematologica. 92:1631–1638. 2007. View Article : Google Scholar : PubMed/NCBI

42 

Mendonça-Torres MC and Roberts SS: The translocator protein (TSPO) ligand PK11195 induces apoptosis and cell cycle arrest and sensitizes to chemotherapy treatment in pre- and post-relapse neuroblastoma cell lines. Cancer Biol Ther. 14:319–326. 2013. View Article : Google Scholar : PubMed/NCBI

Related Articles

Journal Cover

March-2018
Volume 17 Issue 3

Print ISSN: 1791-2997
Online ISSN:1791-3004

Sign up for eToc alerts

Recommend to Library

Copy and paste a formatted citation
x
Spandidos Publications style
Bo L, Wei B, Wang Z, Kong D, Gao Z and Miao Z: Bioinformatics analysis of the CDK2 functions in neuroblastoma. Mol Med Rep 17: 3951-3959, 2018
APA
Bo, L., Wei, B., Wang, Z., Kong, D., Gao, Z., & Miao, Z. (2018). Bioinformatics analysis of the CDK2 functions in neuroblastoma. Molecular Medicine Reports, 17, 3951-3959. https://doi.org/10.3892/mmr.2017.8368
MLA
Bo, L., Wei, B., Wang, Z., Kong, D., Gao, Z., Miao, Z."Bioinformatics analysis of the CDK2 functions in neuroblastoma". Molecular Medicine Reports 17.3 (2018): 3951-3959.
Chicago
Bo, L., Wei, B., Wang, Z., Kong, D., Gao, Z., Miao, Z."Bioinformatics analysis of the CDK2 functions in neuroblastoma". Molecular Medicine Reports 17, no. 3 (2018): 3951-3959. https://doi.org/10.3892/mmr.2017.8368