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Bioinformatics Analysis of the CDK2 Functions in Neuroblastoma

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Bioinformatics Analysis of the CDK2 Functions in Neuroblastoma MOLECULAR MEDICINE REPORTS 17: 3951-3959, 2018 Bioinformatics analysis of the CDK2 functions in neuroblastoma LIJUAN BO1*, BO WEI2*, ZHANFENG WANG2, DALIANG KONG3, ZHENG GAO2 and ZHUANG MIAO2 Departments of 1Infections, 2Neurosurgery and 3Orthopaedics, China-Japan Union Hospital of Jilin University, Changchun, Jilin 130033, P.R. China Received December 20, 2016; Accepted November 14, 2017 DOI: 10.3892/mmr.2017.8368 Abstract. The present study aimed to elucidate the poten- childhood cancer mortality (1,2). Despite intensive myeloabla- tial mechanism of cyclin-dependent kinase 2 (CDK2) in tive chemotherapy, survival rates for neuroblastoma have not neuroblastoma progression and to identify the candidate substantively improved; relapse is common and frequently genes associated with neuroblastoma with CDK2 silencing. leads to mortality (3,4). Like most human cancers, this child- The microarray data of GSE16480 were obtained from the hood cancer can be inherited; however, the genetic aetiology gene expression omnibus database. This dataset contained remains to be elucidated (3). Therefore, an improved under- 15 samples: Neuroblastoma cell line IMR32 transfected standing of the genetics and biology of neuroblastoma may with CDK2 shRNA at 0, 8, 24, 48 and 72 h (n=3 per group; contribute to further cancer therapies. total=15). Significant clusters associated with differen- In terms of genetics, neuroblastoma tumors from patients tially expressed genes (DEGs) were identified using fuzzy with aggressive phenotypes often exhibit significant MYCN C-Means algorithm in the Mfuzz package. Gene ontology and proto-oncogene, bHLH transcription factor (MYCN) amplifi- pathway enrichment analysis of DEGs in each cluster were cation and are strongly associated with a poor prognosis (5). performed, and a protein-protein interaction (PPI) network MYCN, a member of MYC proto‑oncogene family, func- was constructed. Additionally, functional annotation of DEGs tions as a transcriptional factor, which controls cell growth in clusters was performed for the detection of transcription and proliferation and thus has an important role in driving factors and tumor-associated genes. A total of 4 clusters with tumorigenesis of neuroblastoma cells (6,7). Additionally, the significant change tendency and 1,683 DEGs were identified. overexpression of MYC genes in non-MYC‑amplified cells may The hub nodes of the PPI network constructed by DEGs in induce apoptosis (8). A previous study by Molenaar et al (9) Cluster 1, Cluster 2, Cluster 3 and Cluster 4 were MDM2 onco- confirmed that inactivation of cyclin-dependent kinase 2 gene, E3 ubiquitin protein ligase (MDM2), cyclin-dependent (CDK2) was synthetically lethal to neuroblastoma cells with kinase 1 (CDK1), proteasome (prosome, macropain) 26S MYCN‑amplification and overexpression (9). The CDK2 gene subunit, non-ATPase, 14 (PSMD14) and translocator protein encodes a protein that is member of serine/threonine protein (18 kDa) (TSPO) respectively. These genes were significantly kinase family that is involved in cell cycle regulation (10). enriched in the p53 signaling pathway, cell cycle, proteasome Additionally, CDK2 has been demonstrated to regulate the and systemic lupus erythematosus pathways. MDM2, CDK1, progression through the cell cycle (11). A previous study PSMD14 and TSPO may be key target genes of CDK2. CDK2 also has determined that the targeting of aberrant cell may have key functions in neuroblastoma progression by regu- cycle checkpoints in cancer cells may inhibit tumor growth lating the expression of these genes. and induce cell death (12). CDK2 is a vital regulator of S-phase progression and is deemed to be an anticancer drug Introduction target (9,13). Additionally, CDK2 inhibitors may act as poten- tial MYCN-selective cancer therapeutics in the treatment of Neuroblastoma is an embryonal tumor that arises from neuroblastoma (9). However, the molecular mechanism of the sympathetic nervous system, accounts for ~15% 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 Correspondence to: Dr Zhuang Miao, Department of CDK2 silencing. The findings revealed that these upregulated Neurosurgery, China-Japan Union Hospital of Jilin University, genes were target genes of p53, and silencing of p53 protected 126 Xiantai Street, Changchun, Jilin 130033, P.R. China the cells from MYCN‑driven apoptosis (9). However, to the E-mail: [email protected] best of our knowledge, there was no systematic and compre- hensive analysis for this expression profile. The present study * Contributed equally downloaded the microarray data of GSE16480 and then identified significant clusters associated with differentially Key words: neuroblastoma, cyclin-dependent kinase 2, differentially expressed genes (DEGs) following CDK2 silencing. Gene expressed genes, pathways analysis, protein-protein interaction ontology (GO) and pathway enrichment analysis of DEGs in each cluster were performed, and protein-protein interaction 3952 BO et al: DEGS IN NEUROBLASTOMA WITH CDK2 SILENCING (PPI) network was constructed. Additionally, a functional gene (TAG) database (21) summarizes attributes for a specific annotation of DEGs in the clusters was performed. The present entity associated with the TAGs. study aimed to identify key genes and biological pathways Functional annotations of DEGs in clusters were performed underlying the progression of neuroblastoma with CDK2 for the detection of TFs and TAGs and both databases, TSGene silencing by means of comprehensive bioinformatics analysis and TAG database, were used to identify oncogenes and tumor to further elucidate the function of CDK2 in neuroblastoma suppressor genes. progression and determine potential targets for future cancer therapies. Results Materials and methods Soft clustering analysis. Soft clustering analysis of gene expression time-course data identified 4 clusters with Source of data. The microarray data GSE16480 was down- significant change tendency (Fig. 1). Cluster 1 presented an loaded from Gene Expression Omnibus (GEO, http://www. increasing trend (Fig. 1A). Specifically, the expression levels ncbi.nlm.nih.gov/geo/) database based on the platform of of genes exhibited an increase from 0 to 8 h; subsequently GPL570 (Affymetrix Human Genome U133 Plus 2.0 Array), the levels increased significantly from 8 to 48 h and remained which was deposited by Molenaar et al (9). This dataset constant from 48 to 72 h. It is of note that the change tendency contained 15 samples: Neuroblastoma cell line IMR32 was of gene expression in Cluster 3 (Fig. 1C) at different time transfected with CDK2 shRNA at 0, 8, 24, 48 and 72 h (n=3 points is evidently opposite to those observed in Cluster 1. per group; total=15). The expression levels of genes in Cluster 3 decreased slightly from 0 to 8 h, subsequently the levels decreased significantly Data preprocessing. Background correction, quartile from 8 to 48 h and remained constant from 48 to 72 h. In normalization and probe summarization were performed to addition, the change tendency of gene expression in Cluster normalize the gene expression intensities obtained from the 2 (Fig. 1B) at different time points was evidently opposite to raw dataset using robust multi-array average algorithm (14), those observed in Cluster 4 (Fig. 1D). The expression levels of and the gene expression time-course matrix of samples was genes in Cluster 2 decreased from 0 to 24 h and subsequently acquired. decreased significantly from 24 to 72 h, whereas in Cluster 4 this trend was reversed. Soft clustering analysis. Noise robust soft clustering of gene Additionally, DEGs with the same expression pattern as expression time-course data was implemented using the change tendency of clusters was screened. A total of 1,683 DEGs fuzzy C-Means algorithm (15) in the Mfuzz package (15,16). were identified, including 337 upregulated genes in Cluster 1, The following parameters were set: Minimum Standard 649 downregulated genes in Cluster 2, 387 downregulated Deviation=0.4, score=0.7. This method may assign genes into genes in Cluster 3, and 387 upregulated genes in Cluster 4. several clusters according to the expression pattern of DEGs. Then, clusters with significant change tendency were screened PPI network construction. The PPI networks of DEGs in for further analysis. Cluster 1 (Fig. 2A), 2 (Fig. 2B), 3 (Fig. 2C) and 4 (Fig. 2D) included 86, 18,875, 239 and 109 interactions, respectively. PPI network construction. The Search Tool for the Retrieval Based on connectivity degree, the hub genes with the highest of Interacting Genes (STRING) database (17) is a database for degrees in the four clusters were: MDM2 oncogene, E3 the exploration and analysis of known and predicted protein ubiquitin protein ligase (MDM2), cyclin-dependent kinase 1 interactions, including both experimental and predicted (CDK1), proteasome (prosome, macropain) 26S subunit, interaction information. The present study used the STRING non-ATPase, 14 (PSMD14), translocator protein (18 kDa) online tool to analyze the PPIs of up and downregulated genes (TSPO), respectively (Table I). with required confidence (combined score) >0.4. The hub proteins were subsequently identified from the PPI network GO and pathway enrichment analysis. The present study based on connectivity degree analysis. performed GO and KEGG pathway analysis for DEGs in 4 clusters. The over-represented
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