Open Access

Examining the biomarkers and molecular mechanisms of medulloblastoma based on bioinformatics analysis

  • Authors:
    • Biao Yang
    • Jun‑Xi Dai
    • Yuan‑Bo Pan
    • Yan‑Bin Ma
    • Sheng‑Hua Chu
  • View Affiliations

  • Published online on: May 3, 2019
  • Pages: 433-441
  • Copyright: © Yang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Medulloblastoma (MB) is the most common malignant brain tumor in children. The aim of the present study was to predict biomarkers and reveal their potential molecular mechanisms in MB. The gene expression profiles of GSE35493, GSE50161, GSE74195 and GSE86574 were downloaded from the Gene Expression Omnibus (GEO) database. Using the Limma package in R, a total of 1,006 overlapped differentially expressed genes (DEGs) with the cut‑off criteria of P<0.05 and |log2fold‑change (FC)|>1 were identified between MB and normal samples, including 540 upregulated and 466 downregulated genes. Furthermore, the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were also performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool to analyze functional and pathway enrichment. The Search Tool for Retrieval of Interacting Genes database was subsequently used to construct a protein‑protein interaction (PPI) network and the network was visualized in Cytoscape. The top 11 hub genes, including CDK1, CCNB1, CCNB2, PLK1, CDC20, MAD2L1, AURKB, CENPE, TOP2A, KIF2C and PCNA, were identified from the PPI network. The survival curves for hub genes in the dataset GSE85217 predicted the association between the genes and survival of patients with MB. The top 3 modules were identified by the Molecular Complex Detection plugin. The results indicated that the pathways of DEGs in module 1 were primarily enriched in cell cycle, progesterone‑mediated oocyte maturation and oocyte meiosis; and the most significant functional pathways in modules 2 and 3 were primarily enriched in mismatch repair and ubiquitin‑mediated proteolysis, respectively. These results may help elucidate the pathogenesis and design novel treatments for MB.


Medulloblastoma (MB) is the most common solid tumor in children, comprising 15–20% of pediatric central nervous system tumors (1,2). MB may occur at all ages, however its peak incidence is between 4 and 7 years (3). In 2016, the World Health Organization classified MB into four subtypes, including WNT-activated, SHH-activated, group 3 and group 4, by combining molecular profiling with histology (4). In addition, as these tumors occur in the posterior fossa, clinical symptoms are often too vague for accurate and prompt diagnosis (5). The therapeutic options include maximal safe surgical resection, radiation and chemotherapy (3). However, cerebellar mutism may occur in >25% of the cases following maximal surgical resection in patients with high-risk MB; recovering patients may still experience dysarthria and neurocognitive dysfunction (6). In addition, adjuvant chemotherapy and radiotherapy may lead to hearing loss and the development of secondary tumors (6). The main cause of mortality in MB is metastatic disease, which is unresectable (7). Although multimodal therapy significantly improves the prognosis of MB, approximately one-third of the patients eventually succumb to the disease (3). Therefore, further research on the underlying molecular mechanisms is imperative, in order to design more efficient and precise treatment strategies to improve patient survival.

With the completion of the Human Genome Project, molecular diagnosis and therapy have become available in clinical practice, which is helpful for improving the accuracy and efficacy of diagnosis and treatment (4). Using bioinformatics and microarray analysis, it is possible to further examine the underlying gene characteristics and molecular mechanisms involved in the proliferation, invasion and metastasis of MB (8,9). For example, mitotic kinases and WEE1 G2 checkpoint kinase were identified as rational therapeutic targets for MB by performing an integrated genomic analysis using structural and functional methods (10).

In the present study, 4 databases with 115 samples of MB and normal tissues were downloaded from the Gene Expression Omnibus (GEO) database. Following screening of differentially expressed genes (DEGs) though package Limma in R, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to analyze the potential functional and pathway enrichment, and a protein-protein interaction (PPI) network was subsequently constructed using the Search Tool for Retrieval of Interacting Genes (STRING) database and visualized with Cytoscape software, in order to identify biomarkers and examine the potential underlying molecular mechanisms in MB. Therefore, the results of the present study may improve our understanding of MB, identify potential biomarkers and indicate methods of diagnosis and treatment for future research.

Materials and methods

Microarray data

In the present study, the gene expression profiles of GSE35493 (11), GSE50161 (12), GSE74195 (13) and GSE86574 ( were downloaded from the GEO database ( A total of 115 samples, including 78 MB and 37 normal samples, had been hybridized on the Affymetrix Human Genome U133 Plus 2.0 Array (HG-U133_Plus_2) on the GPL570 platform. GSE35493 included 17 MB and 9 normal samples, GSE50161 included 22 MB and 13 normal samples, GSE74195 included 23 MB and 5 normal samples, and GSE86574 included 16 MB and 10 normal samples. GSE85217 from the GPL22286 platform included 613 MB samples.

Identification of DEGs

The raw data in CEL files were transformed into gene symbols based on the downloaded platform annotation files. The data were preprocessed, including background correction, normalization and summarization, via R 3.4.1 software ( (14). Following robust multiarray average normalization, Limma in R package (version 3.26.9) was used to screen DEGs (15). The genes meeting the cut-off criteria of adjusted P<0.05 and |log2fold-change (FC)|>1 were selected as the DEGs.

Functional and pathway enrichment analysis

After acquiring the DEGs, GO enrichment analysis and KEGG pathway enrichment analysis were performed through DAVID ( online tool to identify functional categories of DEGs (16). GO analysis of DEGs included biological process (BP), molecular function (MF) and cell component (CC). In addition, the terms with P<0.05 were considered to indicate a statistically significant difference.

Construction of the PPI network and selection of modules

To identify hub genes and screen modules, the DEGs were uploaded to STRING (version 10.5; to analyze and set up the PPI network (17). Subsequently, the network was visualized in Cytoscape (version 3.5.1; (18). The Cytoscape software was applied to search for hub genes with CytoHubba, a plugin to hub an object from complex networks, and modules with Molecular Complex Detection (MCODE) (19,20). Furthermore, hub genes in selected modules were analyzed via DAVID to examine pathway enrichment.

Survival analysis

To assess the association between hub genes and survival, 613 MB samples with clinical data from GSE85217 were selected and survival curves were drawn by recruiting the survival package in R.


Identification of DEGs

Basing on the cut-off criteria of P<0.05 and |log2fold-change (FC)|>1, a total of 6,305, 4,185, 2,506 and 4,253 DEGs between MB and normal samples were screened from GSE35493, GSE50161, GSE74195 and GSE86574, respectively. DEGs from GSE35493 included 5,242 upregulated and 1,063 downregulated genes. DEGs from GSE50161 included 3,025 upregulated and 1,160 downregulated genes. DEGs from GSE74195 included 1,315 upregulated and 1,191 downregulated genes. DEGs from GSE86574 included 2,772 upregulated and 1,481 downregulated genes. In addition, a total of 1,006 mutual DEGs were screened, among those 4 datasets, by performing a Venn diagram analysis (Fig. 1), where 540 were upregulated and 466 were downregulated.

Functional and pathway enrichment analysis

Submitting 1,006 mutual DEGs to DAVID provided further insight into the function of these DEGs and the molecular mechanisms implicated in MB. The top 10 significant terms of each GO category (Table I) and the top 10 terms of KEGG category (Table II) were selected. The GO analysis results demonstrated that overlapped DEGs were significantly associated with cell division, mitotic nuclear division and chemical synaptic transmission in the BP category; cell junction, condensed chromosome kinetochore and postsynaptic membrane in the CC category; and protein binding, microtubule binding and chromatin binding in the MF category (P<0.05) (Table I). In addition, the enriched KEGG pathway analysis results primarily included cell cycle, DNA replication and retrograde endocannabinoid signaling (Table II).

Table I.

Top 10 significant GO terms of BP, MF and CC.

Table I.

Top 10 significant GO terms of BP, MF and CC.

BPGO:0051301Cell division77 1.54×10−27
BPGO:0007067Mitotic nuclear division55 6.14×10−20
BPGO:0007268Chemical synaptic transmission45 1.47×10−13
BPGO:0000082G1/S transition of mitotic cell cycle28 1.02×10−12
BPGO:0007062Sister chromatid cohesion27 8.76×10−12
BPGO:0006260DNA replication33 1.23×10−11
BPGO:0000070Mitotic sister chromatid segregation12 1.48×10−08
BPGO:0007059Chromosome segregation18 4.14×10−08
BPGO:0000086G2/M transition of mitotic cell cycle24 5.11×10−07
BPGO:0007076Mitotic chromosome condensation8 4.23×10−06
CCGO:0030054Cell junction65 8.14×10−14
CCGO:0000777Condensed chromosome kinetochore24 2.74×10−11
CCGO:0045211Postsynaptic membrane37 8.92×10−11
CCGO:0014069Postsynaptic density34 1.40×10−10
CCGO:0005654Nucleoplasm206 2.20×10−09
CCGO:0030425Dendrite43 3.92×10−08
CCGO:0000775Chromosome, centromeric region16 8.32×10−08
CCGO:0030496Midbody24 9.65×10−08
CCGO:0043025Neuronal cell body40 1.67×10−07
CCGO:0005874Microtubule39 3.78×10−07
MFGO:0005515Protein binding531 3.92×10−09
MFGO:0008017Microtubule binding32 7.31×10−08
MFGO:0003682Chromatin binding40 4.88×10−05
MFGO:0005524ATP binding110 7.45×10−05
MFGO:0019901Protein kinase binding38 1.00×10−04
MFGO:0005201Extracellular matrix structural constituent13 1.27×10−04
MFGO:0005509Calcium ion binding60 1.87×10−04
MFGO:0017075Syntaxin-1 binding7 1.88×10−04
MFGO:0004890GABA-A receptor activity7 2.63×10−04
MFGO:0005219Ryanodine-sensitive calcium-release channel activity4 5.07×10−04

[i] GO, Gene Ontology; BP, biological process; MF, molecular function; CC, cell component.

Table II.

Top 10 significant Kyoto Encyclopedia of Genes and Genomes pathways.

Table II.

Top 10 significant Kyoto Encyclopedia of Genes and Genomes pathways.

hsa04110Cell cycle28 3.95×10−11E2F5, DBF4, TTK, CHEK1, PTTG1, CHEK2, CCNE2, CDC45, MCM7, CDKN2C, CDK1, ESPL1, CDK6, CDC20, MCM2, CDK4, MCM3, WEE1, CDC25A, MCM5, CCNB1, MAD2L1, CCNB2, CCND2, PLK1, PCNA, BUB1B, ABL1
hsa03030DNA replication14 7.26×10−09MCM2, RNASEH2A, MCM3, MCM5, PRIM1, RFC3, RFC4, MCM7, POLE2, RFC2, POLD1, PRIM2, PCNA, FEN1
hsa04723Retrograde endocannabinoid signaling22 1.50×10−08GABRD, GABRG1, GABRA2, GABRA1, GNAI3, GABRA4, GABRB2, GABRB1, GNG13, MAPK10, GRIA4, RIMS1, KCNJ3, ITPR1, SLC17A7, SLC32A1, KCNJ6, KCNJ9, GRIA1, MGLL, GNG3, GNG4
hsa04727GABAergic synapse20 2.14×10−08GABRD, GABRG1, GABRA2, GABARAPL1, GABRA1, GNAI3, SLC6A1, GABRA4, GABRB2, GABRB1, GABBR1, GNG13, GABBR2, GLS2, SLC32A1, KCNJ6, ABAT, GNG3, GNG4, GAD1
hsa05032Morphine addiction19 3.66×10−07GABRD, GABRG1, GABRA2, GNAI3, GABRA1, GABRA4, GABRB2, GABRB1, GABBR1, GNG13, GABBR2, KCNJ3, ADORA1, SLC32A1, KCNJ6, KCNJ9, PDE1A, GNG3, GNG4
hsa05033Nicotine addiction12 2.42×10−06SLC17A7, GABRD, SLC32A1, GABRG1, GABRA2, GABRA1, GABRA4, GRIA1, GABRB2, GABRB1, GRIN2A, GRIA4
hsa04728Dopaminergic synapse20 1.55×10−05SCN1A, CALY, PPP2R3A, GNAI3, KIF5A, GRIN2A, GNG13, MAPK10, GRIA4, KCNJ3, ITPR1, KCNJ6, KCNJ9, PPP1R1B, GRIA1, CREB3L4, CAMK2B, GNG3, PPP3CA, GNG4
hsa04713Circadian entrainment16 5.91×10−05GNAI3, GRIN2A, GNG13, GRIA4, KCNJ3, ITPR1, KCNJ6, KCNJ9, GRIA1, RYR3, RYR1, RYR2, CAMK2B, GUCY1B3, GNG3, GNG4
hsa03430Mismatch repair8 8.58×10−05EXO1, MSH6, RFC3, RFC4, RFC2, MSH2, POLD1, PCNA
hsa04115p53 signaling pathway13 9.34×10−05CCNB1, CCNE2, CDK1, TP53I3, CCNB2, CCND2, RRM2, SIAH1, CHEK1, CDK6, CHEK2, CDK4, GTSE1
Module screening from the PPI network

Among the 4 datasets, the overlapped 1,006 DEGs were analyzed with the PPI network via STRING, and interaction with a score >0.9 was subsequently obtained in the following analysis. Furthermore, the hub genes with degrees >44 were screened out based on CytoHubba. In total, 11 nodes were screened out as hub genes, including CDK1 (degree=90), CCNB1 (degree=68), CCNB2 (degree=60), PLK1 (degree=60), CDC20 (degree=57), MAD2L1 (degree=53), AURKB (degree=50), CENPE (degree=46), TOP2A (degree=45), KIF2C (degree=45) and PCNA (degree=45; Fig. 2A). Among the 11 hub genes, the node with the highest degree (90) was CDK1. Additionally, four heat maps of the expression of 11 hub genes in GSE35493, GSE50161, GSE74195 and GSE86574 are presented in Fig. 3.

Furthermore, following MCODE analysis, 7 modules were identified to be available and the top 3 significant modules are presented in Fig. 2B-D. Furthermore, the top 3 pathways in each module are listed in Table III. Module 1 had 29 nodes, 404 edges and the highest score (score=28.857). In this module, the top 3 enriched KEGG pathways were cell cycle, progesterone-mediated oocyte maturation, and oocyte meiosis. In addition, module 2 (score=11.6) had 31 nodes and 174 edges, and the pathways were primarily associated with mismatch repair, DNA replication and nucleotide excision repair. Module 3 (score=10) had 10 nodes and 45 edges, which were significantly enriched in ubiquitin-mediated proteolysis (P<0.05; Table III).

Table III.

Top 10 significant KEGG pathways of the DEGs in top 3 modules.

Table III.

Top 10 significant KEGG pathways of the DEGs in top 3 modules.

ModuleTermKEGG namesCountP-valueGenes
Module 1hsa04110Cell cycle8 5.80×10−11CCNB1, CDK1, CDC20, CCNB2, PLK1, BUB1B, MAD2L1, ESPL1
hsa04914 Progesterone-mediated oocyte maturation5 4.65×10−06CCNB1, CDK1, PLK1, MAD2L1, CCNB2,
hsa04114Oocyte meiosis5 1.14×10−05CDK1, MAD2L1, PLK1, CDC20, ESPL1
Module 2hsa03430Mismatch repair6 3.25×10−10EXO1, RFC3, RFC4, RFC2, POLD1, PCNA
hsa03030DNA replication6 3.59×10−09RFC3, RFC4, POLE2, RFC2, POLD1, PCNA
hsa03420Nucleotide excision repair6 1.45×10−08RFC3, RFC4, POLE2, RFC2, POLD1, PCNA
Module 3hsa04120Ubiquitin mediated proteolysis4 7.41×10−05FBXW7, SIAH1, UBE2S, UBE2E2

[i] KEGG, Kyoto Encyclopedia of Genes and Genomes; DEGs, differentially expressed genes.

Survival analysis

The survival curves for hub genes in the dataset GSE85217 are presented in Fig. 4. The expressions of CCNB1 (P=0.03108), CCNB2 (P=0.00336), CDC20 (P=0.026), KIF2C (P=0.01622), MAD2L1 (P=0.00145), PLK1 (P=0.00325) and TOP2A (P=0.01387) were negatively associated with patient survival time.


Identifying the molecular mechanisms of MB, which has unique gene expression signatures, is of critical importance for targeted diagnosis and treatment (21). In the present study, 78 MB and 37 normal samples were collected from the GEO database for bioinformatics analysis, aiming to identify hidden biomarkers and elucidate the molecular mechanisms in MB. A total of 1,006 mutual DEGs were screened from the four microarray datasets GSE35493, GSE50161, GSE74195 and GSE86574 using the Limma in R package.

The GO analysis results of the DEGs revealed that the overlapped DEGs were primarily associated with mitosis, including cell division, mitotic nuclear division, G1/S transition of the mitotic cell cycle, sister chromatid cohesion, sister chromatid segregation, chromosome segregation, G2/M transition of the mitotic cell cycle, and mitotic chromosome condensation in the BP category. In addition, Aurora kinase B regulates multiple stages of mitosis, and its inhibitors may inhibit the growth of Group 3 MBs and prolong survival (22). These results suggested that it may be possible to treat MB by regulating the key biomarkers of mitosis (23,24). In addition, it was also observed that these genes were enriched in cell junction, condensed chromosome kinetochore, protein binding, microtubule binding and chromatin binding. Certain RNA binding proteins, including MSI1, DDX3X and CCAR1, were reported to play important roles in the growth and/or maintenance of MB (25).

Following KEGG pathway analysis, the genes were found to be significantly associated with cell cycle, DNA replication, retrograde endocannabinoid signaling, GABAergic synapse, morphine addiction, nicotine addiction, dopaminergic synapse, circadian entrainment, mismatch repair, and p53 signaling pathway. A previous study reported that the defect of NEO1, which was necessary for cell cycle progression, arrests cells at the G2/M phase in MB (26). These results indicated that cannabinoids, morphine and nicotine, consistent with previous studies, were likely associated with the progression of brain tumors (2729). Therefore, these pathway analysis results may enable the prediction of novel therapeutic targets.

Furthermore, the top 11 hub genes, including CDK1, CCNB1, CCNB2, PLK1, CDC20, MAD2L1, AURKB, CENPE, TOP2A, KIF2C and PCNA, were identified using the PPI network. It was reported that inhibiting the catalytic activity of CDK1 using VMY-1-103 may severely disrupt the mitotic cycle of MB cells (24). It was previously reported that the combined expression of MYC, LDHB and CCNB1 as potential prognostic biomarkers may predict survival and provide a more accurate basis for the targeted therapy of patients with MB (30). The inhibitors of PLK1, an oncogenic kinase that controls cell cycle and proliferation, inhibited mitosis in MB cells, and patients expressing high levels of PLK1 were considered as high-risk. All findings indicated that PLK1 is a possible therapeutic target for patients with MB (31,32). In another study, it was reported that the expression levels of TOP2A may be a potential biomarker for sensitivity to etoposide in patients with MB (33). PCNA, which may be used to demonstrate the proliferative phase of the cell cycle, was significantly associated with MB grade, suggesting that PCNA may be a biomarker for assessing grade and the possibility of recurrence in MB (34).

However, 6 hub genes, including CCNB2, CDC20, MAD2L1, AURKB, CENPE and KIF2C, have yet to be verified, to the best of our knowledge, in MB by systematic searches through PubMed, there were a number of studies on other tumors, particularly brain tumors, including glioma (3344). It was reported that CDC20 was a critical regulator of tumor-initiating cell proliferation and survival of glioblastoma cells (35). Combining the findings of other studies, CDC20 was found to play a role in cell cycle progression, apoptosis and brain development, and these findings indicated that it may be a potential novel target for therapeutic intervention in brain tumors, particularly MB (36,37). In addition, the RNA levels of CDC20 and MAD2L1 were associated with glioma grade, which suggested a clinical benefit as a biomarker (38). Other studies demonstrated that MAD2L1 was of diagnostic value in several tumors, including salivary duct carcinomas, breast cancer and acute lymphoblastic leukemia (3941). Furthermore, CCNB2 was predicted as a tumor-associated factor similar to CCNB1 (42). AURKA was reported to be the target of some molecule inhibitors, such as BMS-754807 and SIX3, aiming to inhibit diffuse intrinsic pontine glioma or astrocytoma (43,44). The knockdown of CENPE, which is highly expressed in pediatric glioma, combined with temozolomide treatment, was found to lead to inhibition of glioma cell proliferation (45). KIF2C was reported to be associated with histopathological glioma grades, which indicated this gene may be a potential biomarker for prognosis in patients with glioma (46). Taken together, although their significance has yet to been confirmed, to the best of our knowledge, these findings suggest that the 6 hub genes may be potential biomarkers in the diagnosis, treatment and prognosis of MB.

In conclusion, the findings of the present study provided an integrated bioinformatics analysis of 1,006 overlapped DEGs that may be involved in the growth, recurrence and metastasis of MB. A total of 11 hub genes, including CDK1, CCNB1, CCNB2, PLK1, CDC20, MAD2L1, AURKB, CENPE, TOP2A, KIF2C and PCNA, were identified as novel potential biomarkers. These findings may provide further insight into the underlying molecular mechanisms and identify novel biomarkers for evaluating the diagnosis and prognosis, and advance the treatment of MB. However, further molecular biological research is required to confirm the clinical value of our findings.


Not applicable.


The present study was supported by the Scientific Research Foundation for the Returned Overseas Chinese Scholars (082003), the Research Foundation of Shanghai Municipal Health and Family Planning Commission (201540266), Shanghai Jiao Tong University Medicine-Engineering Cross Research Foundation (YG2015MS25) and the Research Foundation of Shanghai No. 3 People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine (syz2015-015).

Availability of data and materials

The datasets used and/or analyzed during the present study were obtained from the GEO, DAVID and STRING databases.

Authors' contributions

SHC and BY were involved in the conception and design of the research and drafting the manuscript. YBP participated in the acquisition of data. BY and JXD performed the analysis and interpretation of data. YBM was involved in the statistical analysis. All authors read and approved the final manuscript.

Ethics approval and consent to participate

Not applicable.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.







Gene Expression Omnibus


differentially expressed genes


Database for Annotation, Visualization and Integrated Discovery


Gene Ontology


Kyoto Encyclopedia of Genes and Genomes


protein-protein interaction


Search Tool for the Retrieval of Interacting Genes


Molecular Complex Detection


biological process


molecular function


cell component



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Yang B, Dai JX, Pan YB, Ma YB and Chu SH: Examining the biomarkers and molecular mechanisms of medulloblastoma based on bioinformatics analysis. Oncol Lett 18: 433-441, 2019
Yang, B., Dai, J., Pan, Y., Ma, Y., & Chu, S. (2019). Examining the biomarkers and molecular mechanisms of medulloblastoma based on bioinformatics analysis. Oncology Letters, 18, 433-441.
Yang, B., Dai, J., Pan, Y., Ma, Y., Chu, S."Examining the biomarkers and molecular mechanisms of medulloblastoma based on bioinformatics analysis". Oncology Letters 18.1 (2019): 433-441.
Yang, B., Dai, J., Pan, Y., Ma, Y., Chu, S."Examining the biomarkers and molecular mechanisms of medulloblastoma based on bioinformatics analysis". Oncology Letters 18, no. 1 (2019): 433-441.