Abstract

   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 [33]GSE35493, [34]GSE50161, [35]GSE74195 and [36]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
   |log[2]fold-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 [37]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.

   Keywords: medulloblastoma, bioinformatics, differentially expressed
   genes, biomarkers, modules

Introduction

   Medulloblastoma (MB) is the most common solid tumor in children,
   comprising 15–20% of pediatric central nervous system tumors
   ([38]1,[39]2). MB may occur at all ages, however its peak incidence is
   between 4 and 7 years ([40]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 ([41]4). In addition, as these tumors occur in the
   posterior fossa, clinical symptoms are often too vague for accurate and
   prompt diagnosis ([42]5). The therapeutic options include maximal safe
   surgical resection, radiation and chemotherapy ([43]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 ([44]6).
   In addition, adjuvant chemotherapy and radiotherapy may lead to hearing
   loss and the development of secondary tumors ([45]6). The main cause of
   mortality in MB is metastatic disease, which is unresectable ([46]7).
   Although multimodal therapy significantly improves the prognosis of MB,
   approximately one-third of the patients eventually succumb to the
   disease ([47]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 ([48]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 ([49]8,[50]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 ([51]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 [52]GSE35493
   ([53]11), [54]GSE50161 ([55]12), [56]GSE74195 ([57]13) and [58]GSE86574
   ([59]https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE86574) were
   downloaded from the GEO database
   ([60]http://www.ncbi.nlm.nih.gov/geo/). 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
   [61]GPL570 platform. [62]GSE35493 included 17 MB and 9 normal samples,
   [63]GSE50161 included 22 MB and 13 normal samples, [64]GSE74195
   included 23 MB and 5 normal samples, and [65]GSE86574 included 16 MB
   and 10 normal samples. [66]GSE85217 from the [67]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 ([68]https://www.r-project.org/) ([69]14). Following
   robust multiarray average normalization, Limma in R package (version
   3.26.9) was used to screen DEGs ([70]15). The genes meeting the cut-off
   criteria of adjusted P<0.05 and |log[2]fold-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
   ([71]https://david.ncifcrf.gov/) online tool to identify functional
   categories of DEGs ([72]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; [73]http://www.string-db.org/) to analyze and set
   up the PPI network ([74]17). Subsequently, the network was visualized
   in Cytoscape (version 3.5.1; [75]www.cytoscape.org) ([76]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) ([77]19,[78]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 [79]GSE85217 were selected and survival
   curves were drawn by recruiting the survival package in R.

Results

Identification of DEGs

   Basing on the cut-off criteria of P<0.05 and |log[2]fold-change
   (FC)|>1, a total of 6,305, 4,185, 2,506 and 4,253 DEGs between MB and
   normal samples were screened from [80]GSE35493, [81]GSE50161,
   [82]GSE74195 and [83]GSE86574, respectively. DEGs from [84]GSE35493
   included 5,242 upregulated and 1,063 downregulated genes. DEGs from
   [85]GSE50161 included 3,025 upregulated and 1,160 downregulated genes.
   DEGs from [86]GSE74195 included 1,315 upregulated and 1,191
   downregulated genes. DEGs from [87]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 ([88]Fig. 1), where 540 were upregulated and 466 were
   downregulated.

Figure 1.

   Figure 1.
   [89]Open in a new tab

   Venn diagram of differentially expressed genes among the 4 datasets.

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 ([90]Table I) and the
   top 10 terms of KEGG category ([91]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) ([92]Table I). In addition, the enriched KEGG
   pathway analysis results primarily included cell cycle, DNA replication
   and retrograde endocannabinoid signaling ([93]Table II).

Table I.

   Top 10 significant GO terms of BP, MF and CC.
   Category Term Description Count P-value
   BP GO:0051301 Cell division 77 1.54×10^−27
   BP GO:0007067 Mitotic nuclear division 55 6.14×10^−20
   BP GO:0007268 Chemical synaptic transmission 45 1.47×10^−13
   BP GO:0000082 G1/S transition of mitotic cell cycle 28 1.02×10^−12
   BP GO:0007062 Sister chromatid cohesion 27 8.76×10^−12
   BP GO:0006260 DNA replication 33 1.23×10^−11
   BP GO:0000070 Mitotic sister chromatid segregation 12 1.48×10^−08
   BP GO:0007059 Chromosome segregation 18 4.14×10^−08
   BP GO:0000086 G2/M transition of mitotic cell cycle 24 5.11×10^−07
   BP GO:0007076 Mitotic chromosome condensation 8 4.23×10^−06
   CC GO:0030054 Cell junction 65 8.14×10^−14
   CC GO:0000777 Condensed chromosome kinetochore 24 2.74×10^−11
   CC GO:0045211 Postsynaptic membrane 37 8.92×10^−11
   CC GO:0014069 Postsynaptic density 34 1.40×10^−10
   CC GO:0005654 Nucleoplasm 206 2.20×10^−09
   CC GO:0030425 Dendrite 43 3.92×10^−08
   CC GO:0000775 Chromosome, centromeric region 16 8.32×10^−08
   CC GO:0030496 Midbody 24 9.65×10^−08
   CC GO:0043025 Neuronal cell body 40 1.67×10^−07
   CC GO:0005874 Microtubule 39 3.78×10^−07
   MF GO:0005515 Protein binding 531 3.92×10^−09
   MF GO:0008017 Microtubule binding 32 7.31×10^−08
   MF GO:0003682 Chromatin binding 40 4.88×10^−05
   MF GO:0005524 ATP binding 110 7.45×10^−05
   MF GO:0019901 Protein kinase binding 38 1.00×10^−04
   MF GO:0005201 Extracellular matrix structural constituent 13
   1.27×10^−04
   MF GO:0005509 Calcium ion binding 60 1.87×10^−04
   MF GO:0017075 Syntaxin-1 binding 7 1.88×10^−04
   MF GO:0004890 GABA-A receptor activity 7 2.63×10^−04
   MF GO:0005219 Ryanodine-sensitive calcium-release channel activity 4
   5.07×10^−04
   [94]Open in a new tab

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

Table II.

   Top 10 significant Kyoto Encyclopedia of Genes and Genomes pathways.
   Term Description Count P-value Genes
   hsa04110 Cell cycle 28 3.95×10^−11 E2F5, 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
   hsa03030 DNA replication 14 7.26×10^−09 MCM2, RNASEH2A, MCM3, MCM5,
   PRIM1, RFC3, RFC4, MCM7, POLE2, RFC2, POLD1, PRIM2, PCNA, FEN1
   hsa04723 Retrograde endocannabinoid signaling 22 1.50×10^−08 GABRD,
   GABRG1, GABRA2, GABRA1, GNAI3, GABRA4, GABRB2, GABRB1, GNG13, MAPK10,
   GRIA4, RIMS1, KCNJ3, ITPR1, SLC17A7, SLC32A1, KCNJ6, KCNJ9, GRIA1,
   MGLL, GNG3, GNG4
   hsa04727 GABAergic synapse 20 2.14×10^−08 GABRD, GABRG1, GABRA2,
   GABARAPL1, GABRA1, GNAI3, SLC6A1, GABRA4, GABRB2, GABRB1, GABBR1,
   GNG13, GABBR2, GLS2, SLC32A1, KCNJ6, ABAT, GNG3, GNG4, GAD1
   hsa05032 Morphine addiction 19 3.66×10^−07 GABRD, GABRG1, GABRA2,
   GNAI3, GABRA1, GABRA4, GABRB2, GABRB1, GABBR1, GNG13, GABBR2, KCNJ3,
   ADORA1, SLC32A1, KCNJ6, KCNJ9, PDE1A, GNG3, GNG4
   hsa05033 Nicotine addiction 12 2.42×10^−06 SLC17A7, GABRD, SLC32A1,
   GABRG1, GABRA2, GABRA1, GABRA4, GRIA1, GABRB2, GABRB1, GRIN2A, GRIA4
   hsa04728 Dopaminergic synapse 20 1.55×10^−05 SCN1A, CALY, PPP2R3A,
   GNAI3, KIF5A, GRIN2A, GNG13, MAPK10, GRIA4, KCNJ3, ITPR1, KCNJ6, KCNJ9,
   PPP1R1B, GRIA1, CREB3L4, CAMK2B, GNG3, PPP3CA, GNG4
   hsa04713 Circadian entrainment 16 5.91×10^−05 GNAI3, GRIN2A, GNG13,
   GRIA4, KCNJ3, ITPR1, KCNJ6, KCNJ9, GRIA1, RYR3, RYR1, RYR2, CAMK2B,
   GUCY1B3, GNG3, GNG4
   hsa03430 Mismatch repair 8 8.58×10^−05 EXO1, MSH6, RFC3, RFC4, RFC2,
   MSH2, POLD1, PCNA
   hsa04115 p53 signaling pathway 13 9.34×10^−05 CCNB1, CCNE2, CDK1,
   TP53I3, CCNB2, CCND2, RRM2, SIAH1, CHEK1, CDK6, CHEK2, CDK4, GTSE1
   [95]Open in a new tab

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; [96]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
   [97]GSE35493, [98]GSE50161, [99]GSE74195 and [100]GSE86574 are
   presented in [101]Fig. 3.

Figure 2.

   [102]Figure 2.
   [103]Open in a new tab

   PPI network and modules of DEGs in MB. (A) PPI network. (B) Module 1.
   (C) Module 2. (D) Module 3. The circular nodes represent DEGs. The
   edges/lines stand for the regulatory association between two nodes. The
   top 11 hub genes are highlighted with red circles. PPI, protein-protein
   interaction; DEGs, differentially expressed genes; MB, medulloblastoma.

Figure 3.

   [104]Figure 3.
   [105]Open in a new tab

   Hub gene expression heat maps among the 4 databases. (A) Hub gene
   expression heat map of [106]GSE35493. (B) Hub gene expression heat map
   of [107]GSE50161. (C) Hub gene expression heat map of [108]GSE74195.
   (D) Hub gene expression heat map of [109]GSE86574. Red, upregulation;
   blue, downregulation.

   Furthermore, following MCODE analysis, 7 modules were identified to be
   available and the top 3 significant modules are presented in [110]Fig.
   2B-D. Furthermore, the top 3 pathways in each module are listed in
   [111]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; [112]Table III).

Table III.

   Top 10 significant KEGG pathways of the DEGs in top 3 modules.
   Module Term KEGG names Count P-value Genes
   Module 1 hsa04110 Cell cycle 8 5.80×10^−11 CCNB1, CDK1, CDC20, CCNB2,
   PLK1, BUB1B, MAD2L1, ESPL1
   hsa04914 Progesterone-mediated oocyte maturation 5 4.65×10^−06 CCNB1,
   CDK1, PLK1, MAD2L1, CCNB2,
   hsa04114 Oocyte meiosis 5 1.14×10^−05 CDK1, MAD2L1, PLK1, CDC20, ESPL1
   Module 2 hsa03430 Mismatch repair 6 3.25×10^−10 EXO1, RFC3, RFC4, RFC2,
   POLD1, PCNA
   hsa03030 DNA replication 6 3.59×10^−09 RFC3, RFC4, POLE2, RFC2, POLD1,
   PCNA
   hsa03420 Nucleotide excision repair 6 1.45×10^−08 RFC3, RFC4, POLE2,
   RFC2, POLD1, PCNA
   Module 3 hsa04120 Ubiquitin mediated proteolysis 4 7.41×10^−05 FBXW7,
   SIAH1, UBE2S, UBE2E2
   [113]Open in a new tab

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

Survival analysis

   The survival curves for hub genes in the dataset [114]GSE85217 are
   presented in [115]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.

Figure 4.

   [116]Figure 4.
   [117]Open in a new tab

   Survival analysis for hub genes in the dataset [118]GSE85217. The
   difference in survival between low and high expression of (A) AURKB
   (P=0.63726), (B) CCNB1 (P=0.03108), (C) CCNB2 (P=0.00336), (D) CDC20
   (P=0.02600), (E) CDK1 (P=0.70024), (F) CENPE (P=0.12852), (G) KIF2C
   (P=0.01622), (H) MAD2L1 (P=0.00145), (I) PCNA (P=0.21227), (J) PLK1
   (P=0.00325) and (K) TOP2A (P=0.01387). Red lines represent high
   expression and blue lines represent low expression of the hub genes.

Discussion

   Identifying the molecular mechanisms of MB, which has unique gene
   expression signatures, is of critical importance for targeted diagnosis
   and treatment ([119]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 [120]GSE35493, [121]GSE50161,
   [122]GSE74195 and [123]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, G[1]/S transition of the mitotic cell cycle,
   sister chromatid cohesion, sister chromatid segregation, chromosome
   segregation, G[2]/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 ([124]22). These results
   suggested that it may be possible to treat MB by regulating the key
   biomarkers of mitosis ([125]23,[126]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 ([127]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 G[2]/M phase in MB ([128]26). These
   results indicated that cannabinoids, morphine and nicotine, consistent
   with previous studies, were likely associated with the progression of
   brain tumors ([129]27–[130]29). 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 ([131]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 ([132]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 ([133]31,[134]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
   ([135]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 ([136]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
   ([137]33–[138]44). It was reported that CDC20 was a critical regulator
   of tumor-initiating cell proliferation and survival of glioblastoma
   cells ([139]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 ([140]36,[141]37). In addition, the RNA levels of CDC20 and MAD2L1
   were associated with glioma grade, which suggested a clinical benefit
   as a biomarker ([142]38). Other studies demonstrated that MAD2L1 was of
   diagnostic value in several tumors, including salivary duct carcinomas,
   breast cancer and acute lymphoblastic leukemia ([143]39–[144]41).
   Furthermore, CCNB2 was predicted as a tumor-associated factor similar
   to CCNB1 ([145]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 ([146]43,[147]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 ([148]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
   ([149]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.

Acknowledgements