Identification of Hub Genes and Molecular Mechanism Associated With the Progression of Pediatric Low Grade Gliomas

Identification of Hub Genes and Molecular Mechanism Associated With the Progression of Pediatric Low Grade Gliomas | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Identification of Hub Genes and Molecular Mechanism Associated With the Progression of Pediatric Low Grade Gliomas Hualin Tao, Chuyi Zeng, Zhihua Zuo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1256036/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: To explore hub genes and underlying molecular mechanism about the malignant progression of pediatric low grade gliomas (PLGG). Methods: The microarray were obtained from Gene Expression Omnibus database and hub genes were identified by bioinformatics analysis. Results: Ten hub genes were confirmed to increase with the development of PLGG, and their high expression was correlated with poor prognosis. Additionally, the encoding protein expression level of CDK1, KIF11, TRIP13, MAD2L1, PBK were significantly higher in high grade glioma tissues than that in PLGG. Finally, functional enrichment analysis demonstrated these genes were mainly enriched in cell cycle activities. Conclusion: Our results offer novel insights of the molecular mechanisms and identify potential biomarkers or therapeutic targets for PLGG progression. bioinformatics analysis hub genes molecular mechanism pediatric low grade gliomas progression Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Background Pediatric low-grade glioma (PLGG) is the most common central nervous system (CNS) tumors in children, accounting for over 30% case [1, 2]. Like adult low-grade glioma, according to the 2016 World Health Organization (WHO) classification of CNS tumors, PLGG usually encompasses pilocytic astrocytoma, ganglioglioma, subependymal giant cell astrocytoma and so on [3]. But of note, current evidences indicated that gliomas were markedly distinct between children and adults in cellular origins and fundamental molecular and genetic heterogeneity [4], where these differences were commonly ignored in previous studies that explored the development and progression of gliomas [5]. PLGG patients also have showed a favorable prognosis after surgical resection and evolve less frequently into higher-grade lesions compared to adults [6]. However, malignant transformation of PLGG or the remaining residual tumor after surgery will significantly limit the 5-year survival rate of patients, which can drop below 20% [7, 8]. Importantly, the "druggable" targets for pediatric patients were difficult to broadly realize due to the unclear molecular mechanism [4]. Thus, the in-depth study of the underlying mechanism of PLGG progression assists in administering more precise targeted therapy and improvement of prognosis for pediatric patients. In recent years, numerous studies have explored the potential factors leading to higher-grade lesions of PLGG at molecular genetic level, including gene mutation, deletions, DNA methylation, and so on [1, 9-11]. For instance, Mistry et al. 6 reported that BRAF V600E mutations and CDKN2A deletions could increase the risk of transformation of PLGG to malignancy. Besides, the RAS-mitogen-activated protein kinase (RAS/MAPK) pathway was considered as the main pathway to result in the genetic alterations associated with the progression of PLGG [12]. In contrast, the potential functional genomics and biological functions at the molecular level as well as its regulatory mechanism, which were closely associated with the progression of PLGG to PHGG were rarely reported. Microarray, as a high-throughput platform, has been broadly applied to identify tumor-related genes and new diagnostic biomarkers in various types of cancer, including prostate cancer [13], bladder cancer [14], Colon Cancer [15], and lung adenocarcinoma [16]. Meanwhile, large scale gene expression profiling analyses have been contributed to promote the development of molecular diagnosis and prognosis prediction and targeted therapy for cancer patients by the use of microarrays [17]. In the present study, microarray data was the first time to applied in identification of the key genes and exploration of their interaction mechanism for PLGG progression by integrated bioinformatics analysis. The differentially expressed genes (DEGs) were firstly screened out between PLGG tissues and PHGG tissues that were obtained from the Gene Expression Omnibus (GEO) database. Gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were achieved to annotate the biological functions of DEGs. Furthermore, the protein-protein interaction (PPI) network was constructed aimed to determine hub genes and explore the potential regulatory mechanism. Ultimately, we combined with three independent databases, including GEO, the Cancer Genome Atlas (TCGA), and the Chinese Glioma Genome Atlas (CGGA), to independently validate the differential expression of hub genes. Our findings have provided novel insights for further understanding of the underlying molecular mechanisms and defined new biomarkers for PLGG progression and prognosis. Results Identification of differentially expressed genes Figure 1 shows the flow chart of the present study. Eventually, following the defined criteria, a total of 525 genes were identified differentially expressed between 15 PLGG samples and 34 PHGG samples from GSE50161 datasets. These DEGs were consisted of 336 down-regulated and 189 up-regulated DEGs. The volcano plot (Figure 2A) was used to directly show the distribution of DEGs. Similarly, the heat map (Figure 2B) presented the expressions of partial DEGs in each samples, respectively. Functional enrichment and pathway analyses based on differentially expressed genes GO enrichment and KEGG pathway analyses were performed in "clusterProfiler" package, which illustrated the biological function and the potential mechanism of DEGs. As shown in Figure 3A, the top 5 GO terms demonstrated that up-regulated DEGs mainly enriched in cell division, mitotic nuclear division, and DNA replication at category BP, with its in the nucleus, cytoplasm, nucleoplasm at category CC and protein binding, ATP binding, chromatin binding at category MF. Whereas the top 5 GO terms of down-regulated DEGs were visualized in Figure 3B, illustrating that these DEGs significantly enriched in signal transduction, cell adhesion, and chemical synaptic transmission at category BP, with its in integral component of membrane, plasma membrane, extracellular exosome at category CC and calcium ion binding, growth factor activity, and heparin binding at category MF. All GO enrichment results were detailly showed in Table 1. Table 1 The results of GO enrichment analysis of differentially expressed genes. Genes Category GO ID Term Count P value Upregulated biological process GO:0051301 cell division 44 2.40E-34 GO:0007067 mitotic nuclear division 35 9.73E-29 GO:0007062 sister chromatid cohesion 25 2.97E-26 GO:0008283 cell proliferation 22 1.74E-10 GO:0006260 DNA replication 17 5.04E-12 cell component GO:0000777 condensed chromosome kinetochore 23 1.37E-25 GO:0005634 nucleus 98 1.01E-12 GO:0005737 cytoplasm 79 4.92E-06 GO:0005654 nucleoplasm 72 4.34E-16 GO:0005829 cytosol 62 1.01E-07 molecular function GO:0005515 protein binding 124 4.05E-09 GO:0005524 ATP binding 33 2.06E-05 GO:0042802 identical protein binding 17 0.002952 GO:0003682 chromatin binding 15 3.29E-05 GO:0019901 protein kinase binding 14 9.02E-05 Downregulated biological process GO:0007165 signal transduction 39 5.47E-05 GO:0007155 cell adhesion 27 6.28E-08 GO:0007268 chemical synaptic transmission 18 6.32E-07 GO:0034765 regulation of ion transmembrane transport 14 4.26E-08 GO:0071805 potassium ion transmembrane transport 13 8.82E-07 cell component GO:0016021 integral component of membrane 134 1.90E-09 GO:0005886 plasma membrane 115 1.02E-09 GO:0070062 extracellular exosome 60 0.027112 GO:0005576 extracellular region 52 4.09E-06 GO:0005887 integral component of plasma membrane 47 6.72E-06 molecular function GO:0005509 calcium ion binding 31 6.83E-07 GO:0008083 growth factor activity 10 9.28E-04 GO:0008201 heparin binding 9 0.003398 GO:0030246 carbohydrate binding 8 0.032454 GO:0005249 voltage-gated potassium channel activity 6 0.001965 KEGG pathway analysis revealed that for up-regulated DEGs, Cell cycle, Oocyte meiosis, Progesterone-mediated oocyte maturation, Fanconi anemia pathway, and p53 signaling pathway remained significant (Figure 4A); for down-regulated DEGs, Cell adhesion molecules (CAMs), Circadian entrainment, Protein digestion and absorption, Axon guidance, and Neuroactive ligand-receptor interaction were rather significant (Figure 4B). The results were listed in details in Table 2. Table 2 The results of KEGG pathway analysis of differentially expressed genes. Genes KEGG ID Term Count P value Upregulated hsa04110 Cell cycle 17 1.09E-14 hsa04114 Oocyte meiosis 9 7.45E-06 hsa03460 Fanconi anemia pathway 6 1.21E-04 hsa04115 p53 signaling pathway 6 3.69E-04 hsa04914 Progesterone-mediated oocyte maturation 6 0.001223 Downregulated hsa04514 Cell adhesion molecules (CAMs) 12 3.08E-05 hsa04080 Neuroactive ligand-receptor interaction 12 0.008221 hsa04360 Axon guidance 10 3.28E-04 hsa04974 Protein digestion and absorption 8 7.75E-04 hsa04713 Circadian entrainment 7 0.005873 Construction of PPI network and identification of hub genes As shown in Figure 5, a total of 217 nodes and 3299 edges were mapped in the PPI network. "CentiScape" plug‐in was firstly applied to calculate each node degree that represented the significant interaction within given genes. Furthermore, top ten nodes, including CDK1, BUB1, CDC20, CCNA2, KIF11, TRIP13, ITGA2, MAD2L1, PBK and CCNB1, were selected as the hub genes that were evaluated using 12 algorithms (Figure 6A). Subsequently, Figure 6B showed that most hub genes markedly participated in mitotic nuclear division, sister chromatid segregation and cell cycle checkpoint activities, indicating that hub genes may play a role in regulating the stability of genetic material in cell cycle for pediatric gliomas. Validation of hub genes In this work, multiple independent databases were used to verify whether the expression level of hub genes, such as CDK1, BUB1, CDC20, and CCNA2, was significantly different in the different grades of pediatric gliomas (Figure S1). And the results illustrated that all hub genes expression were markedly higher in PHGG samples than in PLGG samples with P less than 0.05 (Figure 7A-C), suggesting that the high expression of given hub genes may contribute to pediatric gliomas progression. Comparison of the encoded protein expression and survival analysis According to CPTAC dataset, CDK1, KIF11, TRIP13, MAD2L1 and PBK were found that actively participated in protein encoding in pediatric brain cancers. Interestingly, we found that the expression level of protein of above hub genes was significantly higher in HGG samples than in LGG samples with a p-value <0.05 (Figure 8A-E). In addition, survival analyses also revealed that pediatric gliomas cases with low expression of those hub genes tended to have a better prognosis than those with expression of high levels (Figure 9). Therefore, these founding indicated that given key genes may serve as the vital biomarkers in the progression and prognosis of patients. Discussion Pediatric gliomas represent the most frequent type in childhood CNS tumors. Currently, PLGG patients had a positive prognosis and survival rate though early radiotherapy and chemotherapy and surgical resection. Nevertheless, once occur the transformation into higher-grade lesions, the majority of patients will die within two years [29]. Therefore, identification of molecular biomarkers and exploration of the regulatory mechanism of PLGG progression are extremely beneficial for the development of precise targeted therapy that could effectively prevent the occurrence of malignant transformation, whereby assist in the prognosis improvement for PLGG patients. In this work, we have identified 525 genes that were differentially expressed between 34 PHGG cases and 15 PLGG cases with the use of microarray. Furthermore, through integrated bioinformatics analysis, functional enrichment was performed to reveal the underlying mechanism of these DEGs. GO and KEGG pathway analyses annotated that up-regulated DEGs mainly participated in the regulation of cell cycle, including cell division, mitotic nuclear division, and DNA replication, while down-regulated DEGs were significantly associated with signal transduction, cell adhesion, and chemical synaptic transmission. Subsequently, top ten DEGs that served as the key regulators in PPI network were selected as hub genes, such as CDK1, BUB1, CDC20, CCNA2, KIF11, TRIP13, ITGA2, MAD2L1, PBK, and CCNB1. The expression levels of all hub genes were found higher in PHGG samples than that in PLGG samples, which were further validated using three independent databases. Interestingly, we then found that the protein expression of CDK1, TRIP13, KIF11, MAD2L1, and PBK was significantly increased in PHGG tissues when compared to PLGG tissues. Additionally, these up-expressed hub genes were suggested a worse overall survival for low-grade patients. Therefore, our results indicated that defined ten hub genes may serve as the reliable biomarkers for progression and prognosis of PLGG. CDK1, as a key gene of mitotic catastrophe, usually encodes the highly conserved protein that plays a vital role in cell cycle regulation [30]. Liu et al reported that down-expressed CDK1 could induced abnormal mitotic by interference with G2/M cell cycle checkpoint, whereby inhibited malignant proliferation of glioma cells [31]. Additionally, plenty of publications have also confirmed that the expression level of CDK1 was significantly increased along with increasing malignancy of gliomas [32, 33]. CDK1 was expected as potential target for the cure of glioblastoma [34]. BUB1 and CDC20 are the important component of spindle assembly checkpoint (SAC) complex that conduct the normal segregation of chromosomes and maintain genomic stability in mitosis [35, 36]. Overexpression and mutations in this two gene were found closely associated with pediatric malignant glioma growth and invasion [37]. Importantly, the up-regulated expression levels of BUB1 and CDC20 were not only significantly correlated with glioma grades but prompted a poor overall survival for patients [38, 39]. CCNA2 and CCNB1 belong to the important members of cyclins family and actively participate in various biological processes of cancer cells, including cell cycle regulatory, cell proliferation, migration, and apoptosis [40, 41]. Yang et al [38] reported that the expression of CCNA2 in glioblastoma was more increased than normal samples, leading to the low overall survival and poor prognosis. While Wang et al [42] reported that abnormal CCNB1 expression may impact the development of glioma, especially in tumor proliferation. In addition, several studies suggested that suppression of CCNB1 had therapeutic potential in enhancing the sensitivity of glioma patients to chemotherapy [42, 43]. KIF11, encoding mitotic Kinesin-like protein, is defined as the hub-bottleneck gene that contributed to cancers via inducing the chromosome instability [44]. Low-expressed KIF11 could block the growth and promote the apoptosis of glioblastomas, and even prevent the malignant progression of low-grade glioma cells [45, 46]. Importantly, KIF11 has been regarded as the drug target to enhance the anti-proliferative effect on gliomas [47]. Additionally, four new genes including TRIP13, CDCA8, MAD2L1, and PBK were also the first time to be identified related to the progression and prognosis of PLGG in this study. TRIP13, a member of the AAA + ATPase super-family, plays an important role in the cell cycle metaphase checkpoint of cancer cells [48]. Meanwhile, TRIP13 was also confirmed as a tumor promoting factor and a predictor for poor prognosis in prostate cancer and hepatocellular carcinoma [49, 50]. For malignant gliomas, silenced TRIP13 could inhibited the proliferation, migration and invasion abilities by FBXW7/c-MYC pathway [51]. CDCA8 is one of the components of chromosomal passenger complex (CPC), which usually regulates the spindle assembly and chromosome segregation in mitosis [52]; In fact, CDCA8 was reported as a putative oncogene for its overexpression would lead to the malignant progression and proliferation in tumor cells [53]. Notably, Zhou et al [54] illustrated that CDCA8 may promoted the transformation into higher-grade lesions of gliomas by participating in cell cycle and p53 signaling pathway, associated with the poor overall survival. MAD2L1 is well known for its critical role in major mitotic spindle assembly checkpoint (SAC) and accurate chromosome segregation [55]. MAD2L1 knockdown suppressed cell survival and migration of glioma, indicating MAD2L1 is required for maintaining glioma cells malignancy [39]. Several publications have reported that the expression level of PBK was markedly increased in high grade gliomas tissues than that in normal tissues, which contributed to recurrence in glioblastoma tumors [56, 57]. Meanwhile, Joel et al [58] confirmed that using pharmacological PBK inhibitor was an effective way to diminish growth of glioblastoma tumors through mice experiment. Although current evidence indicates that genetic instability comprises the primary cause of malignant transformation for pediatric gliomas, the potential molecular mechanism is not fully understood. Aberrant cell cycle activities that may lead to malignant progression and poor prognosis for PLGG patients, are becoming the focus of attention in recent studies. For instance, Lal et al [59] reported that suppressed CDK1 and CCNB1 expression could cause G2/M cell cycle arrest, which further inhibited malignant proliferation and invasion of glioma cells. In the present study, hub genes were found as the key members of cell cycle, which were extremely vital for genetic stability, cell cycle checkpoint and chromosome segregation. Significantly, functional enrichment and pathway analysis revealed that given hub genes are mainly enriching in cell cycle activities, which further confirms the reliability of our results. Interestingly, we also found that the encoding protein expression of CDK1, TRIP13, KIF11, MAD2L1, and PBK was significantly increased in PHGG tissues compared to PLGG tissues. Therefore, given ten hub genes may serve as reliable biomarkers associated with PLGG progression and are promising to be potential therapeutic targets for PLGG. Although this work is the first time to identify hub genes associated with PLGG progression and prognosis, some limitations still cannot be ignored. First, some gene expression profiles with incomplete clinical information or derived from cell lines were excluded, which may cause bias to a certain extent. Second, our results were conducted by integrating bioinformatics analysis, and have been verified using different databases, but insufficient tissues collected were not applicable for further verification due to the special characteristic and limited number of pediatric gliomas. Finally, it urgently needs large-scale clinical experiments to further confirm the potential of these defined hub genes as the biomarkers and therapeutic targets for PLGG progression. Conclusions In conclusion, based on integrated bioinformatics analysis, ten hub genes (CDK1, BUB1, CDC20, CCNA2, KIF11, TRIP13, CDCA8, MAD2L1, PBK, CCNB2) and underlying molecular mechanism involved were identified that were significantly associated with PLGG progression. Multiple databases then confirmed that given hub genes were not only markedly increased with increasing malignancy of pediatric gliomas, but related to the prognosis for patients. We also found that the encoding protein expression of several hub genes, including CDK1, KIF11, TRIP13, MAD2L1, PBK, was statistically higher in PHGG tissues than that in PLGG tissues. Besides, functional enrichment and KEGG pathway analyses showed that hub genes were mainly enriched in cell cycle activities, which may participate in the PLGG progression via regulating the genetic stability. These findings provided novel biomarkers and potential therapeutic targets for PLGG progression, which may promote further understanding of the underlying molecular mechanisms, whereby assists in the improvement of precise diagnosis and targeted therapy for pediatric gliomas patients in future. Materials And Methods Microarray data download RNA-Seq expression profiles (GSE50161, GSE86574) were obtained with the following keywords: "Homo sapiens", "pediatric glioma", and "tissue" according to GEO database ( http://www.ncbi.nlm.nih.gov/geo/ ). Meanwhile, all gene expression data as well as the clinical information of pediatric gliomas were also downloaded from TCGA Genomic Data Commons Data Portal ( https://portal.gdc.cancer.gov/ ) and CGGA database ( http://www.cgga.org.cn/ ). Notably, such gene chips derived from cell lines were excluded. Finally, a total of 58 PHGG and 60 PLGG samples were collected in our study. For GEO data sets, all gene expression profiles were based on the same GPL570 platform (HG‐U133_Plus_2, Affymetrix Human Genome U133 Plus 2.0 Array). And then, probe IDs were converted to gene symbols using platform annotation files. For TCGA cohorts, the original expression data (level-3 raw count data) of pediatric glioma samples that fragments per Kilobase Million (FPKM) files were transformed into Transcripts Per Million (TPM), which were then integrated s into a matrix file and converted the gene ID on ensembl website ( http://asia.ensembl.org/index.html ). If matched with multiple IDs, the gene expression values were averaged. Differentially expressed analysis Based on R studio, the original data pretreatment of GSE50161 was conducted with the robust multiarray average algorithm (RMA)[18]. Furthermore, the classical Bayesian algorithm was utilized to screen DEGs between PHGG and PLGG groups using "limma" package [19]. False-positive discovery (adjust p-value) 1.5 were set as cut-off criteria for significant DEGs. Functional enrichment and pathway analyses of DEGs To deeply annotate the biological functions of DEGs and explore the potential molecular mechanism, GO enrichment and KEGG pathway analyses were achieved using R package "clusterProfiler" [20], of which the results were visualized in "ggplot2" package [21]. The GO terms were further divided into biological process (BP), molecular functions (MF) and cellular components (CC). A p-value <0.05 was considered statistically significant. Construction of protein-protein interaction network among DEGs The interactive relationships of DEGs were mapped onto the Search Tool for the Retrieval of Interacting Genes (STRING, version 10.0, http://string.embl.de/ ) for further analysis [22]. The PPI network was then constructed with setting the minimum required interaction score being 0.7. The result was visualized by Cytoscape software (version 3.7.2) [23], where the "CentiScape" plug‐in was utilized to calculate the degree of nodes [24]. In addition, the "cytohubba" plug-in was applied to screen hub genes, which was performed with 12 independent algorithms including MCC, MNC, Degree and so on, where the higher score in different algorithms, the more important DEGs in disease progression [25]. R package "GOplot" was used to perform the functional annotation of hub genes. Validation of hub genes The CGGA database, as an open-access and user-friendly glioma database on Chinese cohorts over 2,000 samples, contains DNA methylation, mRNA microarray and matched clinical data and so on. Thus, in order to verify whether the hub genes were differentially expressed in the progression of pediatric glioma. The based clinical information and the mRNA expression data of pediatric glioma cases were also obtained from CGGA database, respectively [26]. Thus, the validation derived from three independent databases was considered to make the work more convincing. Comparing the protein expression of hub genes A user-friendly online tool, UALCAN ( http://ualcan.path.uab.edu/index.html ), was utilized to conduct the comparison of the expression of protein encoded by hub genes in pediatric brain cancer including various subgroups (race, gender, tumor histology). The available data was based on Clinical Proteomic Tumor Analysis Consortium (CPTAC) Confirmatory/Discovery dataset [27]. Importantly, we paid special attention to identifying whether the coding protein expression of key genes was significantly different between PLGG samples and PHGG samples. Survival analysis of hub genes GEPIA, a web server for analyzing gene expression data from the TCGA and the GTEx projects ( http://gepia.cancer-pku.cn/ ), has been widely applied to perform the differentially expressed analysis, pathological staging, and survival analysis of patients [28]. Therefore, we further explored whether the expression of hub genes was associated with the prognosis through the survival analyses, where the mean expression was set as the cut-off value to divide patients into high- and low-expressed groups. Statistical analysis Statistical analysis of the data was performed using software GraphPad Prism 8 and R studio 3.6.1. The expression data was presented as mean ± SD. Statistical comparisons of key molecules expression data were subjected to the Mann-Whitney test. P-value less than 0.05 was considered statistically significant. Declarations Acknowledgements The authors gratefully acknowledge contributions from the GEO, TCGA and CGGA databases. Author’s contribution All authors participated in this study. CZ conducted statistical analysis and data management, CZ and ZZ edited and revised the manuscript, HT reviewed the manuscript. All authors read and approved the final manuscript. Funding none. Availability of data and materials The raw data of this study derived from the GEO database ( http://www.ncbi.nlm.nih.gov/geo/ ), TCGA database ( https://portal.gdc.cancer.gov/ ) and CGGA database ( http://www.cgga.org.cn/ ), which are publicly available databases. Ethics approval and consent to participate not necessary. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. References Sturm D, Pfister SM, Jones DTW: Pediatric Gliomas: Current Concepts on Diagnosis, Biology, and Clinical Management. J Clin Oncol 2017, 35(21):2370-2377. Larouche V, Toupin AK, Lalonde B, Simonyan D, Jabado N, Perreault S: Incidence trends in pediatric central nervous system tumors in Canada: a 15 years report from Cancer and Young People in Canada (CYP-C) registry. 2020, 2(1):vdaa012. Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella-Branger D, Cavenee WK, Ohgaki H, Wiestler OD, Kleihues P, Ellison DW: The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary. Acta Neuropathol 2016, 131(6):803-820. Jones DTW, Banito A, Gr¨¹newald TGP, Haber M, J?ger N, Kool M, Milde T, Molenaar JJ, Nabbi A, Pugh TJ, et al: Molecular characteristics and therapeutic vulnerabilities across paediatric solid tumours. Nat Rev Cancer 2019, 19(8):420-438. Collins KL, Pollack IF: Pediatric Low-Grade Gliomas. Cancers (Basel) 2020, 12(5). Mistry M, Zhukova N, Merico D, Rakopoulos P, Krishnatry R, Shago M, Stavropoulos J, Alon N, Pole JD, Ray PN, et al: BRAF mutation and CDKN2A deletion define a clinically distinct subgroup of childhood secondary high-g rade glioma. J Clin Oncol 2015, 33(9):1015-1022. Braunstein S, Raleigh D, Bindra R, Mueller S, Haas-Kogan D: Pediatric high-grade glioma: current molecular landscape and therapeutic approaches. J Neurooncol 2017, 134(3):541-549. Wisoff JH, Sanford RA, Heier LA, Sposto R, Burger PC, Yates AJ, Holmes EJ, Kun LE: Primary neurosurgery for pediatric low-grade gliomas: a prospective multi-institutional study from th e Children's Oncology Group. Neurosurgery 2011, 68(6):1548-1554; discussion 1554-1545. Korshunov A, Capper D, Reuss D, Schrimpf D, Ryzhova M, Hovestadt V, Sturm D, Meyer J, Jones C, Zheludkova O, et al: Histologically distinct neuroepithelial tumors with histone 3 G34 mutation are molecularly similar a nd comprise a single nosologic entity. Acta Neuropathol 2016, 131(1):137-146. Sturm D, Witt H, Hovestadt V, Khuong-Quang DA, Jones DT, Konermann C, Pfaff E, T?njes M, Sill M, Bender S, et al: Hotspot mutations in H3F3A and IDH1 define distinct epigenetic and biological subgroups of glioblasto ma. Cancer Cell 2012, 22(4):425-437. Mueller S, Phillips J, Onar-Thomas A, Romero E, Zheng S, Wiencke JK, McBride SM, Cowdrey C, Prados MD, Weiss WA, et al: PTEN promoter methylation and activation of the PI3K/Akt/mTOR pathway in pediatric gliomas and influe nce on clinical outcome. Neuro Oncol 2012, 14(9):1146-1152. Ryall S, Zapotocky M, Fukuoka K, Nobre L, Guerreiro Stucklin A, Bennett J, Siddaway R, Li C, Pajovic S, Arnoldo A, et al: Integrated Molecular and Clinical Analysis of 1,000 Pediatric Low-Grade Gliomas. Cancer Cell 2020, 37(4):569-583.e565. Li D, Hao X, Song Y: Identification of the Key MicroRNAs and the miRNA-mRNA Regulatory Pathways in Prostate Cancer by Bioi nformatics Methods. Biomed Res Int 2018, 2018:6204128. Jiang J, Bi Y, Liu XP, Yu D, Yan X, Yao J, Liu T, Li S: To construct a ceRNA regulatory network as prognostic biomarkers for bladder cancer. J Cell Mol Med 2020, 24(9):5375-5386. Dalerba P, Sahoo D, Paik S, Guo X, Yothers G, Song N, Wilcox-Fogel N, Forg¨® E, Rajendran PS, Miranda SP, et al: CDX2 as a Prognostic Biomarker in Stage II and Stage III Colon Cancer. N Engl J Med 2016, 374(3):211-222. Zhai Y, Chen Y, Li Q, Zhang L: Exploration of the hub genes and miRNAs in lung adenocarcinoma. Oncol Lett 2019, 18(2):1713-1722. Cheng W, Ren X, Zhang C, Cai J, Liu Y, Han S, Wu A: Bioinformatic profiling identifies an immune-related risk signature for glioblastoma. Neurology 2016, 86(24):2226-2234. Harbron C, Chang KM, South MC: RefPlus: an R package extending the RMA Algorithm. Bioinformatics 2007, 23(18):2493-2494. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK: limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 2015, 43(7):e47. Yu G, Wang LG, Han Y, He QY: clusterProfiler: an R package for comparing biological themes among gene clusters. Omics 2012, 16(5):284-287. Ito K, Murphy D: Application of ggplot2 to Pharmacometric Graphics. CPT Pharmacometrics Syst Pharmacol 2013, 2:e79. Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, Santos A, Doncheva NT, Roth A, Bork P, et al: The STRING database in 2017: quality-controlled protein-protein association networks, made broadly ac cessible. Nucleic Acids Res 2017, 45(D1):D362-d368. Killcoyne S, Carter GW, Smith J, Boyle J: Cytoscape: a community-based framework for network modeling. Methods Mol Biol 2009, 563:219-239. Jiang H, Ma R, Zou S, Wang Y, Li Z, Li W: Reconstruction and analysis of the lncRNA-miRNA-mRNA network based on competitive endogenous RNA reve al functional lncRNAs in rheumatoid arthritis. Mol Biosyst 2017, 13(6):1182-1192. Chin CH, Chen SH, Wu HH, Ho CW, Ko MT, Lin CY: cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol 2014, 8 Suppl 4:S11. Fang S, Liang J, Qian T, Wang Y, Liu X, Fan X, Li S, Wang Y, Jiang T: Anatomic Location of Tumor Predicts the Accuracy of Motor Function Localization in Diffuse Lower-Grad e Gliomas Involving the Hand Knob Area. AJNR Am J Neuroradiol 2017, 38(10):1990-1997. Chen F, Chandrashekar DS, Varambally S, Creighton CJ: Pan-cancer molecular subtypes revealed by mass-spectrometry-based proteomic characterization of more than 500 human cancers. Nat Commun 2019, 10(1):5679. Tang Z, Li C, Kang B, Gao G, Li C, Zhang Z: GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res 2017, 45(W1):W98-w102. Broniscer A, Gajjar A: Supratentorial high-grade astrocytoma and diffuse brainstem glioma: two challenges for the pediatric oncologist. Oncologist 2004, 9(2):197-206. Castedo M, Perfettini JL, Roumier T, Kroemer G: Cyclin-dependent kinase-1: linking apoptosis to cell cycle and mitotic catastrophe. Cell Death Differ 2002, 9(12):1287-1293. Liu WT, Chen C, Lu IC, Kuo SC, Lee KH, Chen TL, Song TS, Lu YL, Gean PW, Hour MJ: MJ-66 induces malignant glioma cells G2/M phase arrest and mitotic catastrophe through regulation of cyclin B1/Cdk1 complex. Neuropharmacology 2014, 86:219-227. Xie C, Xu M, Lu D, Zhang W, Wang L, Wang H, Li J, Ren F, Wang C: Candidate genes and microRNAs for glioma pathogenesis and prognosis based on gene expression profiles. Mol Med Rep 2018, 18(3):2715-2723. Geng RX, Li N, Xu Y, Liu JH, Yuan FE, Sun Q, Liu BH, Chen QX: Identification of Core Biomarkers Associated with Outcome in Glioma: Evidence from Bioinformatics Ana lysis. Dis Markers 2018, 2018:3215958. Peng C, Qi XM, Miao LL, Ren J: 1,2:5,6-dianhydrogalactitol inhibits human glioma cell growth in vivo and in vitro by arresting the c ell cycle at G2/M phase. Acta Pharmacol Sin 2017, 38(4):561-570. Vleugel M, Hoek TA, Tromer E, Sliedrecht T, Groenewold V, Omerzu M, Kops GJ: Dissecting the roles of human BUB1 in the spindle assembly checkpoint. J Cell Sci 2015, 128(16):2975-2982. Zhang Y, Li J, Yi K, Feng J, Cong Z, Wang Z, Wei Y, Wu F, Cheng W, Samo AA, et al: Elevated signature of a gene module coexpressed with CDC20 marks genomic instability in glioma. Proc Natl Acad Sci U S A 2019, 116(14):6975-6984. Morales AG, Pezuk JA, Brassesco MS, de Oliveira JC, de Paula Queiroz RG, Machado HR, Carlotti CG, Neder L, de Oliveira HF, Scrideli CA, Tone LG: BUB1 and BUBR1 inhibition decreases proliferation and colony formation, and enhances radiation sensit ivity in pediatric glioblastoma cells. Childs Nerv Syst 2013, 29(12):2241-2248. Yang L, Zeng W, Sun H, Huang F, Yang C, Cai X, Lu Y, Zeng J, Yang K: Bioinformatical Analysis of Gene Expression Omnibus Database Associates TAF7/CCNB1, TAF7/CCNA2, and G TF2E2/CDC20 Pathways with Glioblastoma Development and Prognosis. World Neurosurg 2020, 138:e492-e514. Bie L, Zhao G, Cheng P, Rondeau G, Porwollik S, Ju Y, Xia XQ, McClelland M: The accuracy of survival time prediction for patients with glioma is improved by measuring mitotic sp indle checkpoint gene expression. PLoS One 2011, 6(10):e25631. Kim DH, Park SE, Kim M, Ji YI, Kang MY, Jung EH, Ko E, Kim Y, Kim S, Shim YM, Park J: A functional single nucleotide polymorphism at the promoter region of cyclin A2 is associated with in creased risk of colon, liver, and lung cancers. Cancer 2011, 117(17):4080-4091. Dachineni R, Ai G, Kumar DR, Sadhu SS, Tummala H, Bhat GJ: Cyclin A2 and CDK2 as Novel Targets of Aspirin and Salicylic Acid: A Potential Role in Cancer Prevent ion. Mol Cancer Res 2016, 14(3):241-252. Wang Q, He Z, Chen Y: Comprehensive Analysis Reveals a 4-Gene Signature in Predicting Response to Temozolomide in Low-Grade Glioma Patients. Cancer Control 2019, 26(1):1073274819855118. Weber RG, Rieger J, Naumann U, Lichter P, Weller M: Chromosomal imbalances associated with response to chemotherapy and cytotoxic cytokines in human mali gnant glioma cell lines. Int J Cancer 2001, 91(2):213-218. Asbaghi Y, Thompson LL, Lichtensztejn Z, McManus KJ: KIF11 silencing and inhibition induces chromosome instability that may contribute to cancer. Genes Chromosomes Cancer 2017, 56(9):668-680. Valensin S, Ghiron C, Lamanna C, Kremer A, Rossi M, Ferruzzi P, Nievo M, Bakker A: KIF11 inhibition for glioblastoma treatment: reason to hope or a struggle with the brain? BMC Cancer 2009, 9:196. Venere M, Horbinski C, Crish JF, Jin X, Vasanji A, Major J, Burrows AC, Chang C, Prokop J, Wu Q, et al: The mitotic kinesin KIF11 is a driver of invasion, proliferation, and self-renewal in glioblastoma. Sci Transl Med 2015, 7(304):304ra143. Venuto S, Monteonofrio L, Cozzolino F, Monti M, Appolloni I, Mazza T, Canetti D, Giambra V, Panelli P, Fusco C, et al: TRIM8 interacts with KIF11 and KIFC1 and controls bipolar spindle formation and chromosomal stability. Cancer Lett 2020, 473:98-106. Zhang Q, Dong Y, Hao S, Tong Y, Luo Q, Aerxiding P: The oncogenic role of TRIP13 in regulating proliferation, invasion, and cell cycle checkpoint in NSCL C cells. Int J Clin Exp Pathol 2019, 12(9):3357-3366. Dong L, Ding H, Li Y, Xue D, Li Z, Liu Y, Zhang T, Zhou J, Wang P: TRIP13 is a predictor for poor prognosis and regulates cell proliferation, migration and invasion in prostate cancer. Int J Biol Macromol 2019, 121:200-206. Zhu MX, Wei CY, Zhang PF, Gao DM, Chen J, Zhao Y, Dong SS, Liu BB: Elevated TRIP13 drives the AKT/mTOR pathway to induce the progression of hepatocellular carcinoma via interacting with ACTN4. J Exp Clin Cancer Res 2019, 38(1):409. Zhang G, Zhu Q, Fu G, Hou J, Hu X, Cao J, Peng W, Wang X, Chen F, Cui H: TRIP13 promotes the cell proliferation, migration and invasion of glioblastoma through the FBXW7/c-MY C axis. Br J Cancer 2019, 121(12):1069-1078. Zhang C, Zhao L, Leng L, Zhou Q, Zhang S, Gong F, Xie P, Lin G: CDCA8 regulates meiotic spindle assembly and chromosome segregation during human oocyte meiosis. Gene 2020, 741:144495. Ci C, Tang B, Lyu D, Liu W, Qiang D, Ji X, Qiu X, Chen L, Ding W: Overexpression of CDCA8 promotes the malignant progression of cutaneous melanoma and leads to poor pr ognosis. Int J Mol Med 2019, 43(1):404-412. Zhou Y, Yang L, Zhang X, Chen R, Chen X, Tang W, Zhang M: Identification of Potential Biomarkers in Glioblastoma through Bioinformatic Analysis and Evaluating Their Prognostic Value. Biomed Res Int 2019, 2019:6581576. Wu D, Wang L, Yang Y, Huang J, Hu Y, Shu Y, Zhang J, Zheng J: MAD2-p31comet axis deficiency reduces cell proliferation, migration and sensitivity of microtubule-in terfering agents in glioma. Biochem Biophys Res Commun 2018, 498(1):157-163. Stangeland B, Mughal AA, Grieg Z, Sandberg CJ, Joel M, Nyg?rd S, Meling T, Murrell W, Vik Mo EO, Langmoen IA: Combined expressional analysis, bioinformatics and targeted proteomics identify new potential therape utic targets in glioblastoma stem cells. Oncotarget 2015, 6(28):26192-26215. Kruthika BS, Jain R, Arivazhagan A, Bharath RD, Yasha TC, Kondaiah P, Santosh V: Transcriptome profiling reveals PDZ binding kinase as a novel biomarker in peritumoral brain zone of glioblastoma. J Neurooncol 2019, 141(2):315-325. Joel M, Mughal AA, Grieg Z, Murrell W, Palmero S, Mikkelsen B, Fjerdingstad HB, Sandberg CJ, Behnan J, Glover JC, et al: Targeting PBK/TOPK decreases growth and survival of glioma initiating cells in vitro and attenuates t umor growth in vivo. Mol Cancer 2015, 14:121. Lal N, Nemaysh V, Luthra PM: Proteasome mediated degradation of CDC25C and Cyclin B1 in Demethoxycurcumin treated human glioma U87 MG cells to trigger G2/M cell cycle arrest. Toxicol Appl Pharmacol 2018, 356:76-89. Additional Declarations No competing interests reported. Supplementary Files FigureS1.TheheatmapoftenhubgenesinGSE86574ATCGAdatabaseBCGGAdatabaseC..tif Fig. S1 The heat map of ten hub genes expression in GSE86574 (A), TCGA database (B), CGGA database (C). Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1256036","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":76498661,"identity":"a83ccba5-293d-4cbc-b807-1e21fd08fd22","order_by":0,"name":"Hualin Tao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYBACPjDJw8DAz8x8+AFRWthgWiTb2dIMSNACBAbneRQkiNPCf8ZMmkfmcOLmwzwMBgw1NtFE2HLG2JiH57Cx2WHeAw8YjqXlNhDUwthj+BioRc7sMF+CAWPDYSK0MPMYHAZq4TFu5jGQIE4LGw/EFgNmorXwsBUbzuFJN5Y4DAzkBGL8ws9/eJvE2x7rxP7+w4cffKixIayFgYHDgIm3pxnCTiCsHATYHzD++FFHnNpRMApGwSgYmQAArd01+DQwxI8AAAAASUVORK5CYII=","orcid":"","institution":"The Affiliated Hospital of Southwest Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Hualin","middleName":"","lastName":"Tao","suffix":""},{"id":76498659,"identity":"753d3f7c-5d78-4f17-b9c7-f61ca19baadc","order_by":1,"name":"Chuyi Zeng","email":"","orcid":"","institution":"The Affiliated Hospital of Southwest Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chuyi","middleName":"","lastName":"Zeng","suffix":""},{"id":76498660,"identity":"a8377152-1031-4163-842b-a2df9c6d3e70","order_by":2,"name":"Zhihua Zuo","email":"","orcid":"","institution":"The Affiliated Hospital of Southwest Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhihua","middleName":"","lastName":"Zuo","suffix":""}],"badges":[],"createdAt":"2022-01-13 06:29:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1256036/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1256036/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":17385392,"identity":"057fcbef-9a01-474f-a503-d09cd09697fc","added_by":"auto","created_at":"2022-01-17 15:43:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":333389,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe flow chart of the current study. \u003c/strong\u003eDEGs, differentially expressed genes; GEO, Gene Expression Omnibus database; TCGA, the Cancer Genome Atlas; CGGA, Chinese Glioma Genome Atlas; GO, Gene ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes.\u003c/p\u003e","description":"","filename":"Figure1.Theflowchartofthecurrentstudy..png","url":"https://assets-eu.researchsquare.com/files/rs-1256036/v1/46d94a40cccf627a9d0fb421.png"},{"id":17385391,"identity":"4cf5292f-f8aa-4b9e-8c9f-034199fbaef3","added_by":"auto","created_at":"2022-01-17 15:43:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":121057,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe volcano plot and heat map of DEGs.\u003c/strong\u003e (A) Volcano plot of all DEGs. (B) The heat map of partial DEGs. DEGs, differentially expressed genes; PLGG, pediatric low grade glioma; PHGG, pediatric high grade glioma.\u003c/p\u003e","description":"","filename":"Figure2.ThevolcanoplotandheatmapofDEGs..png","url":"https://assets-eu.researchsquare.com/files/rs-1256036/v1/2794ff8fd033a3a44b2c8da3.png"},{"id":17385390,"identity":"acde6ce8-3333-4b58-ba8c-9740354fbb89","added_by":"auto","created_at":"2022-01-17 15:43:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":91875,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGO enrichment analysis of DEGs. \u003c/strong\u003e(A) GO analysis of upregulated DEGs. (B) GO analysis of downregulated DEGs. DEGs, differentially expressed genes; GO, Gene ontology.\u003c/p\u003e","description":"","filename":"Figure3.GOenrichmentanalysisofDEGs..png","url":"https://assets-eu.researchsquare.com/files/rs-1256036/v1/250d7b916ca8cdc46ced8272.png"},{"id":17385109,"identity":"f56be369-3408-4186-94e4-7df91122d2a4","added_by":"auto","created_at":"2022-01-17 15:40:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":98492,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKEGG pathway analysis of DEGs. \u003c/strong\u003e(A) KEGG analysis of upregulated DEGs. (B) KEGG analysis of downregulated DEGs. DEGs, differentially expressed genes; KEGG, Kyoto Encyclopedia of Genes and Genomes.\u0026nbsp;\u003c/p\u003e","description":"","filename":"Figure4.KEGGpathwayanalysisofDEGs..png","url":"https://assets-eu.researchsquare.com/files/rs-1256036/v1/841d3cdd76491e5991723784.png"},{"id":17385117,"identity":"31cfd41c-a912-4a16-86ce-3d08512f6940","added_by":"auto","created_at":"2022-01-17 15:40:39","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1204987,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProtein-protein interaction networks of DEGs. \u003c/strong\u003eThe color gradually darkens with the degree of DEGs. DEGs, differentially expressed genes.\u003c/p\u003e","description":"","filename":"Figure5.ProteinproteininteractionnetworksofDEGs..png","url":"https://assets-eu.researchsquare.com/files/rs-1256036/v1/2ca6608198e7053686c7f192.png"},{"id":17385111,"identity":"1bbcd73e-6767-419c-88a6-b0385471534c","added_by":"auto","created_at":"2022-01-17 15:40:38","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":494237,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe interaction and biological function of hub genes. \u003c/strong\u003e(A) The interaction of top ten hub genes. (B) Biological function of top 20 genes.\u003c/p\u003e","description":"","filename":"Figure6.Theinteractionandbiologicalfunctionofhubgenes..png","url":"https://assets-eu.researchsquare.com/files/rs-1256036/v1/6532379a480c6571502e330d.png"},{"id":17385114,"identity":"0146a1f9-68a4-4b01-9ae9-a9cdf00b14b6","added_by":"auto","created_at":"2022-01-17 15:40:39","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1274397,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe validation of ten hub genes expression between PHGG samples and PLGG samples. \u003c/strong\u003e(A) GSE86574. (B) TCGA database. (C) CGGA database. *P\u0026lt;0.05, **P\u0026lt;0.01, ***P\u0026lt;0.0001. PLGG, pediatric low grade glioma; PHGG, pediatric high grade glioma.\u0026nbsp;\u003c/p\u003e","description":"","filename":"Figure7.ThevalidationoftenhubgenesexpressionbetweenPHGGsamplesandPLGGsamples..png","url":"https://assets-eu.researchsquare.com/files/rs-1256036/v1/a20bb12d6bd5dfefa2ae4901.png"},{"id":17385115,"identity":"64bbbb93-6897-4d89-8933-4d36b59f5cde","added_by":"auto","created_at":"2022-01-17 15:40:39","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":148396,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe comparison of expression level of protein encoded by hub genes. \u003c/strong\u003eProtein expression of CDK1 (A), KIF11 (B), TRIP13 (C), MAD2L1 (D), PBK (E).\u003c/p\u003e","description":"","filename":"Figure8.Thecomparisonofexpressionlevelofproteinencodedbyhubgenes..png","url":"https://assets-eu.researchsquare.com/files/rs-1256036/v1/6a47f5368f1a22f436685b02.png"},{"id":17385112,"identity":"182fbd92-b918-4b65-a1ac-1d815e8b42fc","added_by":"auto","created_at":"2022-01-17 15:40:39","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":408264,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSurvival analyses of given ten hub genes in GEPIA database.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure9.SurvivalanalysesofgiventenhubgenesinGEPIAdatabase..png","url":"https://assets-eu.researchsquare.com/files/rs-1256036/v1/0f3b21b6b5bef49b4105be14.png"},{"id":17385399,"identity":"6ad9f7f5-0729-49d9-9076-f5e91df8e31b","added_by":"auto","created_at":"2022-01-17 15:43:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1606903,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1256036/v1/28af9267-6bf4-491c-b8b8-e6bc503fd57a.pdf"},{"id":17385393,"identity":"4ae8c4d5-10f0-4ec3-bea8-efbf80b0d843","added_by":"auto","created_at":"2022-01-17 15:43:39","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":743444,"visible":true,"origin":"","legend":"\u003cp\u003eFig. S1 The heat map of ten hub genes expression in GSE86574 (A), TCGA database (B), CGGA database (C).\u003c/p\u003e","description":"","filename":"FigureS1.TheheatmapoftenhubgenesinGSE86574ATCGAdatabaseBCGGAdatabaseC..tif","url":"https://assets-eu.researchsquare.com/files/rs-1256036/v1/57a7529b58521fdfdecbdd56.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eIdentification of Hub Genes and Molecular Mechanism Associated With the Progression of Pediatric Low Grade Gliomas\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003ePediatric low-grade glioma (PLGG) is the most common central nervous system (CNS) tumors in children, accounting for over 30%\u0026nbsp;case [1, 2]. Like adult low-grade glioma, according to the 2016 World Health Organization (WHO) classification of CNS tumors,\u0026nbsp;PLGG usually encompasses pilocytic astrocytoma, ganglioglioma, subependymal giant cell astrocytoma and so on [3]. But of note, current evidences indicated that gliomas were markedly distinct between children and adults in cellular origins and fundamental molecular and genetic heterogeneity [4], where these differences were commonly ignored in previous studies that explored the development and progression of gliomas [5]. PLGG patients also have showed a favorable prognosis after surgical resection and evolve less frequently into higher-grade lesions compared to adults [6]. However, malignant transformation of PLGG or the remaining residual tumor after surgery will significantly limit the 5-year survival rate of patients, which can drop below 20% [7, 8]. Importantly, the\u0026nbsp;\u0026quot;druggable\u0026quot;\u0026nbsp;targets for pediatric patients were difficult to broadly realize due to the unclear molecular mechanism [4]. Thus, the in-depth study of the underlying mechanism of PLGG progression assists in administering more precise targeted therapy and\u0026nbsp;improvement of prognosis for pediatric patients.\u003c/p\u003e\n\u003cp\u003eIn recent years, numerous studies have explored the potential factors leading to higher-grade lesions of PLGG at molecular genetic level, including gene mutation, deletions, DNA methylation, and so on [1, 9-11]. For instance, Mistry et al. 6 reported that BRAF V600E mutations and CDKN2A deletions could increase the risk of transformation of PLGG to malignancy. Besides, the RAS-mitogen-activated protein kinase (RAS/MAPK) pathway was considered as the main pathway to result in the genetic alterations associated with the progression of PLGG [12].\u0026nbsp;In contrast, the potential functional genomics and biological functions at the molecular level as well as its regulatory mechanism, which were closely associated with the progression of PLGG to PHGG were rarely reported.\u003c/p\u003e\n\u003cp\u003eMicroarray, as a high-throughput platform, has been broadly applied to identify tumor-related genes and new diagnostic biomarkers in various types of cancer, including prostate cancer [13], bladder cancer [14], Colon Cancer [15], and lung adenocarcinoma [16]. Meanwhile, large scale gene expression profiling analyses have been contributed to promote the development of molecular diagnosis and prognosis prediction and targeted therapy for cancer patients by the use of microarrays [17]. In the present study, microarray data was the first time to applied in identification of the key genes and exploration of their interaction mechanism for PLGG progression by integrated bioinformatics analysis. The differentially expressed genes (DEGs) were firstly screened out between PLGG tissues and PHGG tissues that were obtained from the Gene Expression Omnibus (GEO) database. Gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were achieved to annotate the biological functions of DEGs. Furthermore, the protein-protein interaction (PPI) network was constructed aimed to determine hub genes and explore the potential regulatory mechanism. Ultimately, we combined with three independent databases, including GEO, the Cancer Genome Atlas (TCGA), and the Chinese Glioma Genome Atlas (CGGA), to independently validate the differential expression of hub genes. Our findings have provided novel insights for further understanding of the underlying molecular mechanisms and defined new biomarkers for PLGG progression and prognosis.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eIdentification of differentially expressed genes\u003c/h2\u003e\n\u003cp\u003eFigure 1 shows the flow chart of the present study. Eventually, following the defined\u0026nbsp;criteria,\u0026nbsp;a total of 525 genes were identified differentially expressed between 15 PLGG samples and 34 PHGG samples from GSE50161 datasets. These DEGs were consisted of 336 down-regulated and\u0026nbsp;189 up-regulated DEGs. The volcano plot (Figure 2A) was used to directly show the distribution of DEGs.\u0026nbsp;Similarly, the heat map (Figure 2B) presented the expressions of partial DEGs in each samples, respectively.\u003c/p\u003e\n\u003ch2\u003eFunctional enrichment and pathway analyses based on differentially expressed genes\u003c/h2\u003e\n\u003cp\u003eGO enrichment and KEGG pathway analyses were performed in\u0026nbsp;\u0026quot;clusterProfiler\u0026quot;\u0026nbsp;package, which illustrated the biological function and the potential mechanism of DEGs. As shown in Figure 3A,\u0026nbsp;the top 5 GO terms demonstrated that up-regulated DEGs mainly enriched in cell division, mitotic nuclear division, and DNA replication at category BP, with its in the nucleus, cytoplasm, nucleoplasm at category CC and protein binding, ATP binding, chromatin binding at category MF. Whereas the top 5 GO terms of down-regulated DEGs were visualized in\u0026nbsp;Figure 3B, illustrating that these DEGs significantly enriched in signal transduction, cell adhesion, and chemical synaptic transmission at category BP, with its in integral component of membrane, plasma membrane, extracellular exosome at category CC and calcium ion binding, growth factor activity, and heparin binding at category MF. All GO enrichment results were detailly showed in Table 1.\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003e\u003cstrong\u003e\u0026nbsp;The results of GO enrichment analysis of differentially expressed genes.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.218390804597702%\"\u003e\n \u003cp\u003eGenes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.35632183908046%\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.172413793103448%\"\u003e\n \u003cp\u003eGO ID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.96551724137931%\"\u003e\n \u003cp\u003eTerm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.551724137931035%\"\u003e\n \u003cp\u003eCount\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.735632183908047%\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"15\" valign=\"top\" width=\"13.218390804597702%\"\u003e\n \u003cp\u003eUpregulated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" width=\"17.35632183908046%\"\u003e\n \u003cp\u003ebiological process\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.172413793103448%\"\u003e\n \u003cp\u003eGO:0051301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.96551724137931%\"\u003e\n \u003cp\u003ecell division\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.551724137931035%\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.735632183908047%\"\u003e\n \u003cp\u003e2.40E-34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.85430463576159%\"\u003e\n \u003cp\u003eGO:0007067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"56.12582781456954%\"\u003e\n \u003cp\u003emitotic nuclear division\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.437086092715232%\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.582781456953642%\"\u003e\n \u003cp\u003e9.73E-29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.85430463576159%\"\u003e\n \u003cp\u003eGO:0007062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"56.12582781456954%\"\u003e\n \u003cp\u003esister chromatid cohesion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.437086092715232%\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.582781456953642%\"\u003e\n \u003cp\u003e2.97E-26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.85430463576159%\"\u003e\n \u003cp\u003eGO:0008283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"56.12582781456954%\"\u003e\n \u003cp\u003ecell proliferation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.437086092715232%\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.582781456953642%\"\u003e\n \u003cp\u003e1.74E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.85430463576159%\"\u003e\n \u003cp\u003eGO:0006260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"56.12582781456954%\"\u003e\n \u003cp\u003eDNA replication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.437086092715232%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.582781456953642%\"\u003e\n \u003cp\u003e5.04E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003ecell component\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.483443708609272%\"\u003e\n \u003cp\u003eGO:0000777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"44.90066225165563%\"\u003e\n \u003cp\u003econdensed chromosome kinetochore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.549668874172186%\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.066225165562914%\"\u003e\n \u003cp\u003e1.37E-25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.85430463576159%\"\u003e\n \u003cp\u003eGO:0005634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"56.12582781456954%\"\u003e\n \u003cp\u003enucleus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.437086092715232%\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.582781456953642%\"\u003e\n \u003cp\u003e1.01E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.85430463576159%\"\u003e\n \u003cp\u003eGO:0005737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"56.12582781456954%\"\u003e\n \u003cp\u003ecytoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.437086092715232%\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.582781456953642%\"\u003e\n \u003cp\u003e4.92E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.85430463576159%\"\u003e\n \u003cp\u003eGO:0005654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.12582781456954%\"\u003e\n \u003cp\u003enucleoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.437086092715232%\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.582781456953642%\"\u003e\n \u003cp\u003e4.34E-16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.85430463576159%\"\u003e\n \u003cp\u003eGO:0005829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.12582781456954%\"\u003e\n \u003cp\u003ecytosol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.437086092715232%\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.582781456953642%\"\u003e\n \u003cp\u003e1.01E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003emolecular function\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.483443708609272%\"\u003e\n \u003cp\u003eGO:0005515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.90066225165563%\"\u003e\n \u003cp\u003eprotein binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.549668874172186%\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.066225165562914%\"\u003e\n \u003cp\u003e4.05E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.85430463576159%\"\u003e\n \u003cp\u003eGO:0005524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.12582781456954%\"\u003e\n \u003cp\u003eATP binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.437086092715232%\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.582781456953642%\"\u003e\n \u003cp\u003e2.06E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.85430463576159%\"\u003e\n \u003cp\u003eGO:0042802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.12582781456954%\"\u003e\n \u003cp\u003eidentical protein binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.437086092715232%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.582781456953642%\"\u003e\n \u003cp\u003e0.002952\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.85430463576159%\"\u003e\n \u003cp\u003eGO:0003682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.12582781456954%\"\u003e\n \u003cp\u003echromatin binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.437086092715232%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.582781456953642%\"\u003e\n \u003cp\u003e3.29E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.85430463576159%\"\u003e\n \u003cp\u003eGO:0019901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.12582781456954%\"\u003e\n \u003cp\u003eprotein kinase binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.437086092715232%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.582781456953642%\"\u003e\n \u003cp\u003e9.02E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"15\" valign=\"top\" width=\"13.218390804597702%\"\u003e\n \u003cp\u003eDownregulated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" width=\"17.35632183908046%\"\u003e\n \u003cp\u003ebiological process\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.172413793103448%\"\u003e\n \u003cp\u003eGO:0007165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.96551724137931%\"\u003e\n \u003cp\u003esignal transduction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.551724137931035%\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.735632183908047%\"\u003e\n \u003cp\u003e5.47E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.85430463576159%\"\u003e\n \u003cp\u003eGO:0007155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.12582781456954%\"\u003e\n \u003cp\u003ecell adhesion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.437086092715232%\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.582781456953642%\"\u003e\n \u003cp\u003e6.28E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.85430463576159%\"\u003e\n \u003cp\u003eGO:0007268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.12582781456954%\"\u003e\n \u003cp\u003echemical synaptic transmission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.437086092715232%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.582781456953642%\"\u003e\n \u003cp\u003e6.32E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.85430463576159%\"\u003e\n \u003cp\u003eGO:0034765\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.12582781456954%\"\u003e\n \u003cp\u003eregulation of ion transmembrane transport\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.437086092715232%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.582781456953642%\"\u003e\n \u003cp\u003e4.26E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.85430463576159%\"\u003e\n \u003cp\u003eGO:0071805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.12582781456954%\"\u003e\n \u003cp\u003epotassium ion transmembrane transport\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.437086092715232%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.582781456953642%\"\u003e\n \u003cp\u003e8.82E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003ecell component\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.483443708609272%\"\u003e\n \u003cp\u003eGO:0016021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.90066225165563%\"\u003e\n \u003cp\u003eintegral component of membrane\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.549668874172186%\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.066225165562914%\"\u003e\n \u003cp\u003e1.90E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.85430463576159%\"\u003e\n \u003cp\u003eGO:0005886\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.12582781456954%\"\u003e\n \u003cp\u003eplasma membrane\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.437086092715232%\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.582781456953642%\"\u003e\n \u003cp\u003e1.02E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.85430463576159%\"\u003e\n \u003cp\u003eGO:0070062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.12582781456954%\"\u003e\n \u003cp\u003eextracellular exosome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.437086092715232%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.582781456953642%\"\u003e\n \u003cp\u003e0.027112\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.85430463576159%\"\u003e\n \u003cp\u003eGO:0005576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.12582781456954%\"\u003e\n \u003cp\u003eextracellular region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.437086092715232%\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.582781456953642%\"\u003e\n \u003cp\u003e4.09E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.85430463576159%\"\u003e\n \u003cp\u003eGO:0005887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.12582781456954%\"\u003e\n \u003cp\u003eintegral component of plasma membrane\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.437086092715232%\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.582781456953642%\"\u003e\n \u003cp\u003e6.72E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003emolecular function\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.483443708609272%\"\u003e\n \u003cp\u003eGO:0005509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.90066225165563%\"\u003e\n \u003cp\u003ecalcium ion binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.549668874172186%\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.066225165562914%\"\u003e\n \u003cp\u003e6.83E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.85430463576159%\"\u003e\n \u003cp\u003eGO:0008083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.12582781456954%\"\u003e\n \u003cp\u003egrowth factor activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.437086092715232%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.582781456953642%\"\u003e\n \u003cp\u003e9.28E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.85430463576159%\"\u003e\n \u003cp\u003eGO:0008201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.12582781456954%\"\u003e\n \u003cp\u003eheparin binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.437086092715232%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.582781456953642%\"\u003e\n \u003cp\u003e0.003398\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.85430463576159%\"\u003e\n \u003cp\u003eGO:0030246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.12582781456954%\"\u003e\n \u003cp\u003ecarbohydrate binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.437086092715232%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.582781456953642%\"\u003e\n \u003cp\u003e0.032454\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.85430463576159%\"\u003e\n \u003cp\u003eGO:0005249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.12582781456954%\"\u003e\n \u003cp\u003evoltage-gated potassium channel activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.437086092715232%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.582781456953642%\"\u003e\n \u003cp\u003e0.001965\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eKEGG pathway analysis revealed that for up-regulated DEGs, Cell cycle, Oocyte meiosis, Progesterone-mediated oocyte maturation, Fanconi anemia pathway, and p53 signaling pathway remained significant (Figure 4A); for down-regulated DEGs, Cell adhesion molecules (CAMs), Circadian entrainment, Protein digestion and absorption, Axon guidance, and Neuroactive ligand-receptor interaction were rather significant (Figure 4B). The results were listed in details in Table 2.\u003c/p\u003e\n\u003cp id=\"isPasted\" style=\"text-align: center;\"\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003e\u003cstrong\u003e\u0026nbsp;The results of KEGG pathway analysis of differentially expressed genes.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.42948717948718%\"\u003e\n \u003cp\u003eGenes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.782051282051283%\"\u003e\n \u003cp\u003eKEGG ID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.11538461538461%\"\u003e\n \u003cp\u003eTerm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.493589743589743%\"\u003e\n \u003cp\u003eCount\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.179487179487179%\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" width=\"18.42948717948718%\"\u003e\n \u003cp\u003eUpregulated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.782051282051283%\"\u003e\n \u003cp\u003ehsa04110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.11538461538461%\"\u003e\n \u003cp\u003eCell cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.493589743589743%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.179487179487179%\"\u003e\n \u003cp\u003e1.09E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.895874263261298%\"\u003e\n \u003cp\u003ehsa04114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"57.76031434184676%\"\u003e\n \u003cp\u003eOocyte meiosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.412573673870334%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.931237721021612%\"\u003e\n \u003cp\u003e7.45E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.895874263261298%\"\u003e\n \u003cp\u003ehsa03460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"57.76031434184676%\"\u003e\n \u003cp\u003eFanconi anemia pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.412573673870334%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.931237721021612%\"\u003e\n \u003cp\u003e1.21E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.895874263261298%\"\u003e\n \u003cp\u003ehsa04115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"57.76031434184676%\"\u003e\n \u003cp\u003ep53 signaling pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.412573673870334%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.931237721021612%\"\u003e\n \u003cp\u003e3.69E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.895874263261298%\"\u003e\n \u003cp\u003ehsa04914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"57.76031434184676%\"\u003e\n \u003cp\u003eProgesterone-mediated oocyte maturation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.412573673870334%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.931237721021612%\"\u003e\n \u003cp\u003e0.001223\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" width=\"18.42948717948718%\"\u003e\n \u003cp\u003eDownregulated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.782051282051283%\"\u003e\n \u003cp\u003ehsa04514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.11538461538461%\"\u003e\n \u003cp\u003eCell adhesion molecules (CAMs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.493589743589743%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.179487179487179%\"\u003e\n \u003cp\u003e3.08E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.895874263261298%\"\u003e\n \u003cp\u003ehsa04080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"57.76031434184676%\"\u003e\n \u003cp\u003eNeuroactive ligand-receptor interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.412573673870334%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.931237721021612%\"\u003e\n \u003cp\u003e0.008221\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.895874263261298%\"\u003e\n \u003cp\u003ehsa04360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"57.76031434184676%\"\u003e\n \u003cp\u003eAxon guidance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.412573673870334%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.931237721021612%\"\u003e\n \u003cp\u003e3.28E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.895874263261298%\"\u003e\n \u003cp\u003ehsa04974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"57.76031434184676%\"\u003e\n \u003cp\u003eProtein digestion and absorption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.412573673870334%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.931237721021612%\"\u003e\n \u003cp\u003e7.75E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.895874263261298%\"\u003e\n \u003cp\u003ehsa04713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"57.76031434184676%\"\u003e\n \u003cp\u003eCircadian entrainment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.412573673870334%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.931237721021612%\"\u003e\n \u003cp\u003e0.005873\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch2\u003eConstruction of PPI network and identification of hub genes\u003c/h2\u003e\n\u003cp\u003eAs shown in Figure 5, a total of 217 nodes and 3299 edges were mapped in the PPI network.\u0026nbsp;\u0026quot;CentiScape\u0026quot;\u0026nbsp;plug‐in\u0026nbsp;was firstly applied to calculate each node degree that represented the significant interaction within given genes. Furthermore, top ten nodes, including CDK1, BUB1, CDC20, CCNA2, KIF11, TRIP13, ITGA2, MAD2L1, PBK and CCNB1, were selected as the hub genes that were evaluated using 12 algorithms (Figure 6A). Subsequently, Figure 6B showed that most hub genes markedly participated in mitotic nuclear division, sister chromatid segregation and cell cycle checkpoint activities, indicating that hub genes may play a role in regulating the stability of genetic material in cell cycle for pediatric gliomas.\u003c/p\u003e\n\u003ch2\u003eValidation of hub genes\u003c/h2\u003e\n\u003cp\u003eIn this work, multiple independent databases were used to verify whether the expression level of hub genes, such as CDK1, BUB1, CDC20, and CCNA2, was significantly different in the different grades of pediatric gliomas (Figure S1). And the results illustrated that all hub genes expression were markedly higher in PHGG samples than in PLGG samples with P less than 0.05 (Figure\u0026nbsp;7A-C), suggesting that the high expression of given hub genes may contribute to pediatric gliomas progression.\u003c/p\u003e\n\u003ch2\u003eComparison of the encoded protein expression and survival analysis\u003c/h2\u003e\n\u003cp\u003eAccording to CPTAC dataset, CDK1, KIF11, TRIP13, MAD2L1 and PBK were found that actively participated in protein encoding in pediatric brain cancers. Interestingly, we found that the expression level of protein of above hub genes was significantly higher in HGG samples than in LGG samples with a p-value \u0026lt;0.05 (Figure 8A-E). In addition, survival analyses also revealed that pediatric gliomas cases with low expression of those hub genes tended to have a better prognosis than those with expression of high levels (Figure 9). Therefore, these founding indicated that given key genes may serve as the vital biomarkers in the progression and prognosis of patients.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003ePediatric gliomas represent the most frequent type in childhood CNS tumors.\u0026nbsp;Currently, PLGG patients had a positive prognosis and survival rate though early radiotherapy and chemotherapy and surgical resection. Nevertheless, once occur the transformation into higher-grade lesions, the majority of patients will die within two years\u0026nbsp;[29]. Therefore, identification of molecular biomarkers and exploration of the regulatory mechanism of PLGG progression are extremely beneficial for the development of precise targeted therapy that could effectively prevent the occurrence of malignant transformation, whereby assist in the prognosis improvement for PLGG patients.\u003c/p\u003e\n\u003cp\u003eIn this work,\u0026nbsp;we have identified 525 genes that were differentially expressed between 34 PHGG cases and 15 PLGG cases with the use of microarray. Furthermore, through integrated bioinformatics analysis, functional enrichment was performed to reveal the underlying mechanism of these DEGs. GO and KEGG pathway analyses annotated that up-regulated DEGs mainly participated in the regulation of cell cycle, including\u0026nbsp;cell division, mitotic nuclear division, and DNA replication, while down-regulated DEGs were significantly associated with signal transduction, cell adhesion, and chemical synaptic transmission.\u0026nbsp;Subsequently, top\u0026nbsp;ten\u0026nbsp;DEGs that served as the key regulators in PPI network were selected as hub genes, such as CDK1, BUB1, CDC20, CCNA2, KIF11, TRIP13, ITGA2, MAD2L1, PBK, and CCNB1. The expression levels of all hub genes were found higher in PHGG samples than that in PLGG samples, which were further validated using three independent databases. Interestingly, we then found that the protein expression of CDK1, TRIP13, KIF11, MAD2L1, and PBK was significantly increased in PHGG tissues when compared to PLGG tissues. Additionally, these up-expressed hub genes were suggested a worse overall survival for low-grade patients. Therefore, our results indicated that defined\u0026nbsp;ten\u0026nbsp;hub genes may serve as the reliable biomarkers for progression and prognosis of PLGG.\u003c/p\u003e\n\u003cp\u003eCDK1, as a key gene of mitotic catastrophe, usually encodes the highly conserved protein that plays a vital role in cell cycle regulation [30]. Liu et al reported that down-expressed CDK1 could induced abnormal mitotic by interference with G2/M cell cycle checkpoint, whereby inhibited malignant proliferation of glioma cells [31]. Additionally, plenty of publications have also confirmed that the expression level of CDK1 was significantly increased along with increasing malignancy of gliomas [32, 33]. CDK1 was expected as potential target for the cure of glioblastoma [34]. BUB1 and CDC20 are the important component of spindle assembly checkpoint (SAC) complex that conduct the normal segregation of chromosomes and maintain genomic stability in mitosis [35, 36]. Overexpression and mutations in this two gene were found closely associated with pediatric malignant glioma growth and invasion [37]. Importantly, the up-regulated expression levels of BUB1 and CDC20 were not only significantly correlated with glioma grades but prompted a poor overall survival for patients [38, 39]. CCNA2 and CCNB1\u0026nbsp;belong to the important members of cyclins family and actively participate in various biological processes of cancer cells, including cell cycle regulatory, cell proliferation, migration, and apoptosis [40, 41]. Yang et al [38] reported that the expression of CCNA2 in glioblastoma was more increased than normal samples, leading to the low overall survival and poor prognosis. While Wang et al [42] reported that abnormal CCNB1 expression may impact the development of glioma, especially in tumor proliferation. In addition, several studies suggested that suppression of CCNB1 had therapeutic potential in enhancing the sensitivity of glioma patients to chemotherapy [42, 43]. KIF11, encoding mitotic Kinesin-like protein, is defined as the hub-bottleneck gene that contributed to cancers via inducing the chromosome instability\u0026nbsp;[44]. Low-expressed KIF11 could block the growth and promote the apoptosis of glioblastomas, and even prevent the malignant progression of low-grade glioma cells [45, 46]. Importantly, KIF11 has been regarded as the drug target to enhance the anti-proliferative effect on gliomas [47].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdditionally, four new genes including TRIP13, CDCA8, MAD2L1, and PBK were also the first time to be identified related to the progression and prognosis of PLGG in this study. TRIP13, a member of the AAA\u0026thinsp;+\u0026thinsp;ATPase super-family, plays an important role in the cell cycle metaphase checkpoint of cancer cells [48]. Meanwhile, TRIP13 was also\u0026nbsp;confirmed as a tumor promoting factor and a predictor for poor prognosis in prostate cancer and hepatocellular carcinoma [49, 50]. For malignant gliomas, silenced TRIP13 could inhibited the proliferation, migration and invasion abilities by FBXW7/c-MYC pathway [51]. CDCA8 is one of the components of chromosomal passenger complex (CPC), which usually regulates the spindle assembly and chromosome segregation in mitosis [52]; In fact, CDCA8 was reported as a putative oncogene for its overexpression would lead to the malignant progression and proliferation in tumor cells [53]. Notably, Zhou et al [54] illustrated that CDCA8 may promoted the transformation into higher-grade lesions of gliomas by participating in cell cycle and p53 signaling pathway, associated with the poor overall survival. MAD2L1 is well known for its critical role in major mitotic spindle assembly checkpoint (SAC) and accurate chromosome segregation [55]. MAD2L1 knockdown suppressed cell survival and migration of glioma, indicating MAD2L1 is required for maintaining glioma cells malignancy [39]. Several publications have reported that the expression level of PBK was markedly increased in high grade gliomas tissues than that in normal tissues, which contributed to recurrence in glioblastoma tumors [56, 57]. Meanwhile, Joel et al [58] confirmed that using pharmacological PBK inhibitor was an effective way to diminish growth of glioblastoma tumors through mice experiment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlthough current evidence indicates that genetic instability comprises the primary cause of malignant transformation for\u0026nbsp;pediatric\u0026nbsp;gliomas, the potential molecular mechanism is not fully understood. Aberrant cell cycle activities that may lead to malignant progression and poor prognosis for PLGG patients, are becoming the focus of attention in recent studies. For instance, Lal et al [59] reported that suppressed CDK1 and CCNB1 expression could cause G2/M cell cycle arrest, which further inhibited\u0026nbsp;malignant proliferation and invasion of glioma cells. In the present study, hub genes were found as the key members of cell cycle, which were extremely vital for genetic stability,\u0026nbsp;cell cycle checkpoint and chromosome segregation. Significantly, functional enrichment and pathway analysis revealed that given hub genes are mainly enriching in cell cycle activities, which further confirms the reliability of our results. Interestingly, we also found that the encoding protein expression of CDK1, TRIP13, KIF11, MAD2L1, and PBK was significantly increased in PHGG tissues compared to PLGG tissues. Therefore, given ten hub genes may serve as reliable biomarkers associated with PLGG progression and are promising to be potential therapeutic targets for PLGG.\u003c/p\u003e\n\u003cp\u003eAlthough this work is the first time to identify hub genes associated with PLGG progression and prognosis, some limitations still cannot be ignored. First, some gene expression profiles with incomplete clinical information or derived from cell lines were excluded, which may cause bias to a certain extent. Second, our results were conducted by integrating bioinformatics analysis, and have been verified using different databases, but insufficient tissues collected were not applicable for further verification due to the special characteristic and limited number of pediatric gliomas. Finally, it urgently needs large-scale clinical experiments to further confirm the potential of these defined hub genes as the biomarkers and therapeutic targets for PLGG progression.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, based on integrated bioinformatics analysis, ten hub genes (CDK1, BUB1, CDC20, CCNA2, KIF11, TRIP13, CDCA8, MAD2L1, PBK, CCNB2) and underlying molecular mechanism involved were identified that were significantly associated with PLGG progression. Multiple databases then confirmed that given hub genes were not only markedly increased with increasing malignancy of pediatric gliomas, but related to the prognosis for patients. We also found that the encoding protein expression of several hub genes, including CDK1, KIF11, TRIP13, MAD2L1, PBK, was statistically higher in PHGG tissues than that in PLGG tissues. Besides, functional enrichment and KEGG pathway analyses showed that hub genes were mainly enriched in cell cycle activities, which may participate in the PLGG progression via regulating the genetic stability. These findings provided novel biomarkers and potential therapeutic targets for PLGG progression, which may promote further understanding of the underlying molecular mechanisms, whereby assists in the improvement of precise diagnosis and targeted therapy for pediatric gliomas patients in future.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003ch2\u003eMicroarray data download\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eRNA-Seq expression profiles (GSE50161, GSE86574) were obtained with the following keywords: \u0026quot;Homo sapiens\u0026quot;, \u0026quot;pediatric glioma\u0026quot;, and \u0026quot;tissue\u0026quot; according to GEO database (\u003ca href=\"http://www.ncbi.nlm.nih.gov/geo/\"\u003ehttp://www.ncbi.nlm.nih.gov/geo/\u003c/a\u003e). Meanwhile, all gene expression data as well as the clinical information of pediatric gliomas were also downloaded from TCGA Genomic Data Commons Data Portal (\u003ca href=\"https://portal.gdc.cancer.gov/\"\u003ehttps://portal.gdc.cancer.gov/\u003c/a\u003e) and CGGA database (\u003ca href=\"http://www.cgga.org.cn/\"\u003ehttp://www.cgga.org.cn/\u003c/a\u003e). Notably, such gene chips derived from cell lines were excluded. Finally, a total of 58 PHGG and 60 PLGG samples were collected in our study.\u003c/p\u003e\n\u003cp\u003eFor GEO data sets, all gene expression profiles were based on the same GPL570 platform (HG‐U133_Plus_2, Affymetrix Human Genome U133 Plus 2.0 Array). And then, probe IDs were converted to gene symbols using platform annotation files. For TCGA cohorts,\u0026nbsp;the original expression data (level-3 raw count data) of\u0026nbsp;pediatric glioma\u0026nbsp;samples that fragments per Kilobase Million (FPKM) files were transformed into Transcripts Per Million (TPM),\u0026nbsp;which were then integrated s into a matrix file and converted the gene ID on ensembl website (\u003ca href=\"http://asia.ensembl.org/index.html\"\u003ehttp://asia.ensembl.org/index.html\u003c/a\u003e). If matched with multiple IDs, the gene expression values were averaged.\u003c/p\u003e\n\u003ch2\u003eDifferentially expressed analysis\u003c/h2\u003e\n\u003cp\u003eBased on R studio, the original data pretreatment of GSE50161 was conducted with the robust multiarray average algorithm (RMA)[18]. Furthermore, the classical Bayesian algorithm was utilized to screen DEGs between PHGG and PLGG groups using \u0026quot;limma\u0026quot; package [19]. False-positive discovery (adjust p-value) \u0026lt;0.05 and |log\u003csub\u003e2\u003c/sub\u003eFoldChange (FC) | \u0026gt; 1.5 were set as cut-off criteria for significant DEGs.\u003c/p\u003e\n\u003ch2\u003eFunctional enrichment and pathway analyses of DEGs\u003c/h2\u003e\n\u003cp\u003eTo deeply annotate the biological functions of DEGs and explore the potential molecular mechanism, GO enrichment and KEGG pathway analyses were achieved using R package \u0026quot;clusterProfiler\u0026quot; [20], of which the results were visualized in \u0026quot;ggplot2\u0026quot; package [21]. The GO terms were further divided into biological process (BP), molecular functions (MF) and cellular components (CC). A p-value \u0026lt;0.05 was considered statistically significant.\u003c/p\u003e\n\u003ch2\u003eConstruction of protein-protein interaction network among DEGs\u003c/h2\u003e\n\u003cp\u003eThe interactive relationships of DEGs were mapped onto the Search Tool for the Retrieval of Interacting Genes (STRING, version 10.0,\u0026nbsp;\u003ca href=\"http://string.embl.de/\"\u003ehttp://string.embl.de/\u003c/a\u003e) for further analysis [22]. The PPI network was then constructed with setting the minimum required interaction score being 0.7. The result was visualized\u0026nbsp;by Cytoscape software (version 3.7.2) [23], where the\u0026nbsp;\u0026quot;CentiScape\u0026quot;\u0026nbsp;plug‐in was utilized to calculate the degree of nodes [24]. In addition, the \u0026quot;cytohubba\u0026quot; plug-in was applied to screen hub genes, which was performed with 12 independent algorithms\u0026nbsp;including MCC, MNC, Degree and so on, where the higher score in different algorithms, the more important DEGs in disease progression [25]. R package\u0026nbsp;\u0026quot;GOplot\u0026quot;\u0026nbsp;was used to perform the functional annotation of hub genes.\u003c/p\u003e\n\u003ch2\u003eValidation of hub genes\u003c/h2\u003e\n\u003cp\u003eThe CGGA database, as an open-access and user-friendly glioma database on Chinese cohorts over 2,000 samples, contains DNA methylation, mRNA microarray and matched clinical data and so on. Thus,\u0026nbsp;in order to verify whether the hub genes were differentially expressed in the progression of pediatric glioma. The based clinical information and the mRNA expression data of pediatric glioma cases were also obtained from CGGA database, respectively [26]. Thus, the validation derived from three independent databases was considered to make the work more convincing.\u003c/p\u003e\n\u003ch2\u003eComparing the protein expression of hub genes\u003c/h2\u003e\n\u003cp\u003eA user-friendly online tool, UALCAN (\u003ca href=\"http://ualcan.path.uab.edu/index.html\"\u003ehttp://ualcan.path.uab.edu/index.html\u003c/a\u003e), was utilized to conduct the comparison of the expression of protein encoded by hub genes in pediatric brain cancer including various subgroups (race, gender, tumor histology). The available data was based on Clinical Proteomic Tumor Analysis Consortium (CPTAC) Confirmatory/Discovery dataset [27]. Importantly, we paid special attention to identifying whether the coding protein expression of key genes was significantly different between PLGG samples and PHGG samples.\u003c/p\u003e\n\u003ch2\u003eSurvival analysis of hub genes\u003c/h2\u003e\n\u003cp\u003eGEPIA,\u0026nbsp;a web server for analyzing gene expression data from the TCGA and the GTEx projects (\u003ca href=\"http://gepia.cancer-pku.cn/\"\u003ehttp://gepia.cancer-pku.cn/\u003c/a\u003e), has been widely applied to perform the differentially expressed analysis, pathological staging, and survival analysis of patients [28]. Therefore, we further explored whether the expression of hub genes was associated with the prognosis through the survival analyses, where the mean expression was set as the cut-off value to divide patients into high- and low-expressed groups.\u003c/p\u003e\n\u003ch2\u003eStatistical analysis\u003c/h2\u003e\n\u003cp\u003eStatistical analysis of the data was performed using software GraphPad Prism 8 and R studio 3.6.1. The expression data was presented as mean \u0026plusmn; SD. Statistical comparisons of key molecules expression data were subjected to the Mann-Whitney test. P-value less than 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThe authors gratefully acknowledge contributions from the GEO, TCGA and CGGA databases.\u003c/p\u003e\n\u003ch2\u003eAuthor\u0026rsquo;s contribution\u003c/h2\u003e\n\u003cp\u003eAll authors participated in this study. CZ conducted statistical analysis and data management, CZ and ZZ edited and revised the manuscript, HT reviewed the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003enone.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe raw data of this study derived from the GEO database (\u003ca href=\"http://www.ncbi.nlm.nih.gov/geo/\"\u003ehttp://www.ncbi.nlm.nih.gov/geo/\u003c/a\u003e), TCGA database (\u003ca href=\"https://portal.gdc.cancer.gov/\"\u003ehttps://portal.gdc.cancer.gov/\u003c/a\u003e) and CGGA database (\u003ca href=\"http://www.cgga.org.cn/\"\u003ehttp://www.cgga.org.cn/\u003c/a\u003e), which are publicly available databases.\u003c/p\u003e\n\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003enot necessary.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eSturm D, Pfister SM, Jones DTW: Pediatric Gliomas: Current Concepts on Diagnosis, Biology, and Clinical Management. J Clin Oncol 2017, 35(21):2370-2377.\u003c/li\u003e\n \u003cli\u003eLarouche V, Toupin AK, Lalonde B, Simonyan D, Jabado N, Perreault S: Incidence trends in pediatric central nervous system tumors in Canada: a 15 years report from Cancer and Young People in Canada (CYP-C) registry. 2020, 2(1):vdaa012.\u003c/li\u003e\n \u003cli\u003eLouis DN, Perry A, Reifenberger G, von Deimling A, Figarella-Branger D, Cavenee WK, Ohgaki H, Wiestler OD, Kleihues P, Ellison DW: The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary. Acta Neuropathol 2016, 131(6):803-820.\u003c/li\u003e\n \u003cli\u003eJones DTW, Banito A, Gr\u0026uml;\u0026sup1;newald TGP, Haber M, J?ger N, Kool M, Milde T, Molenaar JJ, Nabbi A, Pugh TJ, et al: Molecular characteristics and therapeutic vulnerabilities across paediatric solid tumours. Nat Rev Cancer 2019, 19(8):420-438.\u003c/li\u003e\n \u003cli\u003eCollins KL, Pollack IF: Pediatric Low-Grade Gliomas. Cancers (Basel) 2020, 12(5).\u003c/li\u003e\n \u003cli\u003eMistry M, Zhukova N, Merico D, Rakopoulos P, Krishnatry R, Shago M, Stavropoulos J, Alon N, Pole JD, Ray PN, et al: BRAF mutation and CDKN2A deletion define a clinically distinct subgroup of childhood secondary high-g rade glioma. J Clin Oncol 2015, 33(9):1015-1022.\u003c/li\u003e\n \u003cli\u003eBraunstein S, Raleigh D, Bindra R, Mueller S, Haas-Kogan D: Pediatric high-grade glioma: current molecular landscape and therapeutic approaches. J Neurooncol 2017, 134(3):541-549.\u003c/li\u003e\n \u003cli\u003eWisoff JH, Sanford RA, Heier LA, Sposto R, Burger PC, Yates AJ, Holmes EJ, Kun LE: Primary neurosurgery for pediatric low-grade gliomas: a prospective multi-institutional study from th e Children\u0026apos;s Oncology Group. Neurosurgery 2011, 68(6):1548-1554; discussion 1554-1545.\u003c/li\u003e\n \u003cli\u003eKorshunov A, Capper D, Reuss D, Schrimpf D, Ryzhova M, Hovestadt V, Sturm D, Meyer J, Jones C, Zheludkova O, et al: Histologically distinct neuroepithelial tumors with histone 3 G34 mutation are molecularly similar a nd comprise a single nosologic entity. Acta Neuropathol 2016, 131(1):137-146.\u003c/li\u003e\n \u003cli\u003eSturm D, Witt H, Hovestadt V, Khuong-Quang DA, Jones DT, Konermann C, Pfaff E, T?njes M, Sill M, Bender S, et al: Hotspot mutations in H3F3A and IDH1 define distinct epigenetic and biological subgroups of glioblasto ma. Cancer Cell 2012, 22(4):425-437.\u003c/li\u003e\n \u003cli\u003eMueller S, Phillips J, Onar-Thomas A, Romero E, Zheng S, Wiencke JK, McBride SM, Cowdrey C, Prados MD, Weiss WA, et al: PTEN promoter methylation and activation of the PI3K/Akt/mTOR pathway in pediatric gliomas and influe nce on clinical outcome. Neuro Oncol 2012, 14(9):1146-1152.\u003c/li\u003e\n \u003cli\u003eRyall S, Zapotocky M, Fukuoka K, Nobre L, Guerreiro Stucklin A, Bennett J, Siddaway R, Li C, Pajovic S, Arnoldo A, et al: Integrated Molecular and Clinical Analysis of 1,000 Pediatric Low-Grade Gliomas. Cancer Cell 2020, 37(4):569-583.e565.\u003c/li\u003e\n \u003cli\u003eLi D, Hao X, Song Y: Identification of the Key MicroRNAs and the miRNA-mRNA Regulatory Pathways in Prostate Cancer by Bioi nformatics Methods. Biomed Res Int 2018, 2018:6204128.\u003c/li\u003e\n \u003cli\u003eJiang J, Bi Y, Liu XP, Yu D, Yan X, Yao J, Liu T, Li S: To construct a ceRNA regulatory network as prognostic biomarkers for bladder cancer. J Cell Mol Med 2020, 24(9):5375-5386.\u003c/li\u003e\n \u003cli\u003eDalerba P, Sahoo D, Paik S, Guo X, Yothers G, Song N, Wilcox-Fogel N, Forg\u0026uml;\u0026reg; E, Rajendran PS, Miranda SP, et al: CDX2 as a Prognostic Biomarker in Stage II and Stage III Colon Cancer. N Engl J Med 2016, 374(3):211-222.\u003c/li\u003e\n \u003cli\u003eZhai Y, Chen Y, Li Q, Zhang L: Exploration of the hub genes and miRNAs in lung adenocarcinoma. Oncol Lett 2019, 18(2):1713-1722.\u003c/li\u003e\n \u003cli\u003eCheng W, Ren X, Zhang C, Cai J, Liu Y, Han S, Wu A: Bioinformatic profiling identifies an immune-related risk signature for glioblastoma. Neurology 2016, 86(24):2226-2234.\u003c/li\u003e\n \u003cli\u003eHarbron C, Chang KM, South MC: RefPlus: an R package extending the RMA Algorithm. Bioinformatics 2007, 23(18):2493-2494.\u003c/li\u003e\n \u003cli\u003eRitchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK: limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 2015, 43(7):e47.\u003c/li\u003e\n \u003cli\u003eYu G, Wang LG, Han Y, He QY: clusterProfiler: an R package for comparing biological themes among gene clusters. Omics 2012, 16(5):284-287.\u003c/li\u003e\n \u003cli\u003eIto K, Murphy D: Application of ggplot2 to Pharmacometric Graphics. CPT Pharmacometrics Syst Pharmacol 2013, 2:e79.\u003c/li\u003e\n \u003cli\u003eSzklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, Santos A, Doncheva NT, Roth A, Bork P, et al: The STRING database in 2017: quality-controlled protein-protein association networks, made broadly ac cessible. Nucleic Acids Res 2017, 45(D1):D362-d368.\u003c/li\u003e\n \u003cli\u003eKillcoyne S, Carter GW, Smith J, Boyle J: Cytoscape: a community-based framework for network modeling. Methods Mol Biol 2009, 563:219-239.\u003c/li\u003e\n \u003cli\u003eJiang H, Ma R, Zou S, Wang Y, Li Z, Li W: Reconstruction and analysis of the lncRNA-miRNA-mRNA network based on competitive endogenous RNA reve al functional lncRNAs in rheumatoid arthritis. Mol Biosyst 2017, 13(6):1182-1192.\u003c/li\u003e\n \u003cli\u003eChin CH, Chen SH, Wu HH, Ho CW, Ko MT, Lin CY: cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol 2014, 8 Suppl 4:S11.\u003c/li\u003e\n \u003cli\u003eFang S, Liang J, Qian T, Wang Y, Liu X, Fan X, Li S, Wang Y, Jiang T: Anatomic Location of Tumor Predicts the Accuracy of Motor Function Localization in Diffuse Lower-Grad e Gliomas Involving the Hand Knob Area. AJNR Am J Neuroradiol 2017, 38(10):1990-1997.\u003c/li\u003e\n \u003cli\u003eChen F, Chandrashekar DS, Varambally S, Creighton CJ: Pan-cancer molecular subtypes revealed by mass-spectrometry-based proteomic characterization of more than 500 human cancers. Nat Commun 2019, 10(1):5679.\u003c/li\u003e\n \u003cli\u003eTang Z, Li C, Kang B, Gao G, Li C, Zhang Z: GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res 2017, 45(W1):W98-w102.\u003c/li\u003e\n \u003cli\u003eBroniscer A, Gajjar A: Supratentorial high-grade astrocytoma and diffuse brainstem glioma: two challenges for the pediatric oncologist. Oncologist 2004, 9(2):197-206.\u003c/li\u003e\n \u003cli\u003eCastedo M, Perfettini JL, Roumier T, Kroemer G: Cyclin-dependent kinase-1: linking apoptosis to cell cycle and mitotic catastrophe. Cell Death Differ 2002, 9(12):1287-1293.\u003c/li\u003e\n \u003cli\u003eLiu WT, Chen C, Lu IC, Kuo SC, Lee KH, Chen TL, Song TS, Lu YL, Gean PW, Hour MJ: MJ-66 induces malignant glioma cells G2/M phase arrest and mitotic catastrophe through regulation of cyclin B1/Cdk1 complex. Neuropharmacology 2014, 86:219-227.\u003c/li\u003e\n \u003cli\u003eXie C, Xu M, Lu D, Zhang W, Wang L, Wang H, Li J, Ren F, Wang C: Candidate genes and microRNAs for glioma pathogenesis and prognosis based on gene expression profiles. Mol Med Rep 2018, 18(3):2715-2723.\u003c/li\u003e\n \u003cli\u003eGeng RX, Li N, Xu Y, Liu JH, Yuan FE, Sun Q, Liu BH, Chen QX: Identification of Core Biomarkers Associated with Outcome in Glioma: Evidence from Bioinformatics Ana lysis. Dis Markers 2018, 2018:3215958.\u003c/li\u003e\n \u003cli\u003ePeng C, Qi XM, Miao LL, Ren J: 1,2:5,6-dianhydrogalactitol inhibits human glioma cell growth in vivo and in vitro by arresting the c ell cycle at G2/M phase. Acta Pharmacol Sin 2017, 38(4):561-570.\u003c/li\u003e\n \u003cli\u003eVleugel M, Hoek TA, Tromer E, Sliedrecht T, Groenewold V, Omerzu M, Kops GJ: Dissecting the roles of human BUB1 in the spindle assembly checkpoint. J Cell Sci 2015, 128(16):2975-2982.\u003c/li\u003e\n \u003cli\u003eZhang Y, Li J, Yi K, Feng J, Cong Z, Wang Z, Wei Y, Wu F, Cheng W, Samo AA, et al: Elevated signature of a gene module coexpressed with CDC20 marks genomic instability in glioma. Proc Natl Acad Sci U S A 2019, 116(14):6975-6984.\u003c/li\u003e\n \u003cli\u003eMorales AG, Pezuk JA, Brassesco MS, de Oliveira JC, de Paula Queiroz RG, Machado HR, Carlotti CG, Neder L, de Oliveira HF, Scrideli CA, Tone LG: BUB1 and BUBR1 inhibition decreases proliferation and colony formation, and enhances radiation sensit ivity in pediatric glioblastoma cells. Childs Nerv Syst 2013, 29(12):2241-2248.\u003c/li\u003e\n \u003cli\u003eYang L, Zeng W, Sun H, Huang F, Yang C, Cai X, Lu Y, Zeng J, Yang K: Bioinformatical Analysis of Gene Expression Omnibus Database Associates TAF7/CCNB1, TAF7/CCNA2, and G TF2E2/CDC20 Pathways with Glioblastoma Development and Prognosis. World Neurosurg 2020, 138:e492-e514.\u003c/li\u003e\n \u003cli\u003eBie L, Zhao G, Cheng P, Rondeau G, Porwollik S, Ju Y, Xia XQ, McClelland M: The accuracy of survival time prediction for patients with glioma is improved by measuring mitotic sp indle checkpoint gene expression. PLoS One 2011, 6(10):e25631.\u003c/li\u003e\n \u003cli\u003eKim DH, Park SE, Kim M, Ji YI, Kang MY, Jung EH, Ko E, Kim Y, Kim S, Shim YM, Park J: A functional single nucleotide polymorphism at the promoter region of cyclin A2 is associated with in creased risk of colon, liver, and lung cancers. Cancer 2011, 117(17):4080-4091.\u003c/li\u003e\n \u003cli\u003eDachineni R, Ai G, Kumar DR, Sadhu SS, Tummala H, Bhat GJ: Cyclin A2 and CDK2 as Novel Targets of Aspirin and Salicylic Acid: A Potential Role in Cancer Prevent ion. Mol Cancer Res 2016, 14(3):241-252.\u003c/li\u003e\n \u003cli\u003eWang Q, He Z, Chen Y: Comprehensive Analysis Reveals a 4-Gene Signature in Predicting Response to Temozolomide in Low-Grade Glioma Patients. Cancer Control 2019, 26(1):1073274819855118.\u003c/li\u003e\n \u003cli\u003eWeber RG, Rieger J, Naumann U, Lichter P, Weller M: Chromosomal imbalances associated with response to chemotherapy and cytotoxic cytokines in human mali gnant glioma cell lines. Int J Cancer 2001, 91(2):213-218.\u003c/li\u003e\n \u003cli\u003eAsbaghi Y, Thompson LL, Lichtensztejn Z, McManus KJ: KIF11 silencing and inhibition induces chromosome instability that may contribute to cancer. Genes Chromosomes Cancer 2017, 56(9):668-680.\u003c/li\u003e\n \u003cli\u003eValensin S, Ghiron C, Lamanna C, Kremer A, Rossi M, Ferruzzi P, Nievo M, Bakker A: KIF11 inhibition for glioblastoma treatment: reason to hope or a struggle with the brain? BMC Cancer 2009, 9:196.\u003c/li\u003e\n \u003cli\u003eVenere M, Horbinski C, Crish JF, Jin X, Vasanji A, Major J, Burrows AC, Chang C, Prokop J, Wu Q, et al: The mitotic kinesin KIF11 is a driver of invasion, proliferation, and self-renewal in glioblastoma. Sci Transl Med 2015, 7(304):304ra143.\u003c/li\u003e\n \u003cli\u003eVenuto S, Monteonofrio L, Cozzolino F, Monti M, Appolloni I, Mazza T, Canetti D, Giambra V, Panelli P, Fusco C, et al: TRIM8 interacts with KIF11 and KIFC1 and controls bipolar spindle formation and chromosomal stability. Cancer Lett 2020, 473:98-106.\u003c/li\u003e\n \u003cli\u003eZhang Q, Dong Y, Hao S, Tong Y, Luo Q, Aerxiding P: The oncogenic role of TRIP13 in regulating proliferation, invasion, and cell cycle checkpoint in NSCL C cells. Int J Clin Exp Pathol 2019, 12(9):3357-3366.\u003c/li\u003e\n \u003cli\u003eDong L, Ding H, Li Y, Xue D, Li Z, Liu Y, Zhang T, Zhou J, Wang P: TRIP13 is a predictor for poor prognosis and regulates cell proliferation, migration and invasion in prostate cancer. Int J Biol Macromol 2019, 121:200-206.\u003c/li\u003e\n \u003cli\u003eZhu MX, Wei CY, Zhang PF, Gao DM, Chen J, Zhao Y, Dong SS, Liu BB: Elevated TRIP13 drives the AKT/mTOR pathway to induce the progression of hepatocellular carcinoma via interacting with ACTN4. J Exp Clin Cancer Res 2019, 38(1):409.\u003c/li\u003e\n \u003cli\u003eZhang G, Zhu Q, Fu G, Hou J, Hu X, Cao J, Peng W, Wang X, Chen F, Cui H: TRIP13 promotes the cell proliferation, migration and invasion of glioblastoma through the FBXW7/c-MY C axis. Br J Cancer 2019, 121(12):1069-1078.\u003c/li\u003e\n \u003cli\u003eZhang C, Zhao L, Leng L, Zhou Q, Zhang S, Gong F, Xie P, Lin G: CDCA8 regulates meiotic spindle assembly and chromosome segregation during human oocyte meiosis. Gene 2020, 741:144495.\u003c/li\u003e\n \u003cli\u003eCi C, Tang B, Lyu D, Liu W, Qiang D, Ji X, Qiu X, Chen L, Ding W: Overexpression of CDCA8 promotes the malignant progression of cutaneous melanoma and leads to poor pr ognosis. Int J Mol Med 2019, 43(1):404-412.\u003c/li\u003e\n \u003cli\u003eZhou Y, Yang L, Zhang X, Chen R, Chen X, Tang W, Zhang M: Identification of Potential Biomarkers in Glioblastoma through Bioinformatic Analysis and Evaluating Their Prognostic Value. Biomed Res Int 2019, 2019:6581576.\u003c/li\u003e\n \u003cli\u003eWu D, Wang L, Yang Y, Huang J, Hu Y, Shu Y, Zhang J, Zheng J: MAD2-p31comet axis deficiency reduces cell proliferation, migration and sensitivity of microtubule-in terfering agents in glioma. Biochem Biophys Res Commun 2018, 498(1):157-163.\u003c/li\u003e\n \u003cli\u003eStangeland B, Mughal AA, Grieg Z, Sandberg CJ, Joel M, Nyg?rd S, Meling T, Murrell W, Vik Mo EO, Langmoen IA: Combined expressional analysis, bioinformatics and targeted proteomics identify new potential therape utic targets in glioblastoma stem cells. Oncotarget 2015, 6(28):26192-26215.\u003c/li\u003e\n \u003cli\u003eKruthika BS, Jain R, Arivazhagan A, Bharath RD, Yasha TC, Kondaiah P, Santosh V: Transcriptome profiling reveals PDZ binding kinase as a novel biomarker in peritumoral brain zone of glioblastoma. J Neurooncol 2019, 141(2):315-325.\u003c/li\u003e\n \u003cli\u003eJoel M, Mughal AA, Grieg Z, Murrell W, Palmero S, Mikkelsen B, Fjerdingstad HB, Sandberg CJ, Behnan J, Glover JC, et al: Targeting PBK/TOPK decreases growth and survival of glioma initiating cells in vitro and attenuates t umor growth in vivo. Mol Cancer 2015, 14:121.\u003c/li\u003e\n \u003cli\u003eLal N, Nemaysh V, Luthra PM: Proteasome mediated degradation of CDC25C and Cyclin B1 in Demethoxycurcumin treated human glioma U87 MG cells to trigger G2/M cell cycle arrest. Toxicol Appl Pharmacol 2018, 356:76-89.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"bioinformatics analysis, hub genes, molecular mechanism, pediatric low grade gliomas, progression","lastPublishedDoi":"10.21203/rs.3.rs-1256036/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1256036/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e To explore hub genes and underlying molecular mechanism about the malignant progression of pediatric low grade gliomas (PLGG). \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e The microarray were obtained from Gene Expression Omnibus database and hub genes were identified by bioinformatics analysis. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eTen hub genes were confirmed to increase with the development of PLGG, and their high expression was correlated with poor prognosis. Additionally, the encoding protein expression level of CDK1, KIF11, TRIP13, MAD2L1, PBK were significantly higher in high grade glioma tissues than that in PLGG. Finally, functional enrichment analysis demonstrated these genes were mainly enriched in cell cycle activities. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Our results offer novel insights of the molecular mechanisms and identify potential biomarkers or therapeutic targets for PLGG progression.\u003c/p\u003e","manuscriptTitle":"Identification of Hub Genes and Molecular Mechanism Associated With the Progression of Pediatric Low Grade Gliomas","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-01-17 15:40:35","doi":"10.21203/rs.3.rs-1256036/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"885e4afa-7bdb-47ad-ac43-2aaff9e4282d","owner":[],"postedDate":"January 17th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-01-17T15:40:37+00:00","versionOfRecord":[],"versionCreatedAt":"2022-01-17 15:40:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1256036","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1256036","identity":"rs-1256036","version":["v1"]},"buildId":"ehx78VzkSd0WSzXnipQa-","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}