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      Identification of PBK as a hub gene and potential therapeutic target for medulloblastoma

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          Abstract

          Medulloblastoma (MB) is the most frequent malignant brain tumor in pediatrics. Since the current standard of care for MB consisting of surgery, cranio-spinal irradiation and chemotherapy often leads to a high morbidity rate, a number of patients suffer from long-term sequelae following treatment. Targeted therapies hold the promise of being more effective and less toxic. Therefore, the present study aimed to identify hub genes with an upregulated expression in MB and to search for potential therapeutic targets from these genes. For this purpose, gene expression profile datasets were obtained from the Gene Expression Omnibus database and processed using R 3.6.0 software to screen differentially expressed genes (DEGs) between MB samples and normal brain tissues. A total of 282 upregulated and 436 downregulated DEGs were identified. Functional enrichment analysis revealed that the upregulated DEGs were predominantly enriched in the cell cycle, DNA replication and cell division. The top 10 hub genes were identified from the protein-protein interaction network of upregulated genes, and one identified hub gene [PDZ binding kinase ( PBK)] was selected for further investigation due to its possible role in the pathogenesis of MB. The aberrant expression of PBK in MB was verified in additional independent gene expression datasets. Survival analysis demonstrated that a higher expression level of PBK was significantly associated with poorer clinical outcomes in non-Wingless MBs. Furthermore, targeting PBK with its inhibitor, HI-TOPK-032, impaired the proliferation and induced the apoptosis of two MB cell lines, with the diminished phosphorylation of downstream effectors of PBK, including ERK1/2 and Akt, and the activation of caspase-3. Hence, these results suggest that PBK may be a potential prognostic biomarker and a novel candidate of targeted therapy for MB.

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          Most cited references50

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          Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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            limma powers differential expression analyses for RNA-sequencing and microarray studies

            limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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              edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

              Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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                Author and article information

                Journal
                Oncol Rep
                Oncol Rep
                Oncology Reports
                D.A. Spandidos
                1021-335X
                1791-2431
                July 2022
                19 May 2022
                19 May 2022
                : 48
                : 1
                : 125
                Affiliations
                [1 ]Neurosurgery Center, Department of Pediatric Neurosurgery, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong 510280, P.R. China
                [2 ]Neurosurgery Center, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong 510280, P.R. China
                Author notes
                Correspondence to: Professor Wangming Zhang or Dr Min Li, Neurosurgery Center, Department of Pediatric Neurosurgery, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu, Guangzhou, Guangdong 510280, P.R. China, E-mail: wzhang@ 123456vip.126.com ; zhangwm@ 123456smu.edu.cn , E-mail: minli@ 123456smu.edu.cn
                [*]

                Contributed equally

                Article
                OR-48-01-08336
                10.3892/or.2022.8336
                9164263
                35593307
                68ea5ec7-4283-44f2-95ef-f14e4fff6002
                Copyright: © Deng et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

                History
                : 09 February 2022
                : 26 April 2022
                Funding
                Funded by: 70th China Postdoctoral Science Foundation
                Award ID: 2021M701618
                The present study was supported by the 70th China Postdoctoral Science Foundation (grant no. 2021M701618).
                Categories
                Articles

                medulloblastoma,pdz binding kinase,proliferation,apoptosis,therapeutic target

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