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Research article
Bioinformatics analysis of KIF20A, a potential therapeutic target for glioblastoma
Jiayang Wang
Jiangxi Agricultural University
Corresponding Author
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This work is licensed under a CC BY 4.0 License. Read Full License
Background: Glioblastoma (GBM) is a malignant brain tumor with high mobility. The median survival time of GBM patients is 15 months. Currently, there is no effective treatment for improving the prognosis of the GBM due to a lack of prognostic markers.
Materials and methods: To predict core therapeutic targets for GBM, we analyzed four microarray datasets (GSE49810, GSE50161, GSE65624, and GSE90604) selected from the Gene Expression Omnibus (GEO) database and the other datasets obtained from The Cancer Genome Atlas (TCGA) database. Expression protein array of 227 GBM samples and 18 normal samples were clustered to summarize GBM tissue classification. Differentially expressed genes (DEGs) were analyzed by comparing GBM and normal brain tissues in each profile using the limma package of R software. GO function and KEGG pathway enrichment analysis was performed using the DAVID database. Overlapping DEGs were ranked based on protein expression ratios from the comparison between cancer and normal samples using robustRankaggreg package of R software and scored from high to low. Protein-protein interaction (PPI) network was visualized using CytoHubba and Cluego plugins in Cytoscape software. Core hub genes were analyzed by MCC, MNC, DMNC, and EPC methods. Besides, the GEPIA tool was used to create the survival curves and boxplots to evaluate the prognostic effect of hub genes for improving the diagnostic outcomes and treatment of GBM.
Results: A total of 2064 DEGs were analyzed (1400 downregulated DEGs and 1664 upregulated DEGs) in the GEO database. 3292 DEGs were found (1485 upregulated DEGs and 1807 downregulated DEGs) in TCGA. We selected 221 significant DEGs from four microarrays. Combining the GEO results with the results of TCGA, we found only 181 common DEGs by using Venn analysis. Further, expression levels of KIF20A selected from 10 hub genes closely associated with the survival rate.
Conclusion: Up-regulation of KIF20A has a pivotal role in controlling the prognosis of GBM in 2 years follow-up period; KIF20A should be considered as a potential therapeutic target for GBM.
Keywords
glioblastoma, KIF20A, expression protein profiling, children brain tumor, microtubule cytoskeleton organization
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This preprint is under consideration at BMC Cancer. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
Bioinformatics analysis of KIF20A, a potential therapeutic target for glioblastoma
Jiayang Wang
Jiangxi Agricultural University
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 05 Jan, 2021
No community comments so far
Submission checks complete
On 30 Dec, 2020
Editor invited
On 15 Dec, 2020
First submitted
On 15 Oct, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Glioblastoma (GBM) is a malignant brain tumor with high mobility. The median survival time of GBM patients is 15 months. Currently, there is no effective treatment for improving the prognosis of the GBM due to a lack of prognostic markers.
Materials and methods: To predict core therapeutic targets for GBM, we analyzed four microarray datasets (GSE49810, GSE50161, GSE65624, and GSE90604) selected from the Gene Expression Omnibus (GEO) database and the other datasets obtained from The Cancer Genome Atlas (TCGA) database. Expression protein array of 227 GBM samples and 18 normal samples were clustered to summarize GBM tissue classification. Differentially expressed genes (DEGs) were analyzed by comparing GBM and normal brain tissues in each profile using the limma package of R software. GO function and KEGG pathway enrichment analysis was performed using the DAVID database. Overlapping DEGs were ranked based on protein expression ratios from the comparison between cancer and normal samples using robustRankaggreg package of R software and scored from high to low. Protein-protein interaction (PPI) network was visualized using CytoHubba and Cluego plugins in Cytoscape software. Core hub genes were analyzed by MCC, MNC, DMNC, and EPC methods. Besides, the GEPIA tool was used to create the survival curves and boxplots to evaluate the prognostic effect of hub genes for improving the diagnostic outcomes and treatment of GBM.
Results: A total of 2064 DEGs were analyzed (1400 downregulated DEGs and 1664 upregulated DEGs) in the GEO database. 3292 DEGs were found (1485 upregulated DEGs and 1807 downregulated DEGs) in TCGA. We selected 221 significant DEGs from four microarrays. Combining the GEO results with the results of TCGA, we found only 181 common DEGs by using Venn analysis. Further, expression levels of KIF20A selected from 10 hub genes closely associated with the survival rate.
Conclusion: Up-regulation of KIF20A has a pivotal role in controlling the prognosis of GBM in 2 years follow-up period; KIF20A should be considered as a potential therapeutic target for GBM.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.