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pmid:28789435

Identification of potential key genes associated with glioblastoma based on the gene expression profile.

Lijuan Bo · Bo Wei · Chaohui Li · Zhanfeng Wang · Zheng Gao · Zhuang Miao

Oncology letters · 2017 doi:10.3892/ol.2017.6460

Paper

complete

Experiment

complete

Data

partial

Analysis

partial

Execution

absent

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title
Identification of potential key genes associated with glioblastoma based on the gene expression profile.
doi
10.3892/ol.2017.6460
pmid
28789435
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[0]
Lijuan Bo
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Bo Wei
[2]
Chaohui Li
[3]
Zhanfeng Wang
[4]
Zheng Gao
[5]
Zhuang Miao
journal
Oncology letters
year
2017
abstract
Gliomas are serious primary brain tumors. The aim of the present study was to identify potential key genes associated with the progression of gliomas. The GSE31262 gene expression profile data, which included 9 glioblastoma stem cells (GSCs) samples and 5 neural stem cell samples from adult humans, were downloaded from Gene Expression Omnibus (GEO) database. limma package was used to identify differentially expressed genes (DEGs). Based on STRING database and Pearson Correlation Coefficient (PCC), a co-expression network was constructed to comprehensively understand the interactions between DEGs, and function analysis of genes in the network was conducted. Furthermore, the DEGs that were associated with prognosis were analyzed. A total of 431 DEGs were identified, including 98 upregulated DEGs and 333 downregulated DEGs. Genes including PDZ binding kinase, topoisomerase (DNA) II α (TOP2A), cyclin dependent kinase (CDK) 1, cell division cycle 6 and NIMA related kinase 2 had a relatively high degree in the co-expression network. A set of genes including cyclin D1, CDK1 and CDK2 were significantly enriched in the cell cycle and p53 signaling pathway. Additionally, 69 DEGs were identified as genes involved in glioblastoma prognosis, such as CDK2 and TOP2A. The genes that had a higher degree and were associated with cell cycle and p53 signaling pathway may play pivotal roles in the progress of glioblastoma.
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Cox proportional hazard model (30,31) was utilized to predict the association of DEGs and patient prognosis.
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Cox proportional hazard model (30,31) was utilized to predict the association of DEGs and patient prognosis.
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The mRNA expression profile dataset GSE31262 was downloaded from the GEO database (http://www.ncbi.nlm.nih.gov/geo/), which derived from 9 individual samples of GSCs and 5 individual samples of ahNSCs taken from the adult human brain (16).
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sections[0] "Abstract" paragraph 1
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The GSE31262 gene expression profile data, which included 9 glioblastoma stem cells (GSCs) samples and 5 neural stem cell samples from adult humans, were downloaded from Gene Expression Omnibus (GEO) database. limma package was used to identify differentially expressed genes (DEGs).
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Based on the criteria of FDR <0.05 and |log2 FC| ≥1, a total of 431 DEGs were identified in the GSC samples, including 98 upregulated DEGs and 333 downregulated DEGs.
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