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Current Bioinformatics

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ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

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Research Article

Identification of Glioma Specific Genes as Diagnostic and Prognostic Markers for Glioma

Author(s): Ming Tu, Ling Ye, ShaoBo Hu, Wei Wang, Penglei Zhu, XiangHe Lu and WeiMing Zheng*

Volume 16, Issue 1, 2021

Published on: 24 April, 2020

Page: [120 - 129] Pages: 10

DOI: 10.2174/1574893615999200424090954

Price: $65

Abstract

Background: Malignant gliomas are the most prevalent malignancy of the brain. However, there was still lack of sensitive and accurate biomarkers for gliomas.

Objective: To explore the mechanisms underlying glioma progression and identify novel diagnostic and prognostic markers for glioma.

Methods: By analyzing TCGA dataset, whole-genome genes expression levels were evaluated in 19 different types of human cancers. A protein-protein interacting network was constructed to reveal the potential roles of these glioma special genes. KEGG and GO analysis revealed the potential effect of these genes.

Results: We identified 698 gliomas specially expressed genes by analyzing TCGA dataset. A protein-protein interacting network was constructed to reveal the potential roles of these glioma special genes. KEGG and GO analysis showed gliomas specially expressed genes were involved in regulating neuroactive ligand-receptor interaction, retrograde endocannabinoid signaling, Glutamatergic synapse, chemical synaptic transmission, nervous system development, central nervous system development, and learning. Of note, GRIA1, GNAO1, GRIN1, CACNA1A, CAMK2A, and SYP were identified to be down-regulated and associated with poor prognosis in gliomas.

Conclusion: GRIA1, GNAO1, GRIN1, CACNA1A, CAMK2A, and SYP were identified to be down-regulated and associated with poor prognosis in gliomas. We thought this study will provide novel biomarkers for gliomas.

Keywords: Glioma, glioma specific gene, biomarker, prediction, protein-protein interaction, prognosis.

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