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pmid:33718116

Identification of Core Genes and Screening of Potential Targets in Glioblastoma Multiforme by Integrated Bioinformatic Analysis.

Ji'an Yang · Qian Yang

Frontiers in oncology · 2020 doi:10.3389/fonc.2020.615976

Paper

complete

Experiment

complete

Data

partial

Analysis

partial

Execution

absent

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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2026-11-01T15:41:28Z
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title
Identification of Core Genes and Screening of Potential Targets in Glioblastoma Multiforme by Integrated Bioinformatic Analysis.
doi
10.3389/fonc.2020.615976
pmid
33718116
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[0]
Ji'an Yang
[1]
Qian Yang
journal
Frontiers in oncology
year
2020
abstract
Glioblastoma multiforme is the most common primary intracranial malignancy, but its etiology and pathogenesis are still unclear. With the deepening of human genome research, the research of glioma subtype screening based on core molecules has become more in-depth. In the present study, we screened out differentially expressed genes (DEGs) through reanalyzing the glioblastoma multiforme (GBM) datasets GSE90598 from the Gene Expression Omnibus (GEO), the GBM dataset TCGA-GBM and the low-grade glioma (LGG) dataset TCGA-LGG from the Cancer Genome Atlas (TCGA). A total of 150 intersecting DEGs were found, of which 48 were upregulated and 102 were downregulated. These DEGs from GSE90598 dataset were enriched using the overrepresentation method, and multiple enriched gene ontology (GO) function terms were significantly correlated with neural cell signal transduction. DEGs between GBM and LGG were analyzed by gene set enrichment analysis (GSEA), and the significantly enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways involved in synapse signaling and oxytocin signaling pathways. Then, a protein-protein interaction (PPI) network was constructed to assess the interaction of proteins encoded by the DEGs. MCODE identified 2 modules from the PPI network. The 11 genes with the highest degrees in module 1 were designated as core molecules, namely, GABRD, KCNC1, KCNA1, SYT1, CACNG3, OPALIN, CD163, HPCAL4, ANK3, KIF5A, and MS4A6A, which were mainly enriched in ionic signaling-related pathways. Survival analysis of the GSE83300 dataset verified the significant relationship between expression levels of the 11 core genes and survival. Finally, the core molecules of GBM and the DrugBank database were assessed by a hypergeometric test to identify 10 drugs included tetrachlorodecaoxide related to cancer and neuropsychiatric diseases. Further studies are required to explore these core genes for their potentiality in diagnosis, prognosis, and targeted therapy and explain the relationship among ionic signaling-related pathways, neuropsychiatric diseases and neurological tumors.
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Using the “survival” (https://CRAN.R-project.org/package=survival) package in R, the patients were divided into high and low expression groups by the quartiles of the expression of each core gene. Survival analysis was performed to assess the association of gene expression levels with survival time.
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sections[7] "Survival Analysis of Putative Hub Genes" paragraph 2
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Using the “survival” (https://CRAN.R-project.org/package=survival) package in R, the patients were divided into high and low expression groups by the quartiles of the expression of each core gene. Survival analysis was performed to assess the association of gene expression levels with survival time.
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The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding author.
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Gene expression profiling of 50 glioblastoma multiforme tissue samples
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Gene Expression data from Glioblastoma Multiforme patient tumor samples, healthy brain tissue (partly from GBM patients) and NHA cell line and Human fetal astrocyte cell line
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Data Preprocessing and Differential Gene Identification
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Data Preprocessing and Differential Gene Identification
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Gene Ontology and Kyoto Encyclopedia of Genes and Genomes Pathway Analyses of Differentially Expressed Genes
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GO (Biological Process) function enrichment results (sorted according to p value, the table only shows the top 10 most significant rows) of DEGs from GSE90598 dataset.
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Gene Ontology and Kyoto Encyclopedia of Genes and Genomes Pathway Analyses of Differentially Expressed Genes
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KEGG pathway enrichment results of DEGs from GSE90598 dataset.
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Gene Ontology and Kyoto Encyclopedia of Genes and Genomes Pathway Analyses of Differentially Expressed Genes
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GO (Biological Process) function enrichment results (sorted according to p value, the table shows only the top 10 most significant rows) of the core gene set in module 1.
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Module Screening From the Protein–Protein Interaction Network
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Table 6
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GO (Biological Process) function enrichment results (sorted according to p value, the table shows only the top 10 most significant rows) of the core gene set in module 1.
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[1]
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[3]
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[4]
6
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[3]
4.76E-05
[4]
5
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KEGG pathway enrichment results (sorted according to p value, the table shows only the top 10 most significant rows) of the core gene set in module 1.
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PMC7943725
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12
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Module Screening From the Protein–Protein Interaction Network
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Table 7
caption
KEGG pathway enrichment results (sorted according to p value, the table shows only the top 10 most significant rows) of the core gene set in module 1.
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1.01E-05
[3]
1.07E-03
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1.05E-04
[3]
4.01E-03
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Oxytocin signaling pathway
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1.23E-04
[3]
4.01E-03
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hsa04911
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1.66E-04
[3]
4.01E-03
[4]
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GABAergic synapse
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1.89E-04
[3]
4.01E-03
[4]
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[6] array · 5 items
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hsa04713
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2.64E-04
[3]
4.42E-03
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hsa05033
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2.92E-04
[3]
4.42E-03
[4]
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8.26E-04
[3]
1.09E-02
[4]
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hsa04261
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Adrenergic signaling in cardiomyocytes
[2]
1.34E-03
[3]
1.57E-02
[4]
4
[10] array · 5 items
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Tuberculosis
[2]
2.62E-03
[3]
2.78E-02
[4]
4
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The results of hypergeometric test for the relationship between core genes and drugs.
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PMC7943725
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14
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Core Gene Related Drug Targets Screening
row_count
11
column_count
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table object · 8 fields
label
Table 8
caption
The results of hypergeometric test for the relationship between core genes and drugs.
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[0]
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[1]
drug_key
[2]
drug_gene_num
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core_gene_num
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DB05389
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6
[3]
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3.47E-03
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DB01594
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16
[3]
1
[4]
9.23E-03
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[3]
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DB01567
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DB01559
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DB00546
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9.23E-03
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Using the “survival” (https://CRAN.R-project.org/package=survival) package in R, the patients were divided into high and low expression groups by the quartiles of the expression of each core gene. Survival analysis was performed to assess the association of gene expression levels with survival time.
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The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here.
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GSE83300
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GSE90598
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The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding author.
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The paper names GSE90598, GSE83300 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read.
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In the present study, we screened out differentially expressed genes (DEGs) through reanalyzing the glioblastoma multiforme (GBM) datasets GSE90598 from the Gene Expression Omnibus (GEO), the GBM dataset TCGA-GBM and the low-grade glioma (LGG) dataset TCGA-LGG from the Cancer Genome Atlas (TCGA). A total of 150 intersecting DEGs were found, of which 48 were upregulated and 102 were downregulated. These DEGs from GSE90598 dataset were enriched using the overrepresentation method, and multiple enriched gene ontology (GO) function terms were significantly correlated with neural cell signal transduction. DEGs between GBM and LGG were analyzed by gene set enrichment analysis (GSEA), and the significantly enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways involved in synapse signaling and oxytocin signaling pathways. Then, a protein-protein interaction (PPI) network was constructed to assess the interaction of proteins encoded by the DEGs. MCODE identified 2 modules from the PPI network. The 11 genes with the highest degrees in module 1 were designated as core molecules, namely, GABRD, KCNC1, KCNA1, SYT1, CACNG3, OPALIN, CD163, HPCAL4, ANK3, KIF5A, and MS4A6A, which were mainly enriched in ionic signaling-related pathways. Survival analysis of the GSE83300 dataset verified the significant relationship between expression levels of the 11 core genes and survival. Finally, the core molecules of GBM and the DrugBank database were assessed by a hypergeometric test to identify 10 drugs included tetrachlorodecaoxide related to cancer and neuropsychiatric diseases. 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