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pmid:30867991

Identification of differentially expressed key genes between glioblastoma and low-grade glioma by bioinformatics analysis.

Yang Xu · Rongxin Geng · Fan'en Yuan · Qian Sun · Baohui Liu · Qianxue Chen

PeerJ · 2019 doi:10.7717/peerj.6560

Paper

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title
Identification of differentially expressed key genes between glioblastoma and low-grade glioma by bioinformatics analysis.
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10.7717/peerj.6560
pmid
30867991
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[0]
Yang Xu
[1]
Rongxin Geng
[2]
Fan'en Yuan
[3]
Qian Sun
[4]
Baohui Liu
[5]
Qianxue Chen
journal
PeerJ
year
2019
abstract
Gliomas are a very diverse group of brain tumors that are most commonly primary tumor and difficult to cure in central nervous system. It's necessary to distinguish low-grade tumors from high-grade tumors by understanding the molecular basis of different grades of glioma, which is an important step in defining new biomarkers and therapeutic strategies. We have chosen the gene expression profile GSE52009 from gene expression omnibus (GEO) database to detect important differential genes. GSE52009 contains 120 samples, including 60 WHO II samples and 24 WHO IV samples that were selected in our analysis. We used the GEO2R tool to pick out differently expressed genes (DEGs) between low-grade glioma and high-grade glioma, and then we used the database for annotation, visualization and integrated discovery to perform gene ontology analysis and Kyoto encyclopedia of gene and genome pathway analysis. Furthermore, we used the Cytoscape search tool for the retrieval of interacting genes with molecular complex detection plug-in applied to achieve the visualization of protein-protein interaction (PPI). We selected 15 hub genes with higher degrees of connectivity, including tissue inhibitors metalloproteinases-1 and serum amyloid A1; additionally, we used GSE53733 containing 70 glioblastoma samples to conduct Gene Set Enrichment Analysis. In conclusion, our bioinformatics analysis showed that DEGs and hub genes may be defined as new biomarkers for diagnosis and for guiding the therapeutic strategies of glioblastoma.
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Differentially expressed genes between low-grade glioma and high-grade glioma was detected by GEO2R, which was an online analysis tool based on R language (Davis & Meltzer, 2007).
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sections[4] "Screen genes of differential expression" paragraph 2
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Differentially expressed genes between low-grade glioma and high-grade glioma was detected by GEO2R, which was an online analysis tool based on R language (Davis & Meltzer, 2007).
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The following information was supplied regarding data availability: Data is available at NCBI GEO, accession numbers: GSE52009, GSE4290, GSE53733.
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[1]
6
[2]
6.42E-09
[12] array · 3 items
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PLAT
[1]
5
[2]
2.64E-08
[13] array · 3 items
[0]
PARPBP
[1]
5
[2]
6.34E-15
[14] array · 3 items
[0]
TIMP1
[1]
4
[2]
2.06E-06
[15] array · 3 items
[0]
SAA1
[1]
4
[2]
2.40E-06
locator
section[12]/table-wrap[1]#table-1
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asset:table:pmid:30867991:2
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jats-table
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application/vnd.jats.table+json
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sha256:3f482ba97ca6a42f592c0f81e350107c6efa815adda8f73e7f370bf5ad2a9426
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verified
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read_table
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available
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section[13]/table-wrap[1]#table-2
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note
Gene ontology analysis of differentially expressed genes associated with LGG and HGG.
structure object · 5 fields
pmcid
PMC6409090
section_index
13
section_title
GO function and KEGG pathway enrichment analysis
row_count
29
column_count
7
table object · 8 fields
label
Table 2
caption
Gene ontology analysis of differentially expressed genes associated with LGG and HGG.
rows array · 29 items
[0] array · 7 items
[0]
Expression
[1]
Category
[2]
Term
[3]
Count
[4]
%
[5]
P-value
[6]
FDR
[1] array · 7 items
[0]
Upregulated
[1]
GOTERM_BP_FAT
[2]
GO:0016477—cell migration
[3]
13
[4]
22.80702
[5]
3.95E-05
[6]
0.067618
[2] array · 6 items
[0]
GOTERM_BP_FAT
[1]
GO:0050900—leukocyte migration
[2]
8
[3]
14.03509
[4]
5.28E-05
[5]
0.090452
[3] array · 6 items
[0]
GOTERM_BP_FAT
[1]
GO:0040011—locomotion
[2]
14
[3]
24.5614
[4]
1.09E-04
[5]
0.185762
[4] array · 6 items
[0]
GOTERM_BP_FAT
[1]
GO:0051674—localization of cell
[2]
13
[3]
22.80702
[4]
1.22E-04
[5]
0.208673
[5] array · 6 items
[0]
GOTERM_BP_FAT
[1]
GO:0048870—cell motility
[2]
13
[3]
22.80702
[4]
1.22E-04
[5]
0.208673
[6] array · 6 items
[0]
GOTERM_MF_FAT
[1]
GO:0019838—growth factor binding
[2]
4
[3]
7.017544
[4]
0.004366
[5]
5.428929
[7] array · 6 items
[0]
GOTERM_MF_FAT
[1]
GO:0019834—phospholipase A2 inhibitor activity
[2]
2
[3]
3.508772
[4]
0.010298
[5]
12.37092
[8] array · 6 items
[0]
GOTERM_MF_FAT
[1]
GO:0005102—receptor binding
[2]
10
[3]
17.54386
[4]
0.010815
[5]
12.95228
[9] array · 6 items
[0]
GOTERM_MF_FAT
[1]
GO:0005201—extracellular matrix structural constituent
[2]
3
[3]
5.263158
[4]
0.018986
[5]
21.69308
[10] array · 6 items
[0]
GOTERM_MF_FAT
[1]
GO:0005125—cytokine activity
[2]
4
[3]
7.017544
[4]
0.019477
[5]
22.19193
[11] array · 6 items
[0]
GOTERM_CC_FAT
[1]
GO:0005615—extracellular space
[2]
16
[3]
28.07018
[4]
3.09E-06
[5]
0.003877
[12] array · 6 items
[0]
GOTERM_CC_FAT
[1]
GO:0005578—proteinaceous extracellular matrix
[2]
7
[3]
12.2807
[4]
4.24E-04
[5]
0.531176
[13] array · 6 items
[0]
GOTERM_CC_FAT
[1]
GO:0031012—extracellular matrix
[2]
8
[3]
14.03509
[4]
5.41E-04
[5]
0.676623
[14] array · 6 items
[0]
GOTERM_CC_FAT
[1]
GO:0060205—cytoplasmic membrane-bounded vesicle lumen
[2]
4
[3]
7.017544
[4]
0.002985
[5]
3.680925
[15] array · 6 items
[0]
GOTERM_CC_FAT
[1]
GO:0031983—vesicle lumen
[2]
4
[3]
7.017544
[4]
0.003066
[5]
3.779192
[16] array · 7 items
[0]
Downregulated
[1]
GOTERM_BP_FAT
[2]
GO:0007399—nervous system development
[3]
15
[4]
31.91489
[5]
3.29E-04
[6]
0.533895
[17] array · 6 items
[0]
GOTERM_BP_FAT
[1]
GO:0007420—brain development
[2]
7
[3]
14.89362
[4]
0.00548
[5]
8.553316
[18] array · 6 items
[0]
GOTERM_BP_FAT
[1]
GO:0060322—head development
[2]
7
[3]
14.89362
[4]
0.007003
[5]
10.80691
[19] array · 6 items
[0]
GOTERM_BP_FAT
[1]
GO:0031344—regulation of cell projection organization
[2]
6
[3]
12.76596
[4]
0.011466
[5]
17.11031
[20] array · 6 items
[0]
GOTERM_BP_FAT
[1]
GO:0007417—central nervous system development
[2]
7
[3]
14.89362
[4]
0.019918
[5]
27.91963
[21] array · 6 items
[0]
GOTERM_MF_FAT
[1]
GO:0005509—calcium ion binding
[2]
6
[3]
12.76596
[4]
0.021579
[5]
24.70502
[22] array · 6 items
[0]
GOTERM_MF_FAT
[1]
GO:0019911—structural constituent of myelin sheath
[2]
2
[3]
4.255319
[4]
0.02239
[5]
25.5132
[23] array · 6 items
[0]
GOTERM_MF_FAT
[1]
GO:0032403—protein complex binding
[2]
5
[3]
10.6383
[4]
0.092825
[5]
71.83703
[24] array · 6 items
[0]
GOTERM_CC_FAT
[1]
GO:0097458—neuron part
[2]
15
[3]
31.91489
[4]
6.61E-06
[5]
0.007993
[25] array · 6 items
[0]
GOTERM_CC_FAT
[1]
GO:0045202—synapse
[2]
9
[3]
19.14894
[4]
8.96E-04
[5]
1.078808
[26] array · 6 items
[0]
GOTERM_CC_FAT
[1]
GO:0043005—neuron projection
[2]
9
[3]
19.14894
[4]
0.00424
[5]
5.011629
[27] array · 6 items
[0]
GOTERM_CC_FAT
[1]
GO:0033269—internode region of axon
[2]
2
[3]
4.255319
[4]
0.01097
[5]
12.49482
[28] array · 6 items
[0]
GOTERM_CC_FAT
[1]
GO:0043209—myelin sheath
[2]
4
[3]
8.510638
[4]
0.01206
[5]
13.65505
locator
section[13]/table-wrap[1]#table-2
[6] object · 16 fields
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asset:table:pmid:30867991:3
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jats-table
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KEGG pathway analysis of differentially expressed genes associated with HGG.
structure object · 5 fields
pmcid
PMC6409090
section_index
13
section_title
GO function and KEGG pathway enrichment analysis
row_count
6
column_count
8
table object · 8 fields
label
Table 3
caption
KEGG pathway analysis of differentially expressed genes associated with HGG.
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[0] array · 8 items
[0]
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Category
[2]
Term
[3]
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[4]
%
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[6]
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[7]
FDR
[1] array · 8 items
[0]
Upregulated
[1]
KEGG_PATHWAY
[2]
hsa04510:Focal adhesion
[3]
4
[4]
7.017544
[5]
0.007454
[6]
LAMB1, VEGFA, ITGA2, COL1A1
[7]
7.094772
[2] array · 7 items
[0]
KEGG_PATHWAY
[1]
hsa04512:ECM-receptor interaction
[2]
3
[3]
5.263158
[4]
0.012926
[5]
LAMB1, ITGA2, COL1A1
[6]
12.01164
[3] array · 7 items
[0]
KEGG_PATHWAY
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hsa05146:Amoebiasis
[2]
3
[3]
5.263158
[4]
0.018811
[5]
LAMB1, CXCL8, COL1A1
[6]
17.03775
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[0]
KEGG_PATHWAY
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hsa04151:PI3K-Akt signaling pathway
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4
[3]
7.017544
[4]
0.02979
[5]
LAMB1, VEGFA, ITGA2, COL1A1
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25.72978
[5] array · 7 items
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KEGG_PATHWAY
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hsa05200:Pathways in cancer
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4
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7.017544
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0.041599
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LAMB1, VEGFA, ITGA2, CXCL8
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34.15806
locator
section[13]/table-wrap[2]#table-3
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asset:data:geo:GSE4290 not downloaded
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asset:data:geo:GSE52009 licence not read
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asset:data:geo:GSE53733 licence not read
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[0] object · 10 fields
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method-1
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network_analysis
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Network / pathway analysis
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build_graph
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centrality_scores
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pathway_enrich
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evidence object · 3 fields
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protein-protein interaction
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method-2
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network_analysis
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Network / pathway analysis
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pathway_enrich
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edge_list
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GO Enrichment
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method-3
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null
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GEO2R
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quote
Differentially expressed genes between low-grade glioma and high-grade glioma was detected by GEO2R, which was an online analysis tool based on R language (Davis & Meltzer, 2007).
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capability:computational_analysis
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meta_analysis
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pca
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gwas
candidate
Automated Assays Laravel application and simulated adapters
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complete
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nothing:replicable_now
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capability object · 4 fields
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capability:computational_analysis
availability_state
owned
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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.
data object · 6 fields
tier
R1
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[0]
GSE52009
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GSE53733
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GSE4290
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GSE52009
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GSE53733
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GSE4290
statements array · 1 item
[0]
The following information was supplied regarding data availability: Data is available at NCBI GEO, accession numbers: GSE52009, GSE4290, GSE53733.
basis
The paper names GSE52009, GSE53733, GSE4290 in Gene Expression Omnibus (GEO) database and the accession resolves in the repository. No dataset was downloaded and no licence was read.
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run_analysis
must_build object · 5 fields
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data:geo:GSE4290
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review object · 5 fields
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partial
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extraction_review
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MiniMax-M3.1-Flash
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minimax
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minimax
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agent:corpus-assay-engine
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{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:30867991", "kind": "science-record", "revision": 7, "content_hash": "sha256:6163401f022c7a19cd28999b8d89abf4965c315e96a215f7cda773899e057d94", "created_at": "2026-11-01T15:40:43Z", "updated_at": "2026-11-01T22:00:25Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of differentially expressed key genes between glioblastoma and low-grade glioma by bioinformatics analysis.", "doi": "10.7717/peerj.6560", "pmid": "30867991", "authors": ["Yang Xu", "Rongxin Geng", "Fan'en Yuan", "Qian Sun", "Baohui Liu", "Qianxue Chen"], "journal": "PeerJ", "year": 2019, "abstract": "Gliomas are a very diverse group of brain tumors that are most commonly primary tumor and difficult to cure in central nervous system. It's necessary to distinguish low-grade tumors from high-grade tumors by understanding the molecular basis of different grades of glioma, which is an important step in defining new biomarkers and therapeutic strategies. We have chosen the gene expression profile GSE52009 from gene expression omnibus (GEO) database to detect important differential genes. GSE52009 contains 120 samples, including 60 WHO II samples and 24 WHO IV samples that were selected in our analysis. We used the GEO2R tool to pick out differently expressed genes (DEGs) between low-grade glioma and high-grade glioma, and then we used the database for annotation, visualization and integrated discovery to perform gene ontology analysis and Kyoto encyclopedia of gene and genome pathway analysis. Furthermore, we used the Cytoscape search tool for the retrieval of interacting genes with molecular complex detection plug-in applied to achieve the visualization of protein-protein interaction (PPI). We selected 15 hub genes with higher degrees of connectivity, including tissue inhibitors metalloproteinases-1 and serum amyloid A1; additionally, we used GSE53733 containing 70 glioblastoma samples to conduct Gene Set Enrichment Analysis. 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