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pmid:33313152

Identification and validation of key genes mediating intracranial aneurysm rupture by weighted correlation network analysis.

Siliang Chen · Dan Yang · Bao Liu · Lei Wang · Yuexin Chen · Wei Ye · Changwei Liu · Leng Ni · Xiaobo Zhang · Yuehong Zheng

Annals of translational medicine · 2020 doi:10.21037/atm-20-4083

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Experiment

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:33313152
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science-record
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2026-11-01T15:41:19Z
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paper object · 10 fields
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title
Identification and validation of key genes mediating intracranial aneurysm rupture by weighted correlation network analysis.
doi
10.21037/atm-20-4083
pmid
33313152
authors array · 10 items
[0]
Siliang Chen
[1]
Dan Yang
[2]
Bao Liu
[3]
Lei Wang
[4]
Yuexin Chen
[5]
Wei Ye
[6]
Changwei Liu
[7]
Leng Ni
[8]
Xiaobo Zhang
[9]
Yuehong Zheng
journal
Annals of translational medicine
year
2020
abstract
BACKGROUND: Rupture of intracranial aneurysm (IA) is the leading cause of subarachnoid hemorrhage. However, there are few pharmacological therapies available for the prevention of IA rupture. Therefore, exploring the molecular mechanisms which underlie IA rupture and identifying the potential molecular targets for preventing the rupture of IA is of vital importance. METHODS: We used the Gene Expression Omnibus (GEO) datasets GSE13353, GSE15629, and GSE54083 in our study. The 3 datasets were merged and normalized. Differentially expressed gene (DEG) screening and weighted correlation network analysis (WGCNA) were conducted. The co-expression patterns between ruptured IA samples and unruptured IA samples were compared. Then, the DEGs were mapped into the whole co-expression network of ruptured IA samples, and a DEG co-expression network was generated. Molecular Complex Detection (MCODE) (http://baderlab.org/Software/MCODE) was used to identify key genes based on the DEG co-expression network. Finally, key genes were validated using another GEO dataset (GSE122897), and their potential diagnostic values were shown using receiver operating characteristic (ROC) analysis. RESULTS: In our study, 49 DEGs were screened while 8 and 6 gene modules were detected based on ruptured IA samples and unruptured IA samples, respectively. Pathways associated with inflammation and immune response were clustered in the salmon module of ruptured IA samples. The DEG co-expression network with 35 nodes and 168 edges was generated, and 14 key genes were identified based on this DEG co-expression network. The gene with the highest degree in the key gene cluster was CXCR4. All key genes were validated using GSE122897, and they all showed the potential diagnostic value in predicting IA rupture. CONCLUSIONS: Using a weighted gene co-expression network approach, we identified 8 and 6 modules for ruptured IA and unruptured IA, respectively. After that, we identified the hub genes for each module and key genes based on the DEG co-expression network. All these key genes were validated by another GEO dataset and might serve as potential targets for pharmacological therapies and diagnostic markers in predicting IA rupture. Further studies are needed to elucidate the detailed molecular mechanisms and biological functions of these key genes which underlie the rupture of IA.
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The raw datasets were available from the GEO database (http://www.ncbi.nlm.nih.gov/geo/; GSE13353, GSE15629, GSE54083 and GSE122897).
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RNA Sequencing Analysis of Intracranial Aneurysm Walls Reveals Involvement of Lysosomes and Immunoglobulins in Rupture
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Comparison of gene expression between ruptured and unruptured human intracranial aneurysms
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Global expression profiling of ruptured and unruptured human intracranial aneurysms
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Gene expression profiling of intracranial aneurysms
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DEGs with top-10 fold-change (ruptured IA/unruptured IA)
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Table 1
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DEGs with top-10 fold-change (ruptured IA/unruptured IA)
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[1]
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[1]
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[1]
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[1]
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[4] array · 3 items
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[1]
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[5] array · 3 items
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[1]
Formyl peptide receptor 1
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[6] array · 3 items
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[1]
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[1]
Neutrophil cytosolic factor 2
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[1]
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Uridine phosphorylase 1
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[1]
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[1]
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[1]
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[1]
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GO-BP terms and KEGG pathways with top-10 count number for ruptured IA
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GO-BP terms and KEGG pathways with top-10 count number for ruptured IA
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[1]
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[3]
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[3]
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[1]
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[3]
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Key genes mediating rupture of IA
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Table 3
caption
Key genes mediating rupture of IA
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[1]
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[1]
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[2]
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366
[1]
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[1]
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[1]
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[2]
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[1]
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[2]
PDZ domain containing ring finger 3
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284
[1]
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2357
[1]
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[2]
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290
[1]
ANPEP
[2]
Alanyl aminopeptidase, membrane
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Neutrophil cytosolic factor 2
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128346
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C1orf162
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Chromosome 1 open reading frame 162
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10312
[1]
TCIRG1
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T cell immune regulator 1, ATPase H+ transporting V0 subunit a3
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7852
[1]
CXCR4
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C-X-C motif chemokine receptor 4
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2268
[1]
FGR
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FGR proto-oncogene, Src family tyrosine kinase
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section[16]/table-wrap[1]#t3
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asset:data:geo:GSE122897 not downloaded
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[3]
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asset:data:geo:GSE15629 not downloaded
[5]
asset:data:geo:GSE15629 licence not read
[6]
asset:data:geo:GSE54083 not downloaded
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ROC analysis
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Weighted correlation network analysis (WGCNA) using the WGCNA R package; source data were obtained from GEO datasets GSE13353, GSE15629, and GSE54083.
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“The WGCNA R package was used to construct the co-expression networks of ruptured IA samples and unruptured IA samples.” “The datasets GSE13353, GSE15629, and GSE54083 were selected from the GEO database.”
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gaps array · 1 item
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method.reader_named_unmapped
capability object · 3 fields
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complete
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capability:computational_analysis
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meta_analysis
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pca
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candidate
Automated Assays Laravel application and simulated adapters
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readiness object · 8 fields
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The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: roc.
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GSE13353
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GSE13353
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GSE54083
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GSE122897
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The raw datasets were available from the GEO database (http://www.ncbi.nlm.nih.gov/geo/; GSE13353, GSE15629, GSE54083 and GSE122897).
basis
The paper names GSE13353, GSE15629, GSE54083, GSE122897 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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data:geo:GSE122897
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MiniMax-M3.1-Flash
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minimax
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minimax
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reordered
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PMID 33313152 full text
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GEO GSE122897
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33313152", "kind": "science-record", "revision": 7, "content_hash": "sha256:521bb38a641b290e17fec890af86a594c79f9e42ba39eb50ffbfbacc7f105a76", "created_at": "2026-11-01T15:41:19Z", "updated_at": "2026-11-01T22:02:43Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification and validation of key genes mediating intracranial aneurysm rupture by weighted correlation network analysis.", "doi": "10.21037/atm-20-4083", "pmid": "33313152", "authors": ["Siliang Chen", "Dan Yang", "Bao Liu", "Lei Wang", "Yuexin Chen", "Wei Ye", "Changwei Liu", "Leng Ni", "Xiaobo Zhang", "Yuehong Zheng"], "journal": "Annals of translational medicine", "year": 2020, "abstract": "BACKGROUND: Rupture of intracranial aneurysm (IA) is the leading cause of subarachnoid hemorrhage. However, there are few pharmacological therapies available for the prevention of IA rupture. Therefore, exploring the molecular mechanisms which underlie IA rupture and identifying the potential molecular targets for preventing the rupture of IA is of vital importance.\nMETHODS: We used the Gene Expression Omnibus (GEO) datasets GSE13353, GSE15629, and GSE54083 in our study. The 3 datasets were merged and normalized. Differentially expressed gene (DEG) screening and weighted correlation network analysis (WGCNA) were conducted. The co-expression patterns between ruptured IA samples and unruptured IA samples were compared. Then, the DEGs were mapped into the whole co-expression network of ruptured IA samples, and a DEG co-expression network was generated. Molecular Complex Detection (MCODE) (http://baderlab.org/Software/MCODE) was used to identify key genes based on the DEG co-expression network. Finally, key genes were validated using another GEO dataset (GSE122897), and their potential diagnostic values were shown using receiver operating characteristic (ROC) analysis.\nRESULTS: In our study, 49 DEGs were screened while 8 and 6 gene modules were detected based on ruptured IA samples and unruptured IA samples, respectively. Pathways associated with inflammation and immune response were clustered in the salmon module of ruptured IA samples. The DEG co-expression network with 35 nodes and 168 edges was generated, and 14 key genes were identified based on this DEG co-expression network. The gene with the highest degree in the key gene cluster was CXCR4. All key genes were validated using GSE122897, and they all showed the potential diagnostic value in predicting IA rupture.\nCONCLUSIONS: Using a weighted gene co-expression network approach, we identified 8 and 6 modules for ruptured IA and unruptured IA, respectively. After that, we identified the hub genes for each module and key genes based on the DEG co-expression network. All these key genes were validated by another GEO dataset and might serve as potential targets for pharmacological therapies and diagnostic markers in predicting IA rupture. Further studies are needed to elucidate the detailed molecular mechanisms and biological functions of these key genes which underlie the rupture of IA.", "claims": [{"id": "claim-data-access", "statement": "The raw datasets were available from the GEO database (http://www.ncbi.nlm.nih.gov/geo/; GSE13353, GSE15629, GSE54083 and GSE122897).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33313152", "locator": "sections[3] \"Medical ethics\" paragraph 2", "quote": "The raw datasets were available from the GEO database (http://www.ncbi.nlm.nih.gov/geo/; GSE13353, GSE15629, GSE54083 and GSE122897)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:33313152"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE122897"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE122897"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:33313152", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/atm-20-4083", "sha256": 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"structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The raw datasets were available from the GEO database (http://www.ncbi.nlm.nih.gov/geo/; GSE13353, GSE15629, GSE54083 and GSE122897).", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM3487837", "GSM3487838", "GSM3487839", "GSM3487840", "GSM3487841", "GSM3487842", "GSM3487843", "GSM3487844", "GSM3487845", "GSM3487846", "GSM3487847", "GSM3487848", "GSM3487849", "GSM3487850", "GSM3487851", "GSM3487852", "GSM3487853", "GSM3487854", "GSM3487855", "GSM3487856", "GSM3487857", "GSM3487858", "GSM3487859", "GSM3487860", "GSM3487861", "GSM3487862", "GSM3487863", "GSM3487864", "GSM3487865", "GSM3487866", "GSM3487867", "GSM3487868", "GSM3487869", "GSM3487870", "GSM3487871", "GSM3487872", "GSM3487873", "GSM3487874", "GSM3487875", "GSM3487876", "GSM3487877", "GSM3487878", "GSM3487879", "GSM3487880", "GSM3487881", "GSM3487882", "GSM3487883", "GSM3487884", 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"3.64"], ["GO:0045087", "Innate immune response", "9", "2.89"], ["GO:0042742", "Defense response to bacterium", "7", "4.09"], ["GO:0007155", "Cell adhesion", "7", "1.58"], ["GO:0002576", "Platelet degranulation", "6", "3.81"], ["GO:0051092", "Positive regulation of NF-kappaB transcription factor activity", "6", "3.29"], ["GO:0032496", "Response to lipopolysaccharide", "6", "2.88"], ["GO:0030593", "Neutrophil chemotaxis", "5", "3.50"], ["KEGG pathway", "", "", ""], ["hsa04060", "Cytokine-cytokine receptor interaction", "8", "2.93"], ["hsa04062", "Chemokine signaling pathway", "5", "1.43"]], "row_count": 15, "column_count": 4, "numeric_cells": 36, "header_rows": 1, "locator": "section[15]/table-wrap[1]#t2"}}, {"id": "asset:table:pmid:33313152:3", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:e42d06b2a6a0343402ab29987ab4e87b9b42a464edfbc67a88d4da07fd58a455", "bytes": 924, "access": "open", "state": "verified", 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