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pmid:34861826

Identification of four hub genes in venous thromboembolism via weighted gene coexpression network analysis.

Guoju Fan · Zhihai Jin · Kaiqiang Wang · Huitang Yang · Jun Wang · Yankui Li · Bo Chen · Hongwei Zhang

BMC cardiovascular disorders · 2021 doi:10.1186/s12872-021-02409-4

Paper

complete

Experiment

complete

Data

partial

Analysis

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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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pmid:34861826
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science-record
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2026-11-01T17:36:33Z
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title
Identification of four hub genes in venous thromboembolism via weighted gene coexpression network analysis.
doi
10.1186/s12872-021-02409-4
pmid
34861826
authors array · 8 items
[0]
Guoju Fan
[1]
Zhihai Jin
[2]
Kaiqiang Wang
[3]
Huitang Yang
[4]
Jun Wang
[5]
Yankui Li
[6]
Bo Chen
[7]
Hongwei Zhang
journal
BMC cardiovascular disorders
year
2021
abstract
BACKGROUND: The pathogenic mechanisms of venous thromboembolism (VT) remain to be defined. This study aimed to identify differentially expressed genes (DEGs) that could serve as potential therapeutic targets for VT. METHODS: Two human datasets (GSE19151 and GSE48000) were analyzed by the robust rank aggregation method. Gene ontology and Kyoto encyclopedia of genes and genomes pathway enrichment analyses were conducted for the DEGs. To explore potential correlations between gene sets and clinical features and to identify hub genes, we utilized weighted gene coexpression network analysis (WGCNA) to build gene coexpression networks incorporating the DEGs. Then, the levels of the hub genes were analyzed in the GSE datasets. Based on the expression of the hub genes, the possible pathways were explored by gene set enrichment analysis and gene set variation analysis. Finally, the diagnostic value of the hub genes was assessed by receiver operating characteristic (ROC) analysis in the GEO database. RESULTS: In this study, we identified 54 upregulated and 10 downregulated genes that overlapped between normal and VT samples. After performing WGCNA, the magenta module was the module with the strongest negative correlation with the clinical characteristics. From the key module, FECH, GYPA, RPIA and XK were chosen for further validation. We found that these genes were upregulated in VT samples, and high expression levels were related to recurrent VT. Additionally, the four hub genes might be highly correlated with ribosomal and metabolic pathways. The ROC curves suggested a diagnostic value of the four genes for VT. CONCLUSIONS: These results indicated that FECH, GYPA, RPIA and XK could be used as promising biomarkers for the prognosis and prediction of VT.
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The DEGs that met the criteria of adjusted p value < 0.05 and log 2-fold change (FC) > 0.5 were filtered by robust rank aggregation (RRA) [16].
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The gene expression profiles of GSE19151 and GSE48000 were downloaded from Gene Expression Omnibus (GEO).
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The study identified 54 upregulated and 10 downregulated genes that overlapped between normal and VT samples.
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In this study, we identified 54 upregulated and 10 downregulated genes that overlapped between normal and VT samples.
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Whole Blood Gene Expression Profiles Distinguish Patients with Single versus Recurrent Venous Thromboembolism
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Whole blood gene expression profiles distinguish clinical phenotypes of venous thromboembolism [Set1]
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lineage object · 4 fields
parent_id
pmid:34861826
parent_hash
sha256:3d916a0c0b1119126894f6fa453e6579a78dbd0bb0b614dd08dd1312a3d6b738
fork_of
null
gaps array · 9 items
[0]
asset:data:geo:GSE19151 licence not read
[1]
asset:data:geo:GSE19151 not downloaded
[2]
asset:data:geo:GSE48000 licence not read
[3]
asset:data:geo:GSE48000 not downloaded
[4]
execution.not_run
[5]
method.reader_named_unmapped
[6]
review.human_critique
[7]
review.independent_reader
[8]
review.machine_critique
assays array · 1 item
[0] object · 11 fields
role
analysis
definition_id
network_analysis
definition_version
f2132e96ae6c7a4e
required_operations array · 1 item
[0]
pathway_enrich
support_state
supported
source_hashes object · 1 field
dsl_catalogue
f2132e96ae6c7a4e
evidence array · 1 item
[0]
pathway enrichment
order_basis
analysis_only
id
assay:1
Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34861826", "kind": "science-record", "revision": 3, "content_hash": "sha256:6a084ef92a1bd5ad79d180d7725c74c41b36f4708076a0770803700ef0b65b36", "created_at": "2026-11-01T17:36:33Z", "updated_at": "2026-11-01T21:26:43Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of four hub genes in venous thromboembolism via weighted gene coexpression network analysis.", "doi": "10.1186/s12872-021-02409-4", "pmid": "34861826", "authors": ["Guoju Fan", "Zhihai Jin", "Kaiqiang Wang", "Huitang Yang", "Jun Wang", "Yankui Li", "Bo Chen", "Hongwei Zhang"], "journal": "BMC cardiovascular disorders", "year": 2021, "abstract": "BACKGROUND: The pathogenic mechanisms of venous thromboembolism (VT) remain to be defined. This study aimed to identify differentially expressed genes (DEGs) that could serve as potential therapeutic targets for VT.\nMETHODS: Two human datasets (GSE19151 and GSE48000) were analyzed by the robust rank aggregation method. Gene ontology and Kyoto encyclopedia of genes and genomes pathway enrichment analyses were conducted for the DEGs. To explore potential correlations between gene sets and clinical features and to identify hub genes, we utilized weighted gene coexpression network analysis (WGCNA) to build gene coexpression networks incorporating the DEGs. Then, the levels of the hub genes were analyzed in the GSE datasets. Based on the expression of the hub genes, the possible pathways were explored by gene set enrichment analysis and gene set variation analysis. Finally, the diagnostic value of the hub genes was assessed by receiver operating characteristic (ROC) analysis in the GEO database.\nRESULTS: In this study, we identified 54 upregulated and 10 downregulated genes that overlapped between normal and VT samples. After performing WGCNA, the magenta module was the module with the strongest negative correlation with the clinical characteristics. From the key module, FECH, GYPA, RPIA and XK were chosen for further validation. We found that these genes were upregulated in VT samples, and high expression levels were related to recurrent VT. Additionally, the four hub genes might be highly correlated with ribosomal and metabolic pathways. The ROC curves suggested a diagnostic value of the four genes for VT.\nCONCLUSIONS: These results indicated that FECH, GYPA, RPIA and XK could be used as promising biomarkers for the prognosis and prediction of VT.", "claims": [{"id": "claim-method", "statement": "The DEGs that met the criteria of adjusted p value < 0.05 and log 2-fold change (FC) > 0.5 were filtered by robust rank aggregation (RRA) [16].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34861826", "locator": "sections[4] \"Identification of robust DEGs\" paragraph 2", "quote": "The DEGs that met the criteria of adjusted p value < 0.05 and log 2-fold change (FC) > 0.5 were filtered by robust rank aggregation (RRA) [16]."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The gene expression profiles of GSE19151 and GSE48000 were downloaded from Gene Expression Omnibus (GEO).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34861826", "locator": "sections[26] \"Availability of data and materials\" paragraph 2", "quote": "The gene expression profiles of GSE19151 and GSE48000 were downloaded from Gene Expression Omnibus (GEO)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study identified 54 upregulated and 10 downregulated genes that overlapped between normal and VT samples.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:34861826", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "In this study, we identified 54 upregulated and 10 downregulated genes that overlapped between normal and VT samples."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study identified 54 upregulated and 10 downregulated genes that overlapped between normal and VT samples.", "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:34861826"], "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:GSE19151"], "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:GSE19151"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:34861826", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12872-021-02409-4", "sha256": 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