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pmid:32160184
Crucial Gene Identification in Carotid Atherosclerosis Based on Peripheral Blood Mononuclear Cell (PBMC) Data by Weighted (Gene) Correlation Network Analysis (WGCNA).
Siliang Chen · Dan Yang · Zhili Liu · Fangda Li · Bao Liu · Yuexin Chen · Wei Ye · Yuehong Zheng
Medical science monitor : international medical journal of experimental and clinical research · 2020 doi:10.12659/MSM.921692
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:32160184", "kind": "science-record", "revision": 8, "content_hash": "sha256:09c367a4ce58f005dcea46947483398f7ecf77624e6f6a8e9f9fae60482b9d25", "created_at": "2026-11-01T15:40:57Z", "updated_at": "2026-11-01T22:44:38Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Crucial Gene Identification in Carotid Atherosclerosis Based on Peripheral Blood Mononuclear Cell (PBMC) Data by Weighted (Gene) Correlation Network Analysis (WGCNA).", "doi": "10.12659/MSM.921692", "pmid": "32160184", "authors": ["Siliang Chen", "Dan Yang", "Zhili Liu", "Fangda Li", "Bao Liu", "Yuexin Chen", "Wei Ye", "Yuehong Zheng"], "journal": "Medical science monitor : international medical journal of experimental and clinical research", "year": 2020, "abstract": "BACKGROUND Many patients are not responsive or tolerant to medical therapies for carotid atherosclerosis. Thus, elucidating the molecular mechanism for the pathogenesis and progression of carotid atherosclerosis and identifying new potential molecular targets for medical therapies that can slow progression of carotid atherosclerosis and prevent ischemic events are quite important. MATERIAL AND METHODS We downloaded the expression profiling data of PBMC in Biobank of Karolinska Endarterectomy (BiKE, GSE21545) for GEO. The WGCNA and DEG screening were conducted. The co-expression pattern between patients with ischemic events (the events group) and patients without ischemic events (the no-events group) were compared. Then, we identified hub genes of each module. Finally, the DEG co-expression network was constructed and MCODE was used to identify crucial genes based on this co-expression network. RESULTS In the study, 183 DEGs were screened and 8 and 6 modules were assessed in the events group and no-events group, respectively. Compared to the no-events group, genes associated with inflammation and immune response were clustered in the green-yellow module of the events group. The hub gene of the green-yellow module of the events group was KIR2DL5A. We obtained 1 DEG co-expression network, which has 16 nodes and 24 edges, and we detected 5 crucial genes: SIRT1, THRAP3, RBM43, PEX1, and KLHDC2. The upregulated genes (THRAP3 and RBM43) showed potential diagnostic and prognostic value for the occurrence of ischemic events. CONCLUSIONS We detected 8 modules for the events group and 6 modules for the no-events group. The hub genes for modules and crucial genes of the DEG co-expression network were also identified. These genes might serve as potential targets for medical therapies and biomarkers for diagnosis and prognosis. Further experimental and biological studies are needed to elucidate the role of these crucial genes in the progression of carotid atherosclerosis.", "claims": [{"id": "claim-method", "statement": "Differentially expressed genes (DEGs) were screened using the “limma” R package.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32160184", "locator": "sections[4] \"DEG analysis\" paragraph 2", "quote": "Differentially expressed genes (DEGs) were screened using the “limma” R package."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Supplementary/raw data available from the corresponding author on request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32160184", "locator": "sections[18] \"Footnotes\" paragraph 2", "quote": "Supplementary/raw data available from the corresponding author on request."}], "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:32160184"], "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:GSE21545"], "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:GSE21545"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:32160184", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.12659/MSM.921692", "sha256": 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