Underlying Genes Involved in Atherosclerotic Macrophages: Insights from Microarray Data Mining.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:31875420", "kind": "science-record", "revision": 8, "content_hash": "sha256:0bb38a26126f308e53e4202dfb4a5f2fb494d9c49cd92533b7f8dd5917c25110", "created_at": "2026-11-01T15:40:53Z", "updated_at": "2026-11-01T22:44:26Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Underlying Genes Involved in Atherosclerotic Macrophages: Insights from Microarray Data Mining.", "doi": "10.12659/MSM.917068", "pmid": "31875420", "authors": ["Weihan Wang", "Kai Zhang", "Hao Zhang", "Mengqi Li", "Yan Zhao", "Bangyue Wang", "Wenqiang Xin", "Weidong Yang", "Jianning Zhang", "Shuyuan Yue", "Xinyu Yang"], "journal": "Medical science monitor : international medical journal of experimental and clinical research", "year": 2019, "abstract": "BACKGROUND In an atherosclerotic artery wall, monocyte-derived macrophages are the principal mediators that respond to pathogens and inflammation. The present study aimed to investigate potential genetic changes in gene expression between normal tissue-resident macrophages and atherosclerotic macrophages in the human body. MATERIAL AND METHODS The expression profile data of GSE7074 acquired from the Gene Expression Omnibus (GEO) database, which includes the transcriptome of 4 types of macrophages, was downloaded. Differentially expressed genes (DEGs) were identified using R software, then we performed functional enrichment, protein‑protein interaction (PPI) network construction, key node and module analysis, and prediction of microRNAs (miRNAs)/transcription factors (TFs) targeting genes. RESULTS After data processing, 236 DEGs were identified, including 21 upregulated genes and 215 downregulated genes. The DEG set was enriched in 22 significant Gene Ontology (GO) terms and 25 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and the PPI network constructed with these DEGs comprised 6 key nodes with degrees ≥8. Key nodes in the PPI network and simultaneously involved in the prime modules, including rhodopsin (RHO), coagulation factor V (F5), and bestrophin-1 (BEST1), are promising for the prediction of atherosclerotic plaque formation. Furthermore, in the miRNA/TF-target network, hsa-miR-3177-5p might be involved in the pathogenesis of -atherosclerosis via regulating BEST1, and the transcription factor early growth response-1 (EGR1) was found to be a potential promoter in atherogenesis. CONCLUSIONS The identified key hub genes, predicted miRNAs/TFs, and underlying molecular mechanisms may be involved in atherogenesis, thus potentially contributing to the treatment and diagnosis of patients with atherosclerotic disease.", "claims": [{"id": "claim-method", "statement": "After the probe ID was converted into a universal name for genes (official gene symbol), gene differential expression analysis was performed using the limma package (from the Bioconductor, http://www.bioconductor.org/), in R as well.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:31875420", "locator": "sections[4] \"PCA analysis, identification of DEGs, and hierarchical clustering analysis\" paragraph 2", "quote": "After the probe ID was converted into a universal name for genes (official gene symbol), gene differential expression analysis was performed using the limma package (from the Bioconductor, http://www.bioconductor.org/), in R as well."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "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:31875420"], "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": "blocked", "blocked_on": "data:accession_absent", "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": [], "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": [], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:obtain_source_data blocked on data:accession_absent"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:31875420", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.12659/MSM.917068", "sha256": 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SRR, DLK1, SHROOM2, MPO, FGF11, NPTXR, CD302, STMN1, DRD5, CYP4F8, KCNQ1OT1, CD79A, ZNF277, GBA, PRTFDC1, MAD2L2, VPS11, MYO15A, PDCD10, MYL9, IQCC, TNFAIP6, C1orf159, HAX1, FSCN1, FBXO21, SNORA71B, FBXO24, GPD1L, FPR3, UBL7-AS1, ARNTL2, NDRG4, P2RX4, DRD3, RNASE7, PEX11B, HDLBP, PROP1, HOXC6, FAM204A, MIS18A, KCNJ2, NRSN2, GCNT3, SNAPC2, SLC52A1, SPARCL1, POLR2F, PAX4, PRDM1, CRTAC1, RHO, XXYLT1, KIAA1324, MOGAT3, POLD4, DGAT1, RNLS, CRYBB1, ATP13A1, ZNF496, EIF2B4, NOCT, CHGB, DNAJC2, SLC25A40, LOC340085, ZNF154, FAM131A, HIST1H3E, NOC3L, CCNJ, ARL6IP5, NKX2-8, XCL1, XKR8, ERBB3, VPREB3, ICOS, IL3, GOSR2, OR5L2, KIFC1, BCO1, DUSP22, F5, CLCNKB, CDC42EP2, ACSS2, ZNF148, NLRP1, PDE6G, SCD5, XCR1, SLC39A10, CNTLN, HCRTR2, LINC00115, TULP2, CCM2, SGO1, VIPR2, FUT8, WNT2B, PDPK1, AP1S1, ZNF593, TSTA3, IFT20, MGAT3, AIPL1, POLD2, OR1A1, NAA20, ABCA4, TRAF4, ATP6V1G1, LRRC8A, SPSB4, HIST1H2BL, EFHD1, HIC1, MAP3K12, ROM1, PSMD6-AS2, LOC100507547, YOD1, NDUFA3, MEF2C-AS1, LHX2, SH2B1, AES, 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