Evidence map›Paper›PMID 31875420›Full record

ArticleMedical science monitor : international medical journal of experimental and clinical research2019

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

Weihan Wang, Kai Zhang, Hao Zhang, Mengqi Li, Yan Zhao, Bangyue Wang, Wenqiang Xin, Weidong Yang, Jianning Zhang, Shuyuan Yue and 1 more

Open access · hybridAbstract read
In one paragraph

Article in Medical science monitor : international medical journal of experimental and clinical research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.8field-weighted citation impact, top 28% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 13 citations in OpenAlex.

  1. CaActa physiologica (Oxford, England) · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 1 institution in 1 country.

Weihan WangDepartment of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China (mainland).
Kai ZhangDepartment of Gynecology and Obstetrics, Tianjin Medical University General Hospital, Tianjin, China (mainland).
Hao ZhangDepartment of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China (mainland).
Mengqi LiDepartment of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China (mainland).
Yan ZhaoDepartment of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China (mainland).
Bangyue WangDepartment of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China (mainland).
Wenqiang XinDepartment of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China (mainland).
Weidong YangDepartment of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China (mainland).
Jianning ZhangDepartment of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China (mainland).
Shuyuan YueDepartment of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China (mainland).
Xinyu YangDepartment of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China (mainland).
Tianjin Medical University General Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

AtherosclerosisComputational BiologyDatabases, GeneticData MiningGene Expression ProfilingGene Expression Regulation, NeoplasticGene OntologyGene Regulatory NetworksHumansMacrophagesMicroRNAsOligonucleotide Array Sequence AnalysisProtein Interaction MapsSoftwareTranscription FactorsTranscriptomeMicroRNAsTranscription Factors

Identifiers

PMID31875420
PMCPMC6944040
OpenAlexW2998394575

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.