Evidence map›Paper›PMID 35958279›Full record

ArticleDisease markers2022

Weighted Gene Coexpression Network Analysis Identifies Crucial Genes Involved in Coronary Atherosclerotic Heart Disease.

Jinli Bao

Open access · hybridAbstract read
In one paragraph

Article in Disease markers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.3field-weighted citation impact, top 48% 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

3 citing papers in PubMed, 3 citations in OpenAlex.

  1. G protein regulation by RGS proteins in the pathophysiology of dilated cardiomyopathy.American journal of physiology. Heart and circulatory physiology · 2025
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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

1 author at 1 institution in 1 country.

Jinli BaoDepartment of Internal Medicine, Municipal Hospital, Zaozhuang, Shandong 277100, China.ORCID https://orcid.org/0000-0002-1016-2455
Zaozhuang Municipal Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Coronary atherosclerotic heart disease (CHD) is a lethal disease with an unstated pathogenic mechanism. Therefore, it is urgent to develop innovative strategies to ameliorate the outcome of CHD patients and explore novel biomarkers connected to the pathogenicity of CHD. Methods: The weighted gene coexpression network analysis (WGCNA) was carried out on a coronary atherosclerosis dataset GSE90074 to determine the crucial modules and hub genes for their prospective relationship to CHD. After the different modules associated with CHD have been identified, the Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enriched pathway analyses were conducted. The protein-protein interaction (PPI) network was thereafter performed for the critical module using STRING and Cytoscape. Results: The yellow module was recognized as the most critical module associated with CHD. The enriched pathways in the yellow module included those related to inflammatory response, positive regulation of extracellular signal-regulated kinase1/2 (ERK1/2) cascade, lipid catabolic process, cellular response to oxidative stress, apoptotic pathway, and NF-kappa B pathway. Further CytoHubba analysis revealed the top five hub genes ( Conclusions: The current study provides the prognosis, novel hub genes, and signaling pathways for treating coronary atherosclerosis. However, their potential biological roles require deeper investigation.

Indexed as

Coronary Artery DiseaseGene Expression ProfilingGene OntologyGene Regulatory NetworksHumansPrognosisProspective StudiesProtein Interaction Maps

Identifiers

PMID35958279
PMCPMC9363224
OpenAlexW4289524738

What OpenQuestion holds

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LicenceCC BY
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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.