Evidence map›Paper›PMID 37721628›Full record

ArticleHerz2024

Novel biomarkers identified by weighted gene co-expression network analysis for atherosclerosis.

Jiajun Ni, Kaijian Huang, Jialin Xu, Qi Lu, Chu Chen

Abstract read
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In one paragraph

Article in Herz, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 2 pooled it
–field-weighted citation impact
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

6 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
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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

5 authors.

Jiajun NiClinical Medicine-Internal Medicine, School of Medicine of Nantong University, Nantong University, No. 19 Qixiu Road, 22600, Nantong City, Jiangsu Province, China.
Kaijian HuangDepartment of Cardiology, Qidong Hospital Affiliated to Nantong University (Qidong People's Hospital), No. 568 Minle Middle Road, 226200, Qidong City, Nantong City, Jiangsu Province, China.
Jialin XuEndocrinology department, Qidong Hospital Affiliated to Nantong University (Qidong People's Hospital), No. 568 Minle Middle Road, 226200, Qidong City, Nantong City, Jiangsu Province, China.
Qi LuDepartment of Cardiology, Affiliated Hospital of Nantong University, No. 568 Minle Middle Road, 226200, Qidong City, Nantong City, Jiangsu Province, China. luqint@126.com.
Chu ChenDepartment of Cardiology, Affiliated Hospital of Nantong University, No. 568 Minle Middle Road, 226200, Qidong City, Nantong City, Jiangsu Province, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThis study aimed to screen out the potential diagnostic biomarkers for atherosclerosis (AS).

methodsWe downloaded the gene expression profiles GSE66360, GSE28829, GSE41571, GSE71226, and GSE100927 from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified using the "limma" package in R. Weighted gene co-expression network analysis (WGCNA) was applied to reveal the correlation between genes in different samples. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. The interaction pairs of proteins were retained by the STRING database, and the protein-protein interaction (PPI) network was visualized with the hub genes. Finally, the R packages "ggpubr" and "preprocessCore" were used to analyze immune cell infiltration.

resultsIn total, 40 overlapping genes both in GSE66360 and GSE28829 were found to be related to the occurrence of AS. Further, the top 10 network hub genes including TYROBP, CSF1R, TLR2, CD14, CCL4, FCER1G, CD163, TREM1, PLEK, and C5AR1 were identified as significant key genes. Moreover, four genes (TYROBP, CSF1R, FCGR1B, and CD14) were verified that could efficiently diagnose AS. Finally, the gene TYROBP was found to have a strong correlation with immune-infiltrating cells.

conclusionOur study identified four genes (TYROBP, CSF1R, FCGR1B, and CD14) that may be effective biomarkers for AS, with the potential to guide the clinical diagnosis of AS.

Indexed as

AtherosclerosisBiomarkersGene Expression ProfilingAdaptor Proteins, Signal TransducingDatabases, GeneticGene OntologyGene Regulatory NetworksHumansMembrane ProteinsProtein Interaction MapsReceptor, Macrophage Colony-Stimulating FactorReceptors, Granulocyte-Macrophage Colony-Stimulating FactorAdaptor Proteins, Signal TransducingBiomarkersCSF1R protein, humanMembrane ProteinsReceptor, Macrophage Colony-Stimulating FactorReceptors, Granulocyte-Macrophage Colony-Stimulating FactorTYROBP protein, humanClinical diagnosisGene Ontology and Kyoto Encyclopedia of Genes and Genomes pathwayImmune infiltrationInflammationProtein–protein interaction network

Identifiers

PMID37721628

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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.