Evidence map›Paper›PMID 39130409›Full record

ArticleHeliyon2024

Identification of macrophage differentiation related genes and subtypes linking atherosclerosis plaque processing and metabolic syndrome via integrated bulk and single-cell sequence analysis.

Da-Sheng Ning, Zi-Qing Zhou, Shu-Heng Zhou, Ji-Mei Chen

Abstract read
In one paragraph

Article in Heliyon, 2024. 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
–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

3 citing papers in PubMed.

  1. 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

4 authors.

Da-Sheng NingGuangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Guangzhou 510080, PR China.
Zi-Qing ZhouGuangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Guangzhou 510080, PR China.
Shu-Heng ZhouGuangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Guangzhou 510080, PR China.
Ji-Mei ChenGuangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Guangzhou 510080, PR China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Metabolic syndrome(MS) is a separate risk factor for the advancement of atherosclerosis(AS) plaque but mechanism behind this remains unclear. There may be a significant role for the immune system in this process. This study aims to identify potential diagnostic genes in MS patients at a higher risk of developing and progressing to AS. Datasets were retrevied from gene expression omnibus(GEO) database and differentially expressed genes were identified. Hub genes, immune cell dysregulation and AS subtypes were identified using a conbination of muliple bioinformatic analysis, machine learning and consensus clustering. Diagnostic value of hub genes was estimated using a nomogram and ROC analysis. Finally, enrichment analysis, competing endogenous RNA(ceRNA) network, single-cell RNA(scRNA) sequencing analysis and drug-protein interaction prediction was constructed to identify the functional roles, potential regulators and distribution for hub genes. Four hub genes and two macrophage-related subtypes were identified. Their strong diagnostic value was validated and functional process were identified. ScRNA analysis identified the macrophage differentiation regulation function of F13A1. CeRNA network and drug-protein binding modes revealed the potential therapeutic method. Four immune-correlated hub genes(F13A1, MMRN1, SLCO2A1 and ZNF521) were identified with their diagnostic value being assesed, which F13A1 was found strong correlated with macrophage differentiation and could be potential diagnostic and therapeutic marker for AS progression in MS patients.

Indexed as

AtherosclerosisImmune infiltrationMacrophage differentiationMechine learningMetabolic syndromeSingle-cell RNA sequence analysis

Identifiers

PMID39130409
PMCPMC11315131

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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.