Evidence map›Paper›PMID 42136960›Full record

ArticleHuman mutation2026

Role of Macrophage-Related Genes GAS7 and ZEB2 in Acute Myocardial Infarction Pathogenesis.

Zhenfang Liu, Jia Song, Lingling Li, Ziyin Fan, Genyuan Xie, Li Yang

Abstract read
In one paragraph

Article in Human mutation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

6 authors.

Zhenfang LiuDepartment of Cardiovascular Medicine, The Affiliated Zhuzhou Hospital Xiangya Medical College, Central South University, Zhuzhou, China, csu.edu.cn.ORCID https://orcid.org/0009-0001-2536-8587
Jia SongDepartment of Cardiovascular Medicine, The Affiliated Zhuzhou Hospital Xiangya Medical College, Central South University, Zhuzhou, China, csu.edu.cn.ORCID https://orcid.org/0009-0008-8398-133X
Lingling LiDepartment of Cardiovascular Medicine, The Affiliated Zhuzhou Hospital Xiangya Medical College, Central South University, Zhuzhou, China, csu.edu.cn.ORCID https://orcid.org/0009-0007-6472-0436
Ziyin FanDepartment of Cardiovascular Medicine, The Affiliated Zhuzhou Hospital Xiangya Medical College, Central South University, Zhuzhou, China, csu.edu.cn.ORCID https://orcid.org/0009-0005-0702-5264
Genyuan XieDepartment of Cardiovascular Medicine, The Affiliated Zhuzhou Hospital Xiangya Medical College, Central South University, Zhuzhou, China, csu.edu.cn.ORCID https://orcid.org/0009-0007-5503-0310
Li YangDepartment of Cardiovascular Medicine, The Affiliated Zhuzhou Hospital Xiangya Medical College, Central South University, Zhuzhou, China, csu.edu.cn.ORCID https://orcid.org/0000-0001-6410-5715

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Acute myocardial infarction (AMI) involves complex immune responses and cellular interaction mechanisms. Although the pathogenesis of AMI is now preliminarily understood, there is still a lack of biomarkers that can accurately and rapidly diagnose its disease characteristics. Methods: This study analyzed single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data related to AMI from the Gene Expression Omnibus (GEO) database. Data preprocessing and clustering were performed using Seurat, and cell-cell communication was analyzed using CellChat. Functional enrichment was performed using clusterProfiler. Key transcription factors were identified using SCENIC, and module-specific genes associated with macrophages in AMI were identified using high-dimensional weighted gene coexpression network analysis (hdWGCNA). These were combined with WGCNA to identify genes associated with AMI. Molecular docking was then used to predict potential targeted drugs for AMI. In addition, an oxygen-glucose deprivation (OGD)-induced AC16 cardiomyocyte model combined with quantitative real-time polymerase chain reaction (qRT-PCR) was used to validate the expression patterns of the key regulators and biomarkers. Results: This study identified six cell types in AMI, including adipocytes, cardiomyocytes, endothelial cells, fibroblasts, macrophages, and smooth muscle cells. CellChat showed that cell-cell communication intensity was generally enhanced in AMI. hdWGCNA identified the M5 and M8 modules as significantly associated with macrophages. SCENIC analysis found that Conclusion: This study revealed the cellular communication characteristics in the pathogenesis of AMI and identified

Indexed as

MacrophagesMyocardial InfarctionAnimalsBiomarkersCell CommunicationComputational BiologyGene Expression ProfilingGene Expression RegulationGene Regulatory NetworksHumansMolecular Docking SimulationMyocytes, CardiacRepressor ProteinsSingle-Cell Gene Expression AnalysisBiomarkersRepressor Proteinsacute myocardial infarctiondiagnosishigh-dimensional weighted gene coexpression network analysismacrophagesmolecular dockingsingle-cell transcriptomics analysis

Identifiers

PMID42136960
PMCPMC13168794

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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.