Evidence map›Paper›PMID 39024234›Full record

ArticlePloS one2024

Identification of key genes associated with acute myocardial infarction using WGCNA and two-sample mendelian randomization study.

Xiaohe Yang, Yingtao Huang, Dadong Tang, Liangming Yue

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Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Xiaohe YangDepartment of Cardiology, Guangyuan Hospital of Traditional Chinese Medicine, Guangyuan, China.
Yingtao HuangDepartment of Orthopedics, Liaoning University of Traditional Chinese Medicine, Shenyang, China.
Dadong TangSchool of Clinical College of Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Liangming YueDepartment of Cardiology, Guangyuan Hospital of Traditional Chinese Medicine, Guangyuan, China.ORCID 0000-0002-2076-2788

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveAcute myocardial infarction (AMI) is a severe condition with high morbidity and mortality rates. This study aimed to identify hub genes potentially associated with AMI and assess their clinical utility in predicting AMI occurrence.

methodsGene microarray data were obtained from the Gene Expression Omnibus (GEO) database. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were conducted on samples from patients with AMI and control samples to identify modules significantly associated with AMI. GO and KEGG analyses were applied to investigate the potential functions of these hub genes. Lastly, the mendelian randomization (MR) method was applied to analyze the causal relationship between the hub gene TNF and AMI.

results285 differentially expressed genes (DEGs) were identified through WCGNA and were clustered into 6 modules. The yellow module appeared most relevant to AMI. Further exploration through GO and KEGG pathway enrichment showed that key hub genes in the yellow module were linked to positive regulation of cytokine production, cytokine receptor binding, NF-kappa B signaling pathway, IL-17 signaling pathway, and TNF signaling pathway. The top 10 genes identified through Cytoscape software analysis were IL1B, TNF, TLR4, TLR2, FCGR3B, MMP9, CXCL8, TLR8, ICAM1, and JUK. Utilizing inverse variance weighting (IVW) analysis, we discovered a significant association between TNF and AMI risk, with an OR of 0.946 (95% CI = 0.911-0.984, p = 0.005).

conclusionsThe result of this study indicated that TNF, TLR2, TLR4, IL1B and FCGR3B may be potential biodiagnostic markers for AMI. TNF can inhibit inflammatory and oxidative stress responses in AMI, exerting a protective role in the heart.

Indexed as

Gene Regulatory NetworksMendelian Randomization AnalysisMyocardial InfarctionGene Expression ProfilingHumansSignal TransductionTumor Necrosis Factor-alphaTumor Necrosis Factor-alpha

Identifiers

PMID39024234
PMCPMC11257238

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