Evidence map›Paper›PMID 40226040›Full record

ArticleAmerican journal of translational research2025

Mechanistic insights into COVID-19 mRNA vaccine-associated myocarditis: a bioinformatics analysis.

Yanjie Jiang, Yaxue Xie, Yingnan You, Bo Han

Abstract read
In one paragraph

Article in American journal of translational research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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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.

Yanjie JiangDepartment of Pediatrics, Shandong Provincial Hospital, Shandong University Jinan, Shandong, China.
Yaxue XieDepartment of Pediatrics, Shandong Provincial Hospital, Shandong University Jinan, Shandong, China.
Yingnan YouDepartment of Pediatrics, Shandong Provincial Hospital, Shandong University Jinan, Shandong, China.
Bo HanDepartment of Pediatrics, Shandong Provincial Hospital, Shandong University Jinan, Shandong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeWhile COVID-19 vaccination offers significant public health benefits, it also has potential risks, such as myocarditis. The mechanisms underlying myocarditis after COVID-19 vaccination remain poorly understood. The purpose of this study was to identify potential pathogenic genes and molecular pathways related with COVID-19 mRNA vaccine-associated myocarditis.

methodsDifferentially expressed genes (DEGs) were analyzed from a fulminant myocarditis (FM) cohort and a COVID-19 mRNA vaccination dataset. Shared DEGs were intersected, followed by functional enrichment, protein-protein interaction (PPI) network construction, and hub gene identification. Transcriptional and miRNA regulatory networks, as well as therapeutic drug predictions were also performed.

resultsEighty shared DEGs were identified by interacting DEGs from the FM cohort and the vaccination cohort, we identified. Functional enrichment analysis revealed that DEGs are significantly involved in immune cell-mediated responses, highlighting the critical role of immune dysregulation. PPI network analysis revealed three hub genes (CXCR3, NKG7, and GZMH), which may be involved in the pathogenesis of vaccine-associated myocarditis. Furthermore, transcriptional networks highlighted TBX21 and STAT4 as key regulators of all hub genes, while hsa-mir-146a-5p targeted CXCR3 and NKG7. PhIP, a compound targeting CXCR3 and NKG7, emerged as a potential therapeutic candidate.

conclusionThis study implicates immune dysregulation driven by CXCR3, NKG7, and GZMH in post-vaccination myocarditis, supported by regulatory networks and therapeutic insights using a bioinformatics analysis. These findings advance mechanistic understanding of this rare adverse event and propose potential treatment strategy for further investigation.

Indexed as

bioinformatics analysisBNT162b2COVID-19 mRNA vaccinationmyocarditisprotein-protein interaction network

Identifiers

PMID40226040
PMCPMC11982872

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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.