ArticleAging2024
Biological significance of METTL5 in atherosclerosis: comprehensive analysis of single-cell and bulk RNA sequencing data.
Article in Aging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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.
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.
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Who cites it
3 citing papers in PubMed, 2 citations in OpenAlex.
- Pathomics-based machine learning models for predicting METTL5 expression and prognosis in lung adenocarcinoma.Journal of thoracic disease · 2026Article
- Research on RNA modification in disease diagnosis and prognostic biomarkers: current status and challenges.Briefings in bioinformatics · 2025Review
- METTL Family in Healthy and Disease.Molecular biomedicine · 2024Review
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundN6-methyladenosine (m6A) methylation is involved in the pathogenesis of atherosclerosis (AS). Limited studies have examined the role of the m6A methyltransferase METTL5 in AS pathogenesis.
methodsThis study subjected the AS dataset to differential analysis and weighted gene co-expression network analysis to identify m6A methylation-associated differentially expressed genes (DEGs). Next, the m6A methylation-related DEGs were subjected to consensus clustering to categorize AS samples into distinct m6A subtypes. Single-cell RNA sequencing (scRNA-seq) analysis was performed to investigate the proportions of each cell type in AS and adjacent healthy tissues and the expression levels of key m6A regulators. The mRNA expression levels of METTL5 in AS and healthy tissues were determined using quantitative real-time polymerase chain reaction (qRT-PCR) analysis.
resultsAS samples were classified into two subtypes based on a five-m6A regulator-based model. scRNA-seq analysis revealed that the proportions of T cells, monocytes, and macrophages in AS tissues were significantly higher than those in healthy tissues. Additionally, the levels of m6A methylation were significantly different between AS and healthy tissues. METTL5 expression was upregulated in macrophages, smooth muscle cells (SMCs), and endothelial cells (ECs). qRT-PCR analysis demonstrated that the METTL5 mRNA level in AS tissues was downregulated when compared with that in healthy tissues.
conclusionsMETTL5 is a potential diagnostic marker for AS subtypes. Macrophages, SMCs, and ECs, which exhibit METTL5 upregulation, may modulate AS progression by regulating m6A methylation levels.
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