ArticleFrontiers in pharmacology2022
Effect of M6A regulators on diagnosis, subtype classification, prognosis and novel therapeutic target development of idiopathic pulmonary fibrosis.
Article in Frontiers in pharmacology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 10 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.
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Who cites it
10 citing papers in PubMed, 13 citations in OpenAlex.
- mExperimental and therapeutic medicine · 2026Review
- METTL3 Promotes Lipid Deposition and Pulmonary Fibrosis by Destabilizing PLIN2 in a m6A-Dependent Manner.Journal of cellular and molecular medicine · 2026Article
- RNA Modifications as Molecular Regulators of Alveolar Epithelial Injury and Aberrant Repair in Pulmonary Fibrosis.Biomolecules · 2026Review
- Epigenetic regulatory mechanisms and translational applications in idiopathic pulmonary fibrosis.American journal of clinical and experimental immunology · 2026Review
- METTL14-mediated mEpigenetics · 2025Article
- A novel mechanism of radiation-induced lung injury: METTL3 mediates fibrogenesis through N6-methyladenosine modification of YY1.Journal of translational medicine · 2025Article
- Predictive models and WTAP targeting for idiopathic pulmonary fibrosis (IPF).Scientific reports · 2025Article
- m6A RNA modification pathway: orchestrating fibrotic mechanisms across multiple organs.Briefings in functional genomics · 2025Review
- RNA modifications in pulmonary diseases.MedComm · 2024Review
- Article
Corrections and comments
- Erratum issued
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Molecular biology studies show that RNA N6-methyladenosine (m6A) modifications may take part in the incidence and development of idiopathic pulmonary fibrosis (IPF). Nonetheless, the roles of m6A regulators in IPF are not fully demonstrated. In this study, 12 significant m6A regulators were filtered out between healthy controls and IPF patients using GSE33566 dataset. Random forest algorithm was used to identify 11 candidate m6A regulators to predict the incidence of IPF. The 11 candidate m6A regulators included leucine-rich PPR motif-containing protein (LRPPRC), methyltransferase-like protein 3, FTO alpha-ketoglutarate dependent dioxygenase (FTO), methyltransferase-like 14/16, zinc finger CCCH domain-containing protein 13, protein virilizer homolog, Cbl proto-oncogene like 1, fragile X messenger ribonucleoprotein 1 and YTH domain containing 1/2. A nomogram model was constructed based on 11 candidate m6A regulators and considered beneficial to IPF patients using decision curve analysis. Consensus clustering method was used to distinctly divide IPF patients into two m6A patterns (clusterA and clusterB) based on 12 significant m6A regulators. M6A scores of all IPF patients were obtained using principal component analysis to quantify the m6A patterns. Patients in clusterB had higher m6A scores than those in clusterA. Furthermore, patients in clusterB were correlated with Th17 and Treg cell infiltration, innate immunity and Th1 immunity, while those in clusterA were correlated with adaptive immunity and Th2 immunity. Patients in clusterB also had higher expressions of mesenchymal markers and regulatory factors of fibrosis but lower expressions of epithelial markers. Lastly and interestingly, two m6A regulators, LRPPRC (
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