ArticleFrontiers in genetics2022
Prognostic analysis of m6A-related genes as potential biomarkers in idiopathic pulmonary fibrosis.
Article in Frontiers in genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 9 citations in OpenAlex.
- The mClinical epigenetics · 2026Article
- Integrative Transcriptomic and Machine Learning Analysis Identifies JAG1 and NR2F2 in Keloid.Clinical, cosmetic and investigational dermatology · 2026Article
- METTL14-mediated mEpigenetics · 2025Article
- m6A Methylation-Modified hsa_circ_0044226 Attenuates Pulmonary Fibrosis by Suppressing Ferroptosis.Pulmonary circulation · 2025Article
- m6A RNA modification pathway: orchestrating fibrotic mechanisms across multiple organs.Briefings in functional genomics · 2025Review
- m6A-related genes of peripheral white blood cell in spinal cord injury as potential targets for prognosis and treatment.Frontiers in medicine · 2025Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Idiopathic pulmonary fibrosis (IPF) is a progressive, fatal lung disease with limited treatment options. N6-methyladenosine (m6A) is a reversible RNA modification and has been implicated in various biological processes. However, there are few studies on m6A in IPF. This project mainly explores the prognostic value of m6A-related genes as potential biomarkers in IPF, in order to establish a set of accurate prognostic prediction model. In this study, we used GSE28042 dataset in GEO database to screen out 218 m6A-related candidate genes with high IPF correlation and high differential expression through differentially expressed gene analysis, WGCNA and m6A correlation analysis. The genes associated with the prognosis of IPF were screened out by univariate Cox regression analysis, LASSO analysis, and multivariate Cox regression analysis, and the multivariate Cox model of prognostic risk of related genes was constructed. We found that RBM11, RBM47, RIC3, TRAF5 and ZNF14 were key genes in our model. Finally, the prognostic prediction ability and independent prognostic characteristics of the risk model were evaluated by survival analysis and independent prognostic analysis, and verified by the GSE93606 dataset, which proved that the prognostic risk model we constructed has a strong and stable prediction efficiency.
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