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Updated: Jan 31, 2026

A Method for Measuring RNA N6-methyladenosine Modifications in Cells and Tissues
Published on: December 5, 2016
Expression and clinical value of key m6A RNA modification regulators in tuberculosis
Hongfei Du1, Hang Wang2, Yan Yang1
1Department of Clinical Laboratory, The First Affiliated Hospital of Chengdu Medical College,School of Clinical Medicine,Chengdu Medical College, Chengdu, Sichuan, China.
Background:
N6-methyladenosine (m6A), the most prevalent and reversible post-transcriptional RNA modification, is involved in the progression of various diseases. Nonetheless, the role of m6A modification in Tuberculosis (TB) pathogenesis remains unknown. Here, we investigated the general expression patterns and potential functions of m6A regulators in TB.
Methods:
The differentially expressed m6A genes between the healthy and TB groups were evaluated using the public Gene Expression Omnibus (GEO) database, and quantitative real-time PCR (qRT-PCR) was used to test the expression of key m6A regulators in our collected human TB and healthy samples. Random forest and LASSO regression analysis were performed to determine the prognostic performance of m6A regulators in TB patients. The relationship between m6A regulators and immune cells and immune reaction activity was analyzed through single-sample gene set enrichment analysis (ssGSEA). Unsupervised clustering was used to confirm that m6A regulators induced m6A modification patterns. The relationship between m6A modification patterns and the immune microenvironment, biological function, and TB subtype construction was evaluated by using Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO) analysis and KEGG pathway analysis.
Results:
Our data revealed seven differentially expressed m6A -related genes-METTL3, VIRMA, YTHDF1, YTHDC1, YTHDC2, ELAVL1and LRPPRC mRNA-confirmed as critical m6A regulators in TB. The excellent diagnostic significance of these genes was further supported by the random forest, LASSO regression and clinical samples, which achieved a high area under the ROC (0.97). Unsupervised clustering classified patients into two m6A patterns with different immune microenvironment and biological feature.
Conclusions:
Our study provides an overview of the expression patterns and potential roles of key m6A regulatory genes as diagnostic biomarkers and immunotherapy targets for TB, revealing their functions in TB pathogenesis. Our data may offer a valuable resource to guide both mechanistic and therapeutic analyses of key m6A regulators in TB.
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