Abstract
BACKGROUND: N6-methyladenosine (m(6)A), the most prevalent and reversible post-transcriptional RNA modification, is involved in the progression of various diseases. Nonetheless, the role of m(6)A modification in Tuberculosis (TB) pathogenesis remains unknown. Here, we investigated the general expression patterns and potential functions of m(6)A regulators in TB. METHODS: The differentially expressed m(6)A 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 m(6)A regulators in our collected human TB and healthy samples. Random forest and LASSO regression analysis were performed to determine the prognostic performance of m(6)A regulators in TB patients. The relationship between m(6)A 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 m(6)A regulators induced m(6)A modification patterns. The relationship between m(6)A 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 m(6)A -related genes-METTL3, VIRMA, YTHDF1, YTHDC1, YTHDC2, ELAVL1and LRPPRC mRNA-confirmed as critical m(6)A 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 m(6)A patterns with different immune microenvironment and biological feature. CONCLUSIONS: Our study provides an overview of the expression patterns and potential roles of key m(6)A 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 m(6)A regulators in TB.