Identification of diagnostic biomarkers and molecular subtype analysis associated with m6A in Tuberculosis immunopathology using machine learning

Tuberculosis (TB), ranking just below COVID-19 in global mortality, is a highly complex infectious disease involving intricate immunological molecules, diverse signaling pathways, and multifaceted immune processes. N6-methyladenosine (m6A), a critical epigenetic modification, regulates various immune-metabolic and pathological pathways, though its precise role in TB pathogenesis remains largely unexplored. This study aims to identify m6A-associated genes implicated in TB, elucidate their mechanistic contributions, and evaluate their potential as diagnostic biomarkers and tools for molecular subtyping. Using TB-related datasets from the GEO database, this study identified differentially expressed genes associated with m6A modification. We applied four machine learning algorithms—Random Forest, Support Vector Machine, Extreme Gradient Boosting, and Generalized Linear Model—to construct diagnostic models focusing on m6A regulatory genes. The Random Forest algorithm was selected as the optimal model based on performance metrics (area under the curve [AUC] = 1.0, p