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

Publication date: Dec 02, 2024

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]ā€‰=ā€‰1. 0, pā€‰

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Concepts Keywords
Biomarkers Adenosine
Global Adenosine
Immunopathology Bioinformatics
M6a Biomarkers
Tuberculosis Biomarkers
Gene Expression Profiling
Humans
m6A
Machine Learning
N-methyladenosine
Risk model
Subtyping
Transcriptome
Tuberculosis
Tuberculosis

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