Identification and validation of NETs-related biomarkers in active tuberculosis through bioinformatics analysis and machine learning algorithms

Introduction Diagnostic delays in tuberculosis (TB) threaten global control efforts, necessitating early detection of active TB (ATB). This study explores neutrophil extracellular traps (NETs) as key mediators of TB immunopathology to identify NETs-related biomarkers for differentiating ATB from latent TB infection (LTBI). Methods We analyzed transcriptomic datasets (GSE19491, GSE62525, GSE28623) using differential expression analysis (|log, FC| ≥ 0.585, adj. p Results We identified three hub genes (CD274, IRF1, HPSE) showing high diagnostic accuracy (AUC 0.865-0.98, sensitivity/specificity >80%) validated through ROC/precision-recall curves. IRF1 and HPSE correlated with neutrophil infiltration (r > 0.6, p Discussion CD274, IRF1, and HPSE represent promising NETs-derived diagnostic biomarkers for ATB. Their dual roles in neutrophil-mediated immunity highlight therapeutic potential, though drug predictions require preclinical validation. Future studies should leverage spatial omics and CRISPR screening to elucidate mechanistic pathways.