ORIGINAL RESEARCH article
Front. Immunol.
Sec. Molecular Innate Immunity
Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1599667
Identification and validation of NETs-related biomarkers in active tuberculosis through bioinformatics analysis and machine learning algorithms
Provisionally accepted
- Public Health and Clinical Center of Chengdu, Chengdu, China
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Diagnostic delays in tuberculosis (TB) pose a significant threat to global TB control.It is of utmost necessity to detect active tuberculosis (ATB) at an early stage. This study explores the role of neutrophil extracellular traps (NETs) in TB immunity, aiming to identify NETs-related genes as potential biomarkers for ATB and LTBI diagnosis. Using three Gene Expression Omnibus (GEO) datasets (GSE19491, GSE62525, GSE28623), we identified 88 differentially expressed NETs-related genes (DE-NRGs) via expression profiling. Three machine learning algorithmssupport vector machine recursive feature elimination (SVM-RFE), least absolute shrinkage and selection operator regression (LASSO), and random forest (RF)-identified three hub genes(CD274, IRF1, HPSE) with high diagnostic value (AUC > 0.75), indicating that these genes can serve as potential biomarkers for differentiating ATB from LTBI.They are of great clinical significance, especially for the early screening and intervention of high-risk populations such as household contacts of TB patients.Immune cell infiltration analysis revealed the three hub genes were significantly correlated with the infiltration states of multiple immune cell types. In addition, through regulatory network analysis, FOXC1, GATA2, and hsa-miR-106a-5p were considered the core regulatory factors for the expression of the three hub genes.Finally, 46 potential gene-targeted drugs were screened, including heparin (hepcidin inhibitor) and PD-1 inhibitors, which demonstrated preliminary efficacy in preclinical studies. These findings offer novel insights and research directions for future studies on the pathological mechanisms, early diagnosis, and targeted therapy of ATB.Meanwhile, they also provide theoretical support for the precise prevention and control strategies for high-risk groups.
Keywords: active tuberculosis (ATB), Latent tuberculosis infection (LTBI), diagnosis, neutrophil extracellular traps (NETs), machine learning
Received: 25 Mar 2025; Accepted: 30 May 2025.
Copyright: © 2025 Xia, An, Lin, Tu, Chen and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Zhu Chen, Public Health and Clinical Center of Chengdu, Chengdu, China
Dongmei Wang, Public Health and Clinical Center of Chengdu, Chengdu, China
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