A diagnostic model for distinguishing between active tuberculosis and latent tuberculosis infection based on the blood expression profiles of autophagy-related genes

Abstract

Background: Autophagy is closely involved in the control of mycobacterial infection. Objectives: Here, a diagnostic model was developed using the levels of autophagy-related genes (ARGs) in the blood to differentiate active tuberculosis (ATB) and latent tuberculosis infection (LTBI). Design: Secondary data analysis of three prospective cohorts. Methods: The expression of ARGs in patients with ATB and LTBI were analyzed using the GSE37250, GSE19491, and GSE28623 datasets from the GEO database. Results: Twenty-two differentially expressed ARGs were identified in the training dataset GSE37250. Using least absolute shrinkage and selection operator and multivariate logistic regression, three ARGs ( FOXO1, CCL2 , and ITGA3 ) were found that were positively associated with adaptive immune-related lymphocytes and negatively associated with myeloid and inflammatory cells. A nomogram was constructed using the three ARGs. The accuracy, consistency, and clinical relevance of the nomogram were evaluated using receiver operating characteristic curves, the C-index, calibration curves, and validation in the datasets GSE19491 and GSE28623. The nomogram showed good predictive performance. Conclusion: The nomogram was able to accurately differentiate between ATB and LTBI patients. These findings provide evidence for future study on the pathology of autophagy in tuberculosis infection.