Construction of Immune-Related Diagnostic Model for Latent Tuberculosis Infection and Active Tuberculosis

Abstract

Background: Tuberculosis (TB) is one of the most infectious diseases caused by Mycobacterium tuberculosis (M. tb), and the diagnosis of active tuberculosis (TB) and latent TB infection (LTBI) remains challenging. Methods: Gene expression files were downloaded from the GEO database to identify the differentially expressed genes (DEGs). The ssGSEA algorithm was applied to assess the immunological characteristics of patients with LTBI and TB. Weighted gene co-expression network analysis, protein-protein interaction network, and the cytoHubba plug-in of Cytoscape were used to identify the real hub genes. Finally, a diagnostic model was constructed using real hub genes and validated using a validation set. Results: Macrophages and natural killer cells were identified as important immune cells strongly associated with TB. In total, 726 mRNAs were identified as DEGs. MX1, STAT1, IFIH1, DDX58, and IRF7 were identified as real hub immune-related genes. The diagnostic model generated by the five real hub genes could distinguish active TB from healthy controls or patients with LTBI. Conclusion: Our study may provide implications for the diagnosis and drug development of M. tb infections.