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.

Keywords:

• tuberculosis
• latent tuberculosis infection
• mycobacterium tuberculosis
• immune
• diagnostic model

Data Sharing Statement

The dataset supporting the conclusions of this article is included within the article.

Ethics Approval and Consent to Participate

All participating individuals provided written informed consent. The study was approved by the Ethics Committee of Hebei Chest Hospital (2022084).

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Disclosure

The authors declare that they have no competing interests in this work.

Additional information

Funding

This work was supported by Clinical Medical Talent Training Program (361013).