Integrated Bioinformatic Analyses Constructed a Novel Immune Escape-Related Signature and Classifier to Predict Tuberculosis.

Despite its high preventability and curability, tuberculosis (TB) remains a leading cause of morbidity and mortality worldwide. One factor that contributes to the susceptibility and progression of various diseases is immune escape. Therefore, the primary aim of our study was to explore the involvement of immune escape-related genes in the pathogenesis of TB. Two TB datasets retrieved from the gene expression omnibus database were used to identify differentially expressed genes (DEGs). Machine learning was used to identify the hub immune escape-related genes (HIERGs). Weighted gene co-expression network analysis supported and further validated these findings. Subsequently, we scrutinised two distinct subgroups that were determined through the identification of hub immune escape-related genes, and evaluated the distinct function of the subgroups. Our study identified a total of 11 genes related to immune escape in TB. Additionally, six HIERGs were identified through the least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) algorithms. Diagnostic models constructed using HIERGs exhibited high accuracy. Two immune escape-related subclusters were identified in TB samples, which delineated differences in immune infiltration cells with the distinct TB subgroups. The heightened expression of six HIERGs serves as a significant risk factor for TB. The six HIERGs also contribute towards the development of TB-related diseases. Our findings demonstrate a significant enrichment of immune escape-related gene expression in individuals with TB, suggesting a close relationship between immune escape activity and immune cell abundance. These results underscore the putative role of immune escape in the advancement of TB by disrupting or perturbing the immune response.