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

ABSTRACT: 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 gene

SUBMITTER: Li Z 

PROVIDER: S-EPMC12034850 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature