Multi-cohort analysis reveals immune subtypes and predictive biomarkers in tuberculosis

TL;DR

This study identifies immune subtypes in tuberculosis patients and finds biomarkers that could help predict disease progression and treatment response.

Contribution

The study introduces a multi-cohort analysis method to classify PTB subtypes and predicts progression using a neural network model with biomarker genes.

Findings

Three PTB subtypes (C1, C2, C3) were identified with distinct immune-inflammatory activity and cell infiltration patterns.

A neural network model accurately predicted PTB progression based on biomarker genes.

Females showed a higher risk of disease deterioration compared to males.

Abstract

Tuberculosis (TB) remains a significant global health threat, necessitating effective strategies for diagnosis, prognosis, and treatment. This study employs a multi-cohort analysis approach to unravel the immune microenvironment of TB and delineate distinct subtypes within pulmonary TB (PTB) patients. Leveraging functional gene expression signatures (Fges), we identified three PTB subtypes (C1, C2, and C3) characterized by differential immune-inflammatory activity. These subtypes exhibited unique molecular features, functional disparities, and cell infiltration patterns, suggesting varying disease trajectories and treatment responses. A neural network model was developed to predict PTB progression based on a set of biomarker genes, achieving promising accuracy. Notably, despite both genders being affected by PTB, females exhibited a relatively higher risk of deterioration. Additionally, single-cell analysis provided insights into enhanced major histocompatibility complex (MHC) signaling in the rapid clearance of early pathogens in the C3 subgroup. This comprehensive approach offers valuable insights into PTB pathogenesis, facilitating personalized treatment strategies and precision medicine interventions.

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