Gene signature discovery and systematic validation across diverse clinical cohorts for TB prognosis and response to treatment.

ABSTRACT: While blood gene signatures have shown promise in tuberculosis (TB) diagnosis and treatment monitoring, most signatures derived from a single cohort may be insufficient to capture TB heterogeneity in populations and individuals. Here we report a new generalized approach combining a network-based meta-analysis with machine-learning modeling to leverage the power of heterogeneity among studies. The transcriptome datasets from 57 studies (37 TB and 20 viral infections) across demographics and TB disease states were used for gene signature discovery and model training and validation. The network-based meta-analysis identified a common 45-gene signature specific to active TB disease across studies. Two optimized random forest regression models, using the full or partial 45-gene signature, were

SUBMITTER: Vargas R 

PROVIDER: S-EPMC10393163 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature