Construction of a Diagnosis Model for Tuberculosis based on Long Non-coding RNA

Tuberculosis (TB) remains a significant global public health threat, this study aimed to systematically identify potential long non-coding RNAs (lncRNAs) as biomarkers and construct a diagnostic model for TB. The study revealed the lncRNA expression profile associated with TB in peripheral blood mononuclear cells (PBMCs) through microarray analysis and identified key modules and lncRNAs using weighted gene co-expression network analysis (WGCNA). The expression levels of candidate lncRNAs were validated by real-time quantitative PCR (RT-qPCR), and characteristic variables were selected using the least absolute shrinkage and selection operator (LASSO) regression. The findings suggest that a diagnostic model based on five host-derived lncRNAs combined with the AdaBoost algorithm can serve as a potential tool for TB diagnosis. Genome-wide lncRNA profiling of peripheral blood mononuclear cells (PBMCs) from tuberculosis (TB) patients, latent TB infection (LTBI) individuals, and healthy controls (HCs) was conducted using the Agilent microarray technique. Eight independent samples were included in each group (TB, LTBI and HCs).