Abstract
BACKGROUND: Tuberculosis (TB) diagnostics urgently require non-sputum biomarkers to address the limitations of conventional methods in distinguishing active TB (ATB) from latent infection (LTBI), healthy controls (HCs), and TB-mimicking diseases (ODs, other diseases). METHODS: Transcriptomic data from GSE83456 and GSE152532 were combined to form the selection dataset. Marker genes were identified from differentially expressed autophagy-related genes using a Random Forest classifier. The optimal gene signature was selected based on optimal performance through a linear Support Vector Machine (SVM) classifier with cross-validation. The signature was subsequently evaluated in six independent evaluation datasets and validated using whole blood samples collected from 70 participants. RESULTS: We identified a novel four-gene autophagy-related signature (CASP1, FAS, TRIM5, C5) in the selection dataset. This signature demonstrated robust diagnostic accuracy across multiple evaluation datasets: Area Under the Curve (AUC) 0.83-0.98 for ATB vs. LTBI and 0.82-0.94 for ATB vs. HCs. Crucially, it maintained high specificity (AUC 0.89-0.90) against ODs. RT-qPCR validation in whole blood samples confirmed elevated expression in ATB, while an SVM model achieved promising differentiation (AUC 0.86 for ATB vs. LTBI and AUC 0.99 for ATB vs. HCs). CONCLUSIONS: Our findings yielded a four-gene signature in whole blood that is robustly diagnostic for ATB, validated across multiple evaluation datasets and clinical samples. The autophagy-driven specificity and PCR-compatible design of this signature offer a blood-based, cost-effective strategy to enhance TB detection, addressing WHO-aligned diagnostic needs.