A 10-gene biosignature of tuberculosis treatment monitoring and treatment outcome prediction. (December 2021) - Long, Nguyen Phuoc; Phat, Nguyen Ky; Yen, Nguyen Thi Hai; Park, Seongoh; Park, Yumi; Cho, Yong-Soon; Shin, Jae-Gook
Abstract: The clinical utility of blood transcriptomic biosignatures for the treatment monitoring and outcome prediction of tuberculosis (TB) remains limited. In this study, we aimed to discover and validate biomarkers for pulmonary TB treatment monitoring and outcome prediction based on kinetic responses of gene expression during treatment. In particular, differentially expressed genes (DEGs) were identified by time-series comparison. Subsequently, DEGs with the monotonic expression alterations during the treatment were selected. Ten consistently down-regulated genes ( CD274, KIF1B, IL15, TLR1, TLR5, FCGR1A, GBP1, NOD2, GBP2, EGF ) exhibited significant potential in treatment monitoring, demonstrated via biological and technical validation. Additionally, the biosignature showed potential in predicting the cured versus relapsed patients. Furthermore, the biosignature could be utilized for TB diagnosis, latent tuberculosis infection/active TB differential diagnosis, and risk of progression to active TB. Benchmarking analysis of the 10-gene biosignature with other biosignatures showed equivalent performance in tested data sets. In conclusion, we established a 10-gene transcriptomic biosignature that represents the kinetic responses of TB treatment. Subsequent studies are warranted to validate, refine and translate the biosignature into a precise assay to assist clinical decisions in a broad spectrum of TB management. Highlights: A more robust transcriptomic signature could enhance tuberculosis management. Novel biomarkers were discovered using the kinetic response of host transcriptome. The knowledge-based selection led to a signature with better performance. A 10-gene signature could be used for treatment monitoring and outcome prediction. The signature could also be applied for tuberculosis diagnosis and risk assessment.