Abstract
Whole-blood gene expression has the potential to become an affordable alternative for the diagnosis of pulmonary tuberculosis (TB). Through an immune-based gene expression profile via the NanoString platform, we previously identified 12 genes from whole blood that distinguished active TB from other pulmonary diseases. Here, we performed a three-phase diagnostic test evaluation to determine the scientific validity and analytical and clinical performance of these 12 previously identified genes in accurately diagnosing active TB. We further employed binary classification machine learning models to assess and evaluate transcriptional signatures. Samples from 175 adults were included in the study. During phase one, we assessed the expression of 12 genes in 10 TB patients and 9 healthy controls. Elevated expression of FCGR1A, GBP5, PDCD1LG2, and IRAK3 was observed in TB patients compared with healthy controls (p value 77%, specificity > 94%). The present analysis allowed the discovery of RT‒qPCR-based signatures for TB diagnosis. These signatures could serve as affordable alternatives for TB diagnosis at primary care centers.