A real-time PCR signature to discriminate between tuberculosis and other pulmonary diseases. Issue 4 (July 2015)

• Record Type: Journal Article
• Authors: Laux da Costa, Lucas; Delcroix, Melaine; Dalla Costa, Elis R.; Prestes, Isaías V.; Milano, Mariana; Francis, Steve S.; Unis, Gisela; Silva, Denise R.; Riley, Lee W.; Rossetti, Maria L.R.
• Is Part Of: Tuberculosis. Volume 95:Issue 4(2015)
• Page Start: 421
• Page End: 425
• Publication Date: 2015-07
• Subjects: 616.995
• DOI: 10.1016/j.tube.2015.04.008
• Languages: English
• ISSNs: 1472-9792

Abstract

<abstract xml:lang="en" abstract-type="author" id="abs0010"> <title id="sectitle0010">Summary</title> <sec> <p id="abspara0010">The goal of this study was to identify a host gene signature that can distinguish tuberculosis (TB) from other pulmonary diseases (OPD). We conducted real-time PCR on whole blood samples from patients in Brazil. TB and OPD patients (asthma and non-TB pneumonia) differentially expressed granzyme A (<italic>GZMA</italic>), guanylate binding protein 5 (<italic>GBP5</italic>) and Fc gamma receptor 1A (<italic>CD64</italic>). Receiver operating characteristic, tree classification and random forest analyses were applied to evaluate the discriminatory power of the three genes and find the gene panel most predictive of patients' disease classification. Tree classification produced a model based on <italic>GBP5</italic> and <italic>CD64</italic> expression. In random forest analysis, the combination of the three genes provided a robust biosignature to distinguish TB from OPD with 95% specificity and 93% sensitivity. Our results suggest that <italic>GBP5</italic> and <italic>CD64</italic> in tandem may be the most predictive combination. However, <italic>GZMA</italic> contribution to the prediction model requires further investigation. Regardless, these three genes show promise as a rapid diagnostic marker separating TB from OPD.</p> </sec> </abstract>