Several studies showed that assessing levels of specific circulating microRNAs (miRNAs) is a non-invasive, rapid, and accurate method for diagnosing diseases or detecting alterations in physiological conditions. We aimed to identify a serum miRNA signature to be used for the diagnosis of tuberculosis (TB). To account for variations due to the genetic makeup, we enrolled adults from two study settings in Europe and Africa. The following categories of subjects were considered: healthy (H), active pulmonary TB (PTB), active pulmonary TB, HIV co-infected (PTB/HIV), latent TB infection (LTBI), other pulmonary infections (OPI), and active extra-pulmonary TB (EPTB). Sera from 10 subjects of the same category were pooled and, after total RNA extraction, screened for miRNA levels by TaqMan low-density arrays. After identification of "relevant miRNAs", we refined the serum miRNA signature discriminating between H and PTB on individual subjects. Signatures were analyzed for their diagnostic performances using a multivariate logistic model and a Relevance Vector Machine (RVM) model. A leave-one-out-cross-validation (LOOCV) approach was adopted for assessing how both models could perform in practice. The analysis on pooled specimens identified selected miRNAs as discriminatory for the categories analyzed. On individual serum samples, we showed that 15 miRNAs serve as signature for H and PTB categories with a diagnostic accuracy of 82% (CI 70.2-90.0), and 77% (CI 64.2-85.9) in a RVM and a logistic classification model, respectively. Considering the different ethnicity, by selecting the specific signature for the European group (10 miRNAs) the diagnostic accuracy increased up to 83% (CI 68.1-92.1), and 81% (65.0-90.3), respectively. The African-specific signature (12 miRNAs) increased the diagnostic accuracy up to 95% (CI 76.4-99.1), and 100% (83.9-100.0), respectively. Serum miRNA signatures represent an interesting source of biomarkers for TB disease with the potential to discriminate between PTB and LTBI, but also among the other categories. © 2013 Miotto et al.
MiRNA signatures in sera of patients with active pulmonary tuberculosis
Matteelli A.;
2013-01-01
Abstract
Several studies showed that assessing levels of specific circulating microRNAs (miRNAs) is a non-invasive, rapid, and accurate method for diagnosing diseases or detecting alterations in physiological conditions. We aimed to identify a serum miRNA signature to be used for the diagnosis of tuberculosis (TB). To account for variations due to the genetic makeup, we enrolled adults from two study settings in Europe and Africa. The following categories of subjects were considered: healthy (H), active pulmonary TB (PTB), active pulmonary TB, HIV co-infected (PTB/HIV), latent TB infection (LTBI), other pulmonary infections (OPI), and active extra-pulmonary TB (EPTB). Sera from 10 subjects of the same category were pooled and, after total RNA extraction, screened for miRNA levels by TaqMan low-density arrays. After identification of "relevant miRNAs", we refined the serum miRNA signature discriminating between H and PTB on individual subjects. Signatures were analyzed for their diagnostic performances using a multivariate logistic model and a Relevance Vector Machine (RVM) model. A leave-one-out-cross-validation (LOOCV) approach was adopted for assessing how both models could perform in practice. The analysis on pooled specimens identified selected miRNAs as discriminatory for the categories analyzed. On individual serum samples, we showed that 15 miRNAs serve as signature for H and PTB categories with a diagnostic accuracy of 82% (CI 70.2-90.0), and 77% (CI 64.2-85.9) in a RVM and a logistic classification model, respectively. Considering the different ethnicity, by selecting the specific signature for the European group (10 miRNAs) the diagnostic accuracy increased up to 83% (CI 68.1-92.1), and 81% (65.0-90.3), respectively. The African-specific signature (12 miRNAs) increased the diagnostic accuracy up to 95% (CI 76.4-99.1), and 100% (83.9-100.0), respectively. Serum miRNA signatures represent an interesting source of biomarkers for TB disease with the potential to discriminate between PTB and LTBI, but also among the other categories. © 2013 Miotto et al.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
