Biomedical and environmental sciences : BES · 2018
Screening of Serum Biomarkers for Distinguishing between Latent and Active Tuberculosis Using Proteome Microarray.
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Cao Shu Hui, Chen Yan Qing, Sun Yong, Liu Yang, Zheng Su Hua, Zhang Zhi Guo, Li Chuan You
Аннотация
OBJECTIVE: To identify potential serum biomarkers for distinguishing between latent tuberculosis infection (LTBI) and active tuberculosis (TB).
METHODS: A proteome microarray containing 4,262 antigens was used for screening serum biomarkers of 40 serum samples from patients with LTBI and active TB at the systems level. The interaction network and functional classification of differentially expressed antigens were analyzed using STRING 10.0 and the TB database, respectively. Enzyme-linked immunosorbent assays (ELISA) were used to validate candidate antigens further using 279 samples. The diagnostic performances of candidate antigens were evaluated by receiver operating characteristic curve (ROC) analysis. Both antigen combination and logistic regression analysis were used to improve diagnostic ability.
RESULTS: Microarray results showed that levels of 152 Mycobacterium tuberculosis (Mtb)-antigen- specific IgG were significantly higher in active TB patients than in LTBI patients (P < 0.05), and these differentially expressed antigens showed stronger associations with each other and were involved in various biological processes. Eleven candidate antigens were further validated using ELISA and showed consistent results in microarray analysis. ROC analysis showed that antigens Rv2031c, Rv1408, and Rv2421c had higher areas under the curve (AUCs) of 0.8520, 0.8152, and 0.7970, respectively. In addition, both antigen combination and logistic regression analysis improved the diagnostic ability.
CONCLUSION: Several antigens have the potential to serve as serum biomarkers for discrimination between LTBI and active TB.
METHODS: A proteome microarray containing 4,262 antigens was used for screening serum biomarkers of 40 serum samples from patients with LTBI and active TB at the systems level. The interaction network and functional classification of differentially expressed antigens were analyzed using STRING 10.0 and the TB database, respectively. Enzyme-linked immunosorbent assays (ELISA) were used to validate candidate antigens further using 279 samples. The diagnostic performances of candidate antigens were evaluated by receiver operating characteristic curve (ROC) analysis. Both antigen combination and logistic regression analysis were used to improve diagnostic ability.
RESULTS: Microarray results showed that levels of 152 Mycobacterium tuberculosis (Mtb)-antigen- specific IgG were significantly higher in active TB patients than in LTBI patients (P < 0.05), and these differentially expressed antigens showed stronger associations with each other and were involved in various biological processes. Eleven candidate antigens were further validated using ELISA and showed consistent results in microarray analysis. ROC analysis showed that antigens Rv2031c, Rv1408, and Rv2421c had higher areas under the curve (AUCs) of 0.8520, 0.8152, and 0.7970, respectively. In addition, both antigen combination and logistic regression analysis improved the diagnostic ability.
CONCLUSION: Several antigens have the potential to serve as serum biomarkers for discrimination between LTBI and active TB.