Gene expression profiling identifies candidate biomarkers for active and latent tuberculosis

• Published in: BMC bioinformatics 2016-01, Vol.17 Suppl 1 (2), p.3-3, Article S3
• Main Authors: Lee, Shih-Wei, Wu, Lawrence Shih-Hsin, Huang, Guan-Mau, Huang, Kai-Yao, Lee, Tzong-Yi, Weng, Julia Tzu-Ya
• Format: Article
• Language: English
• Subjects: Bayes Theorem; Biological markers; Biomarkers - analysis; Care and treatment; Case-Control Studies; Complications and side effects; Development and progression; Gene expression; Gene Expression Profiling - methods; Humans; Latent Tuberculosis - diagnosis; Latent Tuberculosis - genetics; Latent Tuberculosis - microbiology; Leukocytes, Mononuclear - metabolism; Microarray Analysis; Mycobacterium tuberculosis - genetics; Proceedings; Reproducibility of Results; ROC Curve; Transcriptome; Tuberculosis; Tuberculosis - diagnosis; Tuberculosis - genetics; Tuberculosis - microbiology
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-015-0848-x

Tuberculosis (TB) is a serious infectious disease in that 90% of those latently infected with Mycobacterium tuberculosis present no symptoms, but possess a 10% lifetime chance of developing active TB. To prevent the spread of the disease, early diagnosis is crucial. However, current methods of detection require improvement in sensitivity, efficiency or specificity. In the present study, we conducted a microarray experiment, comparing the gene expression profiles in the peripheral blood mononuclear cells among individuals with active TB, latent infection, and healthy conditions in a Taiwanese population. Bioinformatics analysis revealed that most of the differentially expressed genes belonged to immune responses, inflammation pathways, and cell cycle control. Subsequent RT-PCR validation identified four differentially expressed genes, NEMF, ASUN, DHX29, and PTPRC, as potential biomarkers for the detection of active and latent TB infections. Receiver operating characteristic analysis showed that the expression level of PTPRC may discriminate active TB patients from healthy individuals, while ASUN could differentiate between the latent state of TB infection and healthy condidtion. In contrast, DHX29 may be used to identify latently infected individuals among active TB patients or healthy individuals. To test the concept of using these biomarkers as diagnostic support, we constructed classification models using these candidate biomarkers and found the Naïve Bayes-based model built with ASUN, DHX29, and PTPRC to yield the best performance. Our study demonstrated that gene expression profiles in the blood can be used to identify not only active TB patients, but also to differentiate latently infected patients from their healthy counterparts. Validation of the constructed computational model in a larger sample size would confirm the reliability of the biomarkers and facilitate the development of a cost-effective and sensitive molecular diagnostic platform for TB.