Identification potential biomarkers in pulmonary tuberculosis and latent infection based on bioinformatics analysis.

BMC Infect Dis

Nanlou Respiratory Diseases Department, Chinese PLA General Hospital, No. 28, Fuxing Road, Beijing, 100853, China.

Published: September 2016

AI Article Synopsis

  • The study focused on identifying potential biomarkers for pulmonary tuberculosis (TB) and latent TB infections using bioinformatics analysis of microarray data.
  • A total of 1084 differentially expressed genes (DEGs) were found, with specific down-regulated (like RIC8A and BATF) and up-regulated genes (such as ATP1A4 and TYK2) highlighted as significant for further analysis.
  • These genes were related to processes like osteoblast differentiation and were determined to have high positive correlations, suggesting they may serve as biomarkers for TB and latent TB infections.

Article Abstract

Background: The study aimed to identify the potential biomarkers in pulmonary tuberculosis (TB) and TB latent infection based on bioinformatics analysis.

Methods: The microarray data of GSE57736 were downloaded from Gene Expression Omnibus database. A total of 7 pulmonary TB and 8 latent infection samples were used to identify the differentially expressed genes (DEGs). The protein-protein interaction (PPI) network was constructed by Cytoscape software. Then network-based neighborhood scoring analysis was performed to identify the important genes. Furthermore, the functional enrichment analysis, correlation analysis and logistic regression analysis for the identified important genes were performed.

Results: A total of 1084 DEGs were identified, including 565 down- and 519 up-regulated genes. The PPI network was constructed with 446 nodes and 768 edges. Down-regulated genes RIC8 guanine nucleotide exchange factor A (RIC8A), basic leucine zipper transcription factor, ATF-like (BATF) and microtubule associated monooxygenase, calponin LIM domain containing 1 (MICAL1) and up-regulated genes ATPase, Na+/K+ transporting, alpha 4 polypeptide (ATP1A4), histone cluster 1, H3c (HIST1H3C), histone cluster 2, H3d (HIST2H3D), histone cluster 1, H3e (HIST1H3E) and tyrosine kinase 2 (TYK2) were selected as important genes in network-based neighborhood scoring analysis. The functional enrichment analysis results showed that these important DEGs were mainly enriched in regulation of osteoblast differentiation and nucleoside triphosphate biosynthetic process. The gene pairs RIC8A-ATP1A4, HIST1H3C-HIST2H3D, HIST1H3E-BATF and MICAL1-TYK2 were identified with high positive correlations. Besides, these genes were selected as significant feature genes in logistic regression analysis.

Conclusions: The genes such as RIC8A, ATP1A4, HIST1H3C, HIST2H3D, HIST1H3E, BATF, MICAL1 and TYK2 may be potential biomarkers in pulmonary TB or TB latent infection.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5031349PMC
http://dx.doi.org/10.1186/s12879-016-1822-6DOI Listing

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