title: Identification potential biomarkers in pulmonary tuberculosis and latent infection based on bioinformatics analysis.
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.
authors.affiliation: Nanlou Respiratory Diseases Department, Chinese PLA General Hospital, No. 28, Fuxing Road, Beijing, 100853, China.
authors.forename: Xue-Bing
authors.initials: XB
authors.lastname: Qin
authors.affiliation: Department of Respiratory, Chinese PLA General Hospital, Beijing, 100853, China.|Medical College, Nankai University, Tianjin, 300071, China.
authors.forename: Wei-Jue
authors.initials: WJ
authors.lastname: Zhang
authors.affiliation: Nanlou Health Care Department, Chinese PLA General Hospital, Beijing, 100853, China.
authors.forename: Lin
authors.initials: L
authors.lastname: Zou
authors.affiliation: Nanlou Respiratory Diseases Department, Chinese PLA General Hospital, No. 28, Fuxing Road, Beijing, 100853, China.
authors.forename: Pei-Jia
authors.initials: PJ
authors.lastname: Huang
authors.affiliation: Nanlou Respiratory Diseases Department, Chinese PLA General Hospital, No. 28, Fuxing Road, Beijing, 100853, China. baojunsdsa@163.com.
authors.forename: Bao-Jun
authors.initials: BJ
authors.lastname: Sun
keywords: Bioinformatics analysis; Biomarker; Differentially expressed genes; Mycobacterium tuberculosis; Pulmonary tuberculosis