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2016 ; 16
(1
): 500
Nephropedia Template TP
gab.com Text
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English Wikipedia
Identification potential biomarkers in pulmonary tuberculosis and latent
infection based on bioinformatics analysis
#MMPMID27655333
Qin XB
; Zhang WJ
; Zou L
; Huang PJ
; Sun BJ
BMC Infect Dis
2016[Sep]; 16
(1
): 500
PMID27655333
show ga
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.