A bioinformatics analysis to identify novel biomarkers for prognosis of pulmonary tuberculosis.

BACKGROUND Due to the fact that pulmonary tuberculosis (PTB) is a highly infectious respiratory disease characterized by high herd susceptibility and hard to be treated, this study aimed to search novel effective biomarkers to improve the prognosis and treatment of PTB patients. METHODS Firstly, bioinformatics analysis was performed to identify PTB-related differentially expressed genes (DEGs) from GEO database, which were then subjected to GO annotation and KEGG pathway enrichment analysis to initially describe their functions. Afterwards, clustering analysis was conducted to identify PTB-related gene clusters and relevant PPI networks were established using the STRING database. RESULTS Based on the further differential and clustering analyses, 10 DEGs decreased during PTB development were identified and considered as candidate hub genes. Besides, we retrospectively analyzed some relevant studies and found that 7 genes (CCL20, PTGS2, ICAM1, TIMP1, MMP9, CXCL8 and IL6) presented an intimate correlation with PTB development and had the potential serving as biomarkers. CONCLUSIONS Overall, this study provides a theoretical basis for research on novel biomarkers of PTB, and helps to estimate PTB prognosis as well as probe into targeted molecular treatment.