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

ABSTRACT:

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 duri

SUBMITTER: Sun Y 

PROVIDER: S-EPMC7585184 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature