Gene co-expression network analysis reveals common system-level properties of genes involved in tuberculosis across independent gene expression studies

Network-based approaches to human disease have diverse biological and clinical applications. A more appropriate interpretation of biological interconnectivity with disease progression could possibly lead to identification of disease pathways and disease genes. This, in turn, helps in accurate prediction of biomarkers and targets for drug development. This study is a large-scale system-level network analysis in tuberculosis to identify co-expressed modules and hub genes involved in active and latent tuberculosis that are preserved across different group studies. Three tuberculosis gene expression data sets containing untreated latent and active tuberculosis samples were analyzed. Weighted gene co-expression network analysis was conducted to detect modules of correlated genes. For validation, module preservation analysis is performed on respective independent data sets. Finally, seven modules were found to be preserved out of which three were significantly associated with latent and active tuberculosis. Last, hub genes within these modules were identified as they may be involved in the initiation or progression of tuberculosis and may, therefore, be targets for further functional studies.