Decoding differentially expressed genes to identify potential immunity associated biomarkers in Tuberculosis: An integrative bioinformatics approach

Tuberculosis (TB) poses a significant threat to the Indian population, with India accounting for 20 % of the global TB cases. The current study aims to identify molecular biomarkers for better diagnostics by comparing the transcriptome signatures of healthy individuals against TB-affected individuals. Next-generation sequencing (NGS) tools were used to identify critical differentially expressed genes (DEGs). 302 DEGs were identified based on a logFC threshold of |3| and adjusted p-value < 0.05. STRING database was used to plot the interactions amongst the 302 DEGs. The DEGs were functionally annotated, highlighting numerous physiological functions affected due to the dysregulation of the identified hub genes. TLR4 , FCGR1A , ITGAM , LTF, and CXCR2 were the hub genes identified and observed to dysregulate crucial physiological functions. TLR4 has been implicated in the progression of TB in various populations, and the findings of this study will enable researchers to improve the current landscape of diagnostics for TB. • Identification of aberrantly expressed genes from transcriptomic signatures in TB-affected individuals. • Hub-gene interaction analysis reveals TLR4 gene as a potential biomarker. • Functional annotation reveals various dysregulated physiological functions due to the aberrant expression of the TLR4 gene.