Text Mining-Based Drug Discovery and MAPK14, STAT3, VEGFA as Novel Candidate Genes in Post-Tuberculosis Tracheobronchial Stenosis.

Abstract

The purpose of this study was to use bioinformatics techniques to investigate the genetic basis and evaluate medication for individuals with Post-Tuberculosis Tracheobronchial Stenosis (PTTS). Text mining was done to find 63 gene sets that are related to tuberculosis, granulation tissue proliferation, cicatrization, and hypertrophic scars. Using the DAVID and STRING tool to study the functional classification of these genes, revealed that these genes are associated with significant ways for instance the 'MAPK' signaling cascade, 'JAK-STAT' signaling pathway, and the 'VEGF' signaling pathway. The relevance of the genes was suggested by real-time quantitative PCR, which verified that MAPK14, STAT3, and VEGFA expression was elevated in PTTS tissues. The 14 hub genes were used to search the DGI database for drugs associated with these genes and the corresponding diseases. Out of the identified compounds, 34 of them were found to be possible drug candidates that could target PTTS's underlying pathophysiology. Such drugs include those targeting protein kinase, a cytokine with anti-inflammatory properties, angiogenesis inhibitors, and those targeting fibrosis. By applying this bioinformatics workflow, it was possible to use very little time in identifying genes and drugs that are involved in the main disease processes of PTTS. Future work should then be directed to conducting more experimental validation on these genes and possible drug candidates for future therapeutic application.