Identification of Key Genes and Molecular Pathways in Keratoconus: Integrating Text Mining and Bioinformatics Analysis.

Purpose: To identify the potential key genes and molecular pathways associated with keratoconus and allergic disease. Methods: The pubmed2ensembl database was used to identify the text mining genes (TMGs) collectively involved in keratoconus and allergic disease. The GeneCodis program was used to perform the Gene Ontology (GO) biological process and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of TMGs. The protein-protein interaction (PPI) network of the TMGs was established by STRING; the significant gene modules and hub genes of PPI were further performed using the Cytoscape software. The DAVID database was used to perform the GO and KEGG analyses of the significant module. Results: In total, 98 TMGs collectively involved in keratoconus and allergic disease were identified. 19 enriched biological processes including 71 genes and 25 enriched KEGG pathways including 59 genes were obtained. A TMG PPI network was constructed, and 51 genes/nodes were identified with 110 edges; 3 most significant modules and 12 hub genes were chosen from the PPIs. GO enrichment analysis showed that the TMGs were primarily associated with collagen catabolic process, extracellular matrix organization and disassembly, cell adhesion and migration, collagen-containing extracellular matrix, extracellular matrix, and structure organization. KEGG pathway analysis showed that these DEGs were mainly involved in the IL-17 signaling pathway, inflammatory bowel disease, rheumatoid arthritis, allograft rejection, T cell receptor signaling pathway, cytokine-cytokine receptor interaction, and TNF signaling pathway. Conclusions: The results revealed that