Identification of key genes and pathways in corneal neovascularisation: integrating text mining and bioinformatic analysis

Corneal neovascularisation (CNV) is a vision-threatening condition that currently lacks approved treatment. This paper describes a comprehensive bioinformatics analysis to identify key genes and molecular pathways associated with CNV and to identify existing drugs for repurposing. The CNV-associated genes were identified using text-mining platforms. Three prominent web-based bioinformatics tools, such as the Database of Annotation, Visualisation and Integrated Discovery (DAVID), Metascape, and Gene Ontology enrichment analysis and visualisation (GOrilla), were employed to analyse Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomics (KEGG) pathways. A Protein-Protein Interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) online database and analysed in Cytoscape. GeneAnalytics was used to identify genes expressed in the cornea. The module genes/clusters were identified using Molecular Complex Detection (MCODE). The Drug-Gene Interaction database (DGIdb) evaluated drug–gene interactions to identify potential drugs, although these exploratory findings require further validation. From all obtained CNV-associated Homo sapiens genes, 143 genes were selected. MCODE revealed one gene module with 12 nodes and 20 edges. DGIdb analysis of 823 proposed drugs identified the top 20 based on high interaction scores, along with their regulatory approvals, pharmacological activities, anti-angiogenic and anti-inflammatory evidence, ocular relevance, and final prioritisation. The top 20 drugs possess potential for repurposing for CNV management.