Identification of Potential Therapeutic Targets for Myopic Choroidal Neovascularization via Discovery-Driven Data Mining

Abstract

Purpose: Myopic choroidal neovascularization (mCNV) is a prevalent cause of vision loss. However, the development of effective therapeutic targets for mCNV has been hindered by the paucity of suitable animal models. Therefore, the aim of this study is to identify potential genes and pathways associated with mCNV and to unearth prospective therapeutic targets that can be utilized to devise efficacious treatments. Methods: Text data mining was used to identify genes linked to choroid, neovascularization, and myopia. g: Profiler was utilized to analyze the biological processes of gene ontology and the Reactome pathways. Protein interaction network analysis was performed using strings and visualized in Cytoscape. MCODE and cytoHubba were used for further screening. Results: Discovery-driven text data mining identified 55 potential genes related to choroid, neovascularization, and myopia. Gene enrichment analysis revealed 11 biological processes and seven Reactome pathways. A protein-protein interaction network with 47 nodes was constructed and analyzed using centrality ranking. Key clusters were identified through algorithm tools. Finally, 14 genes (IL6, FGF2, MMP9, IL10, TNF, MMP2, HGF, MMP3, IGF1, CCL2, CTNNB1, BDNF, NGF, and EDN1), in addition to VEGFA, were evaluated as targets with potential as future therapeutics. Conclusions: This study provides new potential therapeutic targets for mCNV, including IL6, FGF2, MMP9, IL10, TNF, MMP2, HGF, MMP3, IGF1, CCL2, CTNNB1, BDNF, NGF, and EDN1, which correspond to seven potential enriched pathways. These findings provide a basis for further research and offer new possibilities for developing therapeutic interventions for this condition.