Invest Ophthalmol Vis Sci. 2026 May 1;67(5):4. doi: 10.1167/iovs.67.5.4.
ABSTRACT
PURPOSE: Vision-threatening ocular diseases are impacted by aging-associated molecular changes, including mitochondrial dysfunction, cellular senescence, and chronic inflammation. Anti-VEGF therapies targeting VEGF-A/VEGFR2 signaling remain the frontline standard of care, but many patients exhibit suboptimal or nondurable responses, often due to compensatory and/or compromised antiangiogenic and anti-inflammatory pathways. We aimed to elucidate shared mechanisms underlying treatment failure and disease progression.
METHODS: We applied an integrative systems biology framework that combined multiomics datasets, network-based machine learning, and disease-specific pathway mapping. A comprehensive literature review of conditions, including diabetic retinopathy, age-related macular degeneration, retinitis pigmentosa, glaucoma, and aging, identified 14 core genes consistently associated with angiogenesis, inflammation, and immune signaling. Multialgorithm centrality and enrichment analyses reconstructed disease-specific interaction networks, revealing consensus mechanistic axes. Integration of cell-type-specific single-cell RNA sequencing data from AMD-RPE clusters identified cluster-specific gene hubs and vertical signaling axes, leading to VEGF blockade failure.
RESULTS: EGFR, HSP90AA1, SIRT1, and STAT3 emerged as central resistance hubs linking angiogenesis and inflammatory processes. Pathway enrichment analyses revealed 21 conserved core signaling cascades, grouped into six functional categories, with AGE-RAGE, PI3K-Akt, HIF-1, MAPK, and chemokine pathways playing central roles. A MiRGD-based peptide nanocomplex delivering htsFLT01 achieved efficient RPE transfection and controlled gene activation under basal conditions.
CONCLUSIONS: This systems-level framework clarifies mechanisms of VEGF blockade resistance and provides a rational basis for next-generation, combinatorial therapeutic strategies requiring validation in disease-relevant models.
PMID:42080790 | DOI:10.1167/iovs.67.5.4