An Integrative Bioinformatics Framework Nominates Candidate Limbal Stem-Cell Exosome Cargo for Keratoconus by Coupling Corneal Transcriptomics, Disease-Gene Evidence and Extracellular-Vesicle Repositories.

Keratoconus is a progressive corneal ectasia characterised by extracellular matrix (ECM) loss and an emerging inflammatory component, for which no disease-modifying molecular therapy exists. Exosomes derived from limbal and mesenchymal stem cells are an attractive cell-free therapeutic modality, but the cargo that should be delivered is undefined, and no curated limbal stem-cell (LSC) exosome cargo dataset currently exists. We reanalysed a public keratoconus corneal RNA-sequencing dataset (GEO: GSE77938; discovery and replication cohorts) with DESeq2, defined a replicated differentially expressed gene (DEG) set, and performed Gene Ontology, KEGG and Reactome enrichment. A high-confidence protein-protein interaction (PPI) network (STRING) identified hub genes. We integrated keratoconus disease-gene evidence (Open Targets Platform) and documented extracellular-vesicle cargo (ExoCarta, Vesiclepedia) and computed a transparent Cargo Prioritization Score (CPS) to nominate candidate LSC-exosome therapeutic cargo. A total of 1677 DEGs were detected in discovery (152 up, 1525 down) and 1380 were replicated. Enrichment was dominated by extracellular matrix organisation; adaptive immune response; and mononuclear cell differentiation. Network analysis nominated ECM and immune hub genes. The CPS prioritised COL1A1, FN1, COL4A1, COL3A1, COL5A1, MMP1 as leading restoration-cargo candidates, all documented as EV cargo and present in the mesenchymal stem-cell EV reference proteome. This fully reproducible, real-data framework provides a ranked, evidence-traceable shortlist of candidate LSC-exosome cargo for keratoconus and an explicit account of current data gaps to guide experimental validation.