Molecular Subtype Identification and Potential Drug Prediction Based on Anoikis-Related Genes Expression in Keratoconus.

PURPOSE

Anoikis is a special apoptosis accompanied by the loss of extracellular matrix (ECM) environment and the decomposition of ECM is an important process in the occurrence of keratoconus (KC). This study aims to describe the expression profile of anoikis-related genes (ARGs) in KC samples, identify differentially expressed genes (DEGs), characterize the biological functions and immune characteristics of different molecular subtypes of KC and predict potential drugs based on the construction of a co-expression network.

METHODS

First, we identified molecular subtypes by optimal clustering K based on the expression profile of ARGs in the KC dataset and analyzed the differences of functional and immune characteristics. Then a weighted gene co-expression network was constructed based on cluster analysis to obtain hub genes and protein-protein interaction network was constructed to analyze hub nodes and predict potential node-targeting drugs.

RESULTS

By comparing the expression profile between disease and normal samples, we found that there were significant differences in ARGs such as BCL2, CAV1, and CEACAM5. Consistent cluster analysis identified two definite clusters on the basis of ARGs expression difference. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology enrichment analysis showed that DEGs were enriched significantly in pathways like ECM receptor interaction, chemokine signal, notch signal, focal adhesion, and functional sets like proteolysis, anoikis, regulation of natural killer and T-cell proliferation. CIBERSORT calculation showed that there were significant differences between the two subtypes on immune cell infiltration (monocytes and plasma) and immune molecules (CCL11, CCL14, HLA-A, HLA-B, and so on). Then, co-expression network was constructed based on cluster phenotype, 5202 genes were selected as hub genes, and 321 HubDEGs were obtained after intersection with significant DEGs. Seven hub nodes, EIF4G1, KHSRP, PABPC1, POLR2A, PTBP1, RPS19, and SMARCA4, were identified and matched drugs or small molecular compounds. Insulin and dexamethasone were identified as potential target drugs.

CONCLUSIONS

We revealed the differential expression of ARGs in KC samples, and identified two distinct subtypes that showed significant differences in biological function and immune infiltration. The identification of hub gene nodes elucidated their therapeutic value on predicted potential drugs.