Identification of novel therapeutic targets for Fuchs' endothelial corneal dystrophy based on gene bioinformatics analysis

Abstract

Fuchs' endothelial corneal dystrophy (FECD) is a disease where progressive visual impairment occurs by the thickening of the Descemet's membrane and the gradual degeneration and loss of corneal endothelial cells. This study aimed to investigate the key changes in gene expression associated with FECD and explore potential biomarkers and new therapeutic strategies for FECD. To explore the potential therapeutic targets of FECD, we downloaded the gene expression dataset GSE171830 from the Gene Expression Omnibus (GEO) database. A total of 303 differentially expressed genes (DEGs) were identified by the limma package. The enriched Gene Ontology (GO) annotations and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of DEGs mostly included the extracellular matrix organization, collagen-containing extracellular matrix, and the structural constituents of the extracellular matrix. Fifteen hub genes from the most significant module were ascertained by Cytoscape. Both collagen-containing extracellular matrix and extracellular matrix hit to ANXA1, VCAN, GPC3, TNC, IGFBP7, MATN3, and SPARCL1 genes in the GO cellular components. Among these genes, the expression of SPARCL1 was down-regulated in the FECD samples, whereas the expression of GPC3, MATN3, IGFBP7, TNC, VCAN, and ANXA1 was up-regulated in the FECD samples. Gene set enrichment analysis (GSEA) plots showed that among the 20,937 genes, SPARCL1 played an important role in three pathways, cytokine-cytokine receptor interaction, the TGF-beta signaling pathway, and antigen processing and presentation. The top three pathways enriched by the GPC3, MATN3, IGFBP7, TNC, VCAN, and ANXA1 genes were those for cytokine-cytokine receptor interaction, TGF-beta signaling, and RIG-I-like receptor signaling. In conclusion, the DEGs identified here might assist clinicians in understanding the pathogenesis of FECD. Furthermore, these identified biomarkers might serve as potential therapeutic targets for the treatment of FECD.

Fig 1

(A) Pearson’s correlation analysis of GSE171830 data set samples. The color reflects the strength of the correlation. When 0

Fig 2. Differentially expressed gene analysis of GSE171830 datasets.

The volcanic map of differential expression. In the GSE171830 data set, we identified 303 differentially expressed genes, including 257 upregulated and 46 downregulated genes.

Fig 3. The heat map of the up-regulated genes and the down-regulated genes, blue indicates a relatively low expression and red indicates a relatively high expression.

Fig 4. Biological Processes (BP) of the Gene Ontology (GO) analysis of upregulated and downregulated diferentially expressed genes.

Fig 5. Cellular Component (CC) of the Gene Ontology (GO) analysis of upregulated and downregulated diferentially expressed genes.

Fig 6. Molecular Functions (MF) of the Gene Ontology (GO) analysis of upregulated and downregulated diferentially expressed genes.

Fig 7. The KEGG pathway analysis of up-regulated and down-regulated diferentially expressed genes.

Only enriched the top 10 signaling pathways, each of which had significant differences.

Fig 8. The most significant module from the protein-protein interaction network.

Fig 9

(A) The hub genes were identified from the PPI network. (B) Hierarchical clustering demonstrated that the hub genes could effectively differentiate the Fuchs’ endothelial corneal dystrophy (FECD) samples from the unaffected group samples in the GSE171830 datasets. The upregulated genes are marked in blue, the downregulated genes are marked in red. (C) The GO enrichment analyses of DEGs of hub genes.

Fig 10. GAES analysis of SPARCL1, GPC3, MATN3, IGFBP7, TNC, VCAN, and ANXA1.

A,B, and C represent the top three pathway of function enrichment about SPARCL1 gene, cytokine-cytokine receptor interaction, TGF-beta signaling, and antigen processing and presentation, respectively. D,E, and F represent the top three pathway about function enrichment of GPC3, MATN3, IGFBP7, TNC, VCAN, and ANXA1, combining these genes, the top three enrichment pathways were those for cytokine-cytokine receptor interaction, TGF-beta signaling, and RIG-I-like receptor signaling, respectively.