Identifying key genes, miRNAs, and pathways in keloid formation: A bioinformatics and experimental study

Abstract

Keloids represent a challenging clinical problem because of their unpredictable and often refractory nature to treatment. This study aimed to identify the key changes in gene expression in the formation of keloid and provide potential biomarker candidates for clinical treatment and drug target discovery. Keloids and normal skin samples were analyzed for gene expression, and datasets from the Gene Expression Omnibus database were also analyzed. Differentially expressed genes (DEGs) were identified and analyzed using bioinformatics techniques, including gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. A protein–protein interaction network of the DEGs was created using the Search Tool for the Retrieval of Interacting Genes database. The gene set enrichment analysis was performed on keloid and normal skin tissue from clinical samples. The enriched functions and pathways identified included collagen-containing extracellular matrix (ECM), ECM, and external encapsulating structure. Ten hub genes were identified, along with one differentially expressed microRNA, miR-22–5p. miRNA target gene prediction was performed using miRPathDB 2.0 and Targetscan database. Among the hub genes, RUNX2, IGF1, EGF, and PPARGC1A were predicted targets of miR-22–5p. Validation at the tissue level highlighted RUNX2 as a crucial DEG in keloid tissue. These findings shed light on the molecular mechanisms of keloid formation and offer candidate therapeutic targets, suggesting that modulation of the miR-22–5p/RUNX2 axis may be a promising avenue for keloid diagnosis and treatment, thus laying a foundation for improved clinical management of keloid disorders.