Background: Keloid is a common pathological scar tissue, which invades the surrounding normal skin and leads to symptoms such as pain, pruritus, erythema, and edema, thereby impacting the quality of life. In this study, we conducted bioinformatics analysis of keloid fibroblasts and normal skin tissue to identify DEGs and the pathways involved in the mechanism of keloid fibroblast proliferation. Methods: GSE145725 was downloaded from the Gene Expression Omnibus (GEO) database, including nine keloid fibroblasts and 10 normal tissue fibroblast samples. GSE158395 included four lesional and three nonlesional samples from keloid patients, and six normal skin tissue samples were also evaluated. Through bioinformatics analysis, we established diagnosis model, and at the same time, we predicted therapeutic targets in the DSigDB database. Results: Six key genes were screened out by bioinformatics analysis, including BMP4, SPP1, HIF1α, POSTN, WNT5A, and SMAD3. Subsequently, three of these genes (BMP4, POSTN, and WNT5A) were found to be significantly associated with keloids. Paricalcitol and phosphine were identified as potential therapeutic candidates. Conclusions: This study identified three hub genes—BMP4, POSTN, and WNT5A—that are closely linked to keloid fibroblast hyperplasia and may serve as potential biomarkers for inhibiting keloid fibroblast hyperplasia. Further molecular and animal studies are needed to fully understand the mechanisms of keloid development.
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