Inhibition of Proliferation, Migration, and Invasion of Keloid Fibroblasts By miR-183-5p Through Downregulating EGR1 / Inhibición de la Proliferación, Migración e Invasión de Fibroblastos Queloides por miR-183-5p Mediante la Regulación Negativa de EGR1

SUMMARY: Keloid scar is a unique benign fibroproliferative tumor of the human skin. Previously, it was reported that early growth response 1 (EGR1), a transcription factor, promotes keloid fibrosis; however, the mechanism by which EGR1 modulates keloid formation was not elaborated. In this research, the specific function and the microRNA (miRNA) regulatory network of EGR1 in keloids was examined. Keloid fibroblasts (KFs) were transfected with EGR1-small interfering RNA (siEGR1), EGR1-overexpression plasmid (pcDNA3.1-EGR1), and microRNA (miR-183-5p)-mimics to regulate the expression of EGR1 and miR-183-5p. The study employed dual-luciferase reporter assays to explore the targeting regulation of miR-183-5p on EGR1. Additionally, Western blotting, flow cytometry, qRT-PCR, cell count kit-8 (CCK-8), transwell, and wound healing assays, and RNA sequencing were conducted. EGR1 was upregulated in KFs, and EGR1 silencing diminished proliferation, fibrosis, migration, invasion, and apoptosis of cells. In KFs, the expression of miR- 183-5p was reduced, leading to the inhibition of cell proliferation, migration, and invasion. Conversely, it enhanced apoptosis. By targeting EGR1, miR-183-5p partially counteracted the impact of EGR1 on migration, invasion, and fibrosis in KFs. The findings imply that miR-183-5p suppresses keloid formation by targeting EGR1. As a result, EGR1 holds promise as a potential therapeutic target for preventing and treating keloids.

La cicatriz queloide es un tumor fibroproliferativo benigno único de la piel humana. Anteriormente, se informó que la respuesta de crecimiento temprano 1 (EGR1), un factor de transcripción, promueve la fibrosis queloide; sin embargo, no se explicó el mecanismo por el cual EGR1 modula la formación de queloides. En esta investigación, se examinó la función específica y la red reguladora de microARN (miARN) de EGR1 en queloides. Se transfectaron fibroblastos queloides (KF) con ARN de interferencia pequeño de EGR1 (siEGR1), plásmido de sobreexpresión de EGR1 (pcDNA3.1-EGR1) y miméticos de microARN (miR-183-5p) para regular la expresión de EGR1 y miR-183. -5p. El estudio empleó ensayos de indicador de luciferasa dual para explorar la regulación dirigida de miR-183-5p en EGR1. Además, se realizaron pruebas de transferencia Western, citometría de flujo, qRT-PCR, kit de recuento celular-8 (CCK-8), transwell y curación de heridas, y secuenciación de ARN. EGR1 estaba regulado positivamente en KF, y el silenciamiento de EGR1 disminuyó la proliferación, fibrosis, migración, invasión y apoptosis de las células. En KF, la expresión de miR- 183-5p se redujo, lo que llevó a la inhibición de la proliferación, migración e invasión celular. Por el contrario, mejoró la apoptosis. Al apuntar a EGR1, miR-183-5p contrarrestó parcialmente el impacto de EGR1 en la migración, invasión y fibrosis en KF. Los hallazgos implican que miR-183-5p suprime la formación de queloides al apuntar a EGR1. Como resultado, EGR1 es prometedor como objetivo terapéutico potencial para prevenir y tratar los queloides.

Keloid nomogram prediction model based on weighted gene co-expression network analysis and machine learning / 生物医学工程学杂志

Keloids are benign skin tumors resulting from the excessive proliferation of connective tissue in wound skin. Precise prediction of keloid risk in trauma patients and timely early diagnosis are of paramount importance for in-depth keloid management and control of its progression. This study analyzed four keloid datasets in the high-throughput gene expression omnibus (GEO) database, identified diagnostic markers for keloids, and established a nomogram prediction model. Initially, 37 core protein-encoding genes were selected through weighted gene co-expression network analysis (WGCNA), differential expression analysis, and the centrality algorithm of the protein-protein interaction network. Subsequently, two machine learning algorithms including the least absolute shrinkage and selection operator (LASSO) and the support vector machine-recursive feature elimination (SVM-RFE) were used to further screen out four diagnostic markers with the highest predictive power for keloids, which included hepatocyte growth factor (HGF), syndecan-4 (SDC4), ectonucleotide pyrophosphatase/phosphodiesterase 2 (ENPP2), and Rho family guanosine triphophatase 3 (RND3). Potential biological pathways involved were explored through gene set enrichment analysis (GSEA) of single-gene. Finally, univariate and multivariate logistic regression analyses of diagnostic markers were performed, and a nomogram prediction model was constructed. Internal and external validations revealed that the calibration curve of this model closely approximates the ideal curve, the decision curve is superior to other strategies, and the area under the receiver operating characteristic curve is higher than the control model (with optimal cutoff value of 0.588). This indicates that the model possesses high calibration, clinical benefit rate, and predictive power, and is promising to provide effective early means for clinical diagnosis.

Keloids and diabetes in a family.

An interesting familial association of keloids with diabetes mellitus in three successive generations is reported. Keloids did not develop if the injection site was changed to the anterior abdominal wall.