Objective To seek for a method of constructing the tissue microarray which contains keloid, skin around keloid, and normal skin. Methods The specimens were gained from patients of voluntary donation between March and May2009, including the tissues of keloid (27 cases), skin around keloid (13 cases), and normal skin (27 cases). The specimens were imbedded by paraffin as donor blocks. The traditional method of constructing the tissue microarray and section were modified according to the histological characteristics of the keloid and skin tissue and the experimental requirement. The tissue cores were drilled from donor blocks and attached securely on the adhesive platform which was prepared. The adhesive platform with tissue cores in situ was placed into an imbedding mold, which then was preheated briefly. Paraffin at approximately 70℃ was injected to fill the mold and then cooled to room temperature. Then HE staining, immunohistochemistry staining were performed and the results were observed by microscope. Results The constructed tissue microarray block contained 67 cores as designed and displayed smooth surface with no crack. All the cores distributed regularly, had no disintegration or manifest shift. HE staining of tissue microarray section showed that all cores had equal thickness, distinct layer, manifest contradistinction, well-defined edge, and consistent with original pathological diagnosis. Immunohistochemistry staining results demonstrated that all cores contained enough tissue dose to apply group comparison. However, in tissue microarray which was made as traditional method, many cores missed and a few cores shifted obviously. Conclusion Applying modified method can successfully construct tissue microarray which is composed of keloid, skin around keloid, and normal skin. This tissue microarray will become an effective tool of researching the pathogenesis of keloid.
To study the variations of l ipid peroxidation products and copper, zinc-superoxide dismutase(CuZn-SOD) in pathological scars (hypertrophic scars and keloids). Methods The specimens were gained from patients of voluntary contributions from May 2005 to August 2005. The tissues of hypertrophic scar (10 cases, aged 16-35 years, the mean course of disease was 2.2 years), keloid (10 cases, aged 17-32 years, the mean course of disease was 8 months) and normal skin (8 cases, aged 16-34 years) were obtained. The content of malonaldehyde (MDA)and CuZn-SOD activity were detected by spectrophotometric method. The expression of CuZn-SOD was evaluated by immunohistochemistry technique. Results The contents of MDA and CuZn-SOD activity were significantly higher in hypertrophic scars[MDA (1.139 0 ± 0.106 7)nmoL/mg prot, CuZn-SOD (31.65 ± 2.21)U/mg prot, (P lt; 0.05)]and keloids[MDA (1.190 0 ± 0.074 8)nmoL/ mg prot, CuZn-SOD (34.36 ± 5.01)U/mg prot (P lt; 0.05)] than those of normal skin tissues [MDA (0.821 3 ± 0.086 4)nmoL/mg prot, CuZn-SOD (20.60 ± 5.56)U/mg prot]. Immunohistochemical studies indicated that the brown particles were CuZn-SOD positive signals, which mainly located cytoplasm in normal skin tissues, hypertrophic scars as well as keloids epidermal keratinocytes and dermal fibroblasts. CuZn-SOD expression evaluation in hypertrophic scars (4.14 ± 0.90, P lt; 0.05) and keloids epidermal keratinocytes (4.43 ± 0.79, P lt; 0.05) markedly increased when compared with normal skin tissues (2.20 ± 0.45). The expression of CuZn-SODin hypertrophic scars (4.00 ± 0.82, P lt; 0.05) and keloids dermal fibroblasts (4.43 ± 0.53, P lt; 0.05) were significantly higher than that of normal skin tissues (1.60 ± 0.89). There were no differences in the content of MDA, CuZn-SOD activity and expression evaluation between hypertrophic scars and keloids (P gt; 0.05). Conclusion In pathological scars, the contents of MDA and CuZn-SOD activity increase and the expressions of CuZn-SOD are enlarged.
ObjectiveTo explore if Smad7 protein can inhibit growth of keloids by observing the gene and protein expressions of Smad7, collagen type Ⅰ, and collagen type Ⅲ and cell proliferation after over-expression vectors of Smad7 transfecting keloid fibroblasts (KFb). MethodsFibroblasts were acquired from 10 male patient with keloids at the age of 20 to 25 years. After in vitro culture, KFb were divided into 3 groups: untransfected group (group A), pcDNA3.1 (-) transfected group (group B), and pcDNA3.1 (-)-smad7 transfected group (group C). The mRNA and protein expression levels of Smad7, collagen type Ⅰ, and collagen type Ⅲ were detected by real-time fluorescence quantitative PCR and Western blot at 48 hours after transfection. The cell proliferation ability was detected by MTT assay at 24 hours after transfection. ResultsThe relative expression levels of mRNA and protein of Smad7 in group C were significantly higher than those in group A and group B (P < 0.01). The relative expression levels of mRNA and protein of collagen type Ⅰ and collagen type Ⅲ in group C were significantly lower than those in group A and group B (P < 0.01). The relative expression levels of mRNA of collagen type Ⅰ and collagen type Ⅲ in group B were significantly higher than those in group A (P < 0.01); and the relative expression levels of proteins of Smad7, collagen type Ⅰ, and collagen type Ⅲ were significantly lower than those in group A (P < 0.01). The cell proliferation ability in group C was significantly lower than that in group A and group B at each time point by MTT assay (P < 0.05), but no difference was found between group A and group B (P>0.05). ConclusionGene expressions of collagen type Ⅰ, and collagen type Ⅲ and cell proliferation will be inhibited after KFb are transfected by over-expression vector of Smad7.
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.