Immune-related gene expression in skin, inflamed and keloid tissue from patients with keloids

Abstract

Keloids are a tumor-like fibroproliferative skin disease that could cause disfigurement and disability. The pathological mechanisms underlying this condition remain unclear, particularly the progression from normal healthy skin to inflammatory skin tissue, then keloid. In the present study, three immune-related gene expression profiling datasets, were obtained from normal skin tissue (N group), inflamed tissue (I group) and keloid tissue samples from patients with keloids (K group). This sample grouping represents the primary steps of keloid formation, from normal to inflammatory, and finally to keloid tissue. The expression levels of immune-related genes were analyzed, and the differentially expressed genes (DEGs) between the three groups were compared. Protein-protein interaction networks were established using Cytoscape. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were carried out to determine the main functions associated with the DEGs and keloid-associated pathways. The results identified hub genes in the N and I groups, including C-C motif chemokine receptor (CCR) 1, CCR7, CD40 ligand, C-X-C motif chemokine ligand 9, IL-6 and IL-10. The hub genes in the I and the K groups included IL-10, IL-6, IL-13 and CD86. The expression levels of these genes were verified using reverse transcription-quantitative PCR. The results demonstrated that IL-6 expression levels were significantly increased in the I group compared with the N group (P=0.0111). CCR7 levels significantly differed between all three groups (P<0.017). The results of GO analysis suggested that the hub genes in the I and N groups may be associated with 'regulation of lymphocyte activation' and 'T-cell activation'. Similar results were also observed between the I and K groups, which may play an important role in keloid initiation and formation. In conclusion, CCR7, IL-10 and IL-6 may be important in keloid initiation and formation. These findings provided insight into the pathogenesis of keloids and may help identify novel immune-related therapeutic targets for this condition.

Keywords: C-C motif chemokine receptor 7; differentially expressed genes; immune-related gene; keloid; tumor epigenetics.

Figure 1.

Histological images of healthy skin, inflamed tissue and keloid tissue from the same patient. The three groups of samples were not consistent in morphology, representing the morphological changes of the disease from normal skin to inflammatory tissue, and finally forming keloids (A) H&E staining of group N. Magnification, ×100. (B) H&E staining of group I. Magnification, ×100. (C) H&E staining of group K. Magnification, ×100. The deep purple cells indicated by the red arrow are inflammatory cells. The blue arrows indicate collagen fibers. H&E, hematoxylin and eosin.

Figure 2.

PCA is one of the most widely used data dimension reduction algorithms. By calculating the covariance matrix of the data matrix, the eigenvalue eigenvector of the covariance matrix is obtained, and the matrix composed of the corresponding eigenvectors of k features with the largest eigenvalue (i.e., the largest variance) is selected. In this way, the data matrix can be transformed into the new space and the dimension reduction of data features can be realized. (A) PCA of samples between groups I and N. In the figure, principal component 1 (PC1) and principal component 2 (PC2) are used as the X-axis and Y-axis, respectively, to draw the scatter diagram, where each point represents a sample. In such a PCA diagram, the farther the two samples are from each other, the greater the difference is between the two samples in terms of gene expression patterns. (B) PCA of groups I and K. (C) PCA of groups N and K. PCA, principal component analysis.

Figure 3.

(A) A volcano map between groups I and N. The horizontal and vertical coordinates represent the average value of each different gene expression between groups I and N. The cutoff for log2 fold change >1.5 or <-1.5 and P<0.05 were the screening criteria. Significantly upregulated DEGs are shown in red, and significantly downregulated DEGs are shown in green. (B) Volcano map plot between groups I and K. (C) Volcano map between groups N and K.

Figure 4.

PPI constructs the interactions between known and predicted proteins, and calculates the co-expression relationships of genes and function interaction network among genes. (A) PPI network of DEGs in groups I and N. (B) PPI network of DEGs in groups I and K. (C) PPI network of DEGs in groups N and K. (D) The hub genes were identified from the PPI network in groups I and N. (E) The hub genes were identified from the PPI network in groups I and K. (F) The hub genes were identified from the PPI network in groups N and K. The larger circle, the darker color, the more it is associated with surrounding molecules. The more important the gene, the easier it is to screen out. DEG, differentially expressed gene; PPI, protein-protein interaction.

Figure 5.

GO, a database created by the Gene Ontology Consortium, consists of a set of pre-defined GO terms that define and describe the functions of genes and proteins. (A-C) GO enrichment analyses of biological processes, cellular components, and molecular functions of differentially expressed genes between groups I and N. The cutoff for log2 fold change >1.5 or <-0.5 and P<0.05 were used as screening criteria. GO, Gene Ontology; BP, biological processes; CC, cellular components; MF, molecular functions.

Figure 6.

(A-C) Gene Ontology enrichment analyses of biological processes, cellular components, and molecular functions of differentially expressed genes between groups I and K. The cutoff for log2 fold change >1.5 or <-0.5 and P<0.05 were used as screening criteria. GO, Gene Ontology; BP, biological processes; CC, cellular components; MF, molecular functions.

Figure 7.

(A) KEGG pathway analysis of DEGs between groups I and N. The cutoff for log2 fold change >1.5 or <-0.5 and P<0.05 were used as screening criteria. (B) KEGG pathway analysis of DEGs between groups I and K. The cutoff for log2 fold change >1.5 or <-0.5 and P<0.05 were used as screening criteria. (C) KEGG pathway analysis of DEGs between groups N and K. The cutoff for log2 fold change >1.5 or <-0.5 and P<0.05 were used as screening criteria. KEGG, Kyoto Encyclopedia of Genes and Genomes; DEGs, differentially expressed genes.

Figure 8.

Relative expression of genes. P<0.017 was considered to indicate a statistically significant difference. (A) Relative expression of CCR1 by RT-qPCR analysis. (B) Relative expression of CCR7 by RT-qPCR analysis. (C) Relative expression of CD40LG by RT-qPCR analysis. (D) Relative expression of CXCL9 by RT-qPCR analysis. (E) Relative expression of IL-6 by RT-qPCR analysis. (F) Relative expression of IL-10 by RT-qPCR analysis. RT-qPCR, reverse transcription-quantitative PCR.

Figure 9.

(A-C) Relative expression of CD86, and MMP2 by reverse transcription-quantitative PCR analysis. (D) Quantitative comparison of CCR7 expression among the three groups. (E) Western blot expression of CCR7 among the three groups (N, I and K group). P<0.0001, I vs. N; P=0.0023, I vs. K; P=0.0001, N vs. K; and P=0.0002. P<0.017 was considered to indicate a statistically significant difference.