[Weighted gene co-expression network analysis of methylated genes in burn scar tissue]

Objective: To investigate the methylated genes in burn scar tissue by weighted gene co-expression network analysis (WGCNA), and to discover molecular markers and therapeutic targets of scar formation. Methods: An observational research method was used. Datasets were downloaded from the National Center for Biotechnology Information Gene Expression Omnibus Database of America. The GSE136906 (n=6) and GSE137134 (n=6) datasets in the same batch were screened out for mRNA sequencing and methylation sequencing respectively, and the dataset GSE108110 (n=9) was incorporated into support vector machine and modeling analysis. The Limma software package was used to identify the differentially expressed genes and differentially methylated genes between scar tissue after burn and normal tissue. WGCNA was used to select the module with strong correlation with clinical features of scar tissue and large number of genes. Functional enrichment analysis of the genes in the module was performed to find genes with abnormal methylation. The receiver operating characteristic (ROC) curve was used to judge diagnostic efficacy of genes with abnormal methylation for scar, and support vector machine (SVM) was used to verify. Results: A total of 10 modules were identified, and the brown module with large number of genes was highly correlated to burn scar tissue formation. The genes in the brown module were mainly concentrated in "regulation of androgen receptor signaling pathway", "cytokine-cytokine receptor interaction", "positive regulation of insulin secretion", and so on. The model showed 35 genes with abnormal methylation status. The ROC curve (area under the curve>0.9) and SVM modeling (accuracy=93.3%) indicated that CCR2, LMO7, STEAP4, NNAT, and TCF7L2 genes had good diagnostic performance for scar. Conclusions: CCR2, LMO7, STEAP4, NNAT, and TCF7L2 can be used as potential targets for burn scar treatment.