Identification of Hub Genes of Keloid Fibroblasts by Coexpression Network Analysis and Degree Algorithm

Abstract

Background. Keloid is a benign dermal tumor characterized by abnormal proliferation and invasion of fibroblasts. The establishment of biomarkers is essential for the diagnosis and treatment of keloids. Methods. We systematically identified coexpression modules using the weighted gene coexpression network analysis method (WGCNA). Differential expressed genes (DEGs) in GSE145725 and genes in significant modules were integrated to identify overlapping key genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were then performed, as well as protein-protein interaction (PPI) network construction for hub gene screening. Results. Using the R package of WGCNA, 22 coexpression modules consisting of different genes were identified from the top 5,000 genes with maximum mean absolute deviation in 19 human fibroblast samples. Blue-green and yellow modules were identified as the most important modules, where genes overlapping with DEGs were identified as key genes. We identified the most critical functions and pathways as extracellular structure organization, vascular smooth muscle contraction, and the cGMP-PKG signaling pathway. Hub genes from key genes as BMP4, MSX1, HAND2, TBX2, SIX1, IRX1, EDN1, DLX5, MEF2C, and DLX2 were identified. Conclusion. The blue-green and yellow modules may play an important role in the pathogenesis of keloid. 10 hub genes were identified as potential biomarkers and therapeutic targets for keloid.