Keloids can be resected through surgery, but they may still recur. The purpose of this study was to explore the biomarkers to predict the postoperative recurrence of keloids. Patients who underwent surgical treatment and postoperative superficial X-ray radiation between January 2019 and December 2020 were recruited with clinical data and keloid samples for RNA-seq. By screening differentially expressed genes (DEGs) between postoperative recurrent and non-recurrent sample groups and constructing a co-expression network via the weighted gene co-expression network analysis (WGCNA), an immunity-related module was chosen for subsequent analysis. By constructing a DEG co-expression network and using the Molecular Complex Detection (MCODE) algorithm, five hub genes were identified in the key module. Receiver Operating Characteristic (ROC) curve analysis showed that the area under the curve (AUC) for the five combined hub genes was 0.776. The result of qRT-PCR showed that CHI3L1, IL1RN, MMP7, TNFAIP3, and TNFAIP6 were upregulated in the recurrent group with statistical significance (p < 0.05). Immune infiltration analysis showed that mast cells, macrophages, and T cells were the major components of the keloid immune microenvironment. This study provides potential biomarkers for predicting keloid recurrence and offers insights into genetic targets for recurrence prevention.
Keywords: RNA-Seq; WGCNA; gene; keloid; recurrence; surgery.