Construction of ceRNA Network and Disease Diagnosis Model for Keloid Based on Tumor Suppressor ERRFI1

Abstract

The aim of this study is to identify the key biomarker of keloid (KD) with significant diagnostic value and to construct the related competing endogenous RNA (ceRNA) network and disease diagnostic model to provide new ideas for the early diagnosis and prevention of KD. Public databases were used to identify the key gene of KD. Enrichment analysis and immune cell infiltration (ICI) analysis revealed its functional and immune characteristics. Then, a ceRNA network was constructed to explore the potential pathways of it. Random forest (RF) analysis was applied to construct a predictive model for the disease diagnosis of KD. Finally, immunohistochemistry (IHC) and RT-qPCR were used to verify the differential expression of key gene. ERRFI1 was identified as a key biomarker in KD and was lowly expressed in KD. The ceRNA network revealed that H0TAIRM1-has-miR-148a-3p-ERRFI1 may be a potential pathway in KD. Finally, a 2-gene diagnostic prediction model (ERRFI1, HSD3B7) was constructed and externally validated and the results suggested that the model had good diagnostic performance. ERRFI1 is a downregulated gene in KD and is expected to be a promising predictive marker and disease diagnostic gene. ICI may play a role in the progression of KD. The ceRNA network may provide new clues to the potential pathogenesis of KD. Finally, the new KD diagnostic model could be an effective tool for assessing the risk of KD development.