Identifying miRNAs Associated with the Progression of Keloid through mRNA-miRNA Network Analysis and Validating the Targets of miR-29a-3p in Keloid Fibroblasts

Abstract

Background. Keloid has brought great trouble to people and currently has no uniformly successful treatment. It is urgent to find new targets to effectively prevent the progress of keloid. The current research mainly identifies the differentially expressed genes (DEGs) in keloid through high-throughput sequencing technology and bioinformatics analysis technology, to screen new therapeutic targets and potential biomarkers. However, due to the different samples, different control groups, and small sample sizes, the sequencing results obtained from different studies are quite different and lack reliability. It is necessary to analyze the existing datasets in a reasonable way. Methods. Datasets about keloid were filtered in Gene Expression Omnibus (GEO) and ArrayExpress databases according to the inclusion and exclusion criteria. The discovery datasets were used for summarizing significant DEGs, and the validation datasets were to validate the mRNA and miRNA expression levels. The Encyclopedia of RNA Interactomes (ENCORI) online platform was used to predict the interactions between miRNAs and their target mRNAs. Protein-protein interaction network (PPI network) analysis and functional enrichment analysis were conducted. miRNA-mRNA network was established by Cytoscape software and verified in keloid tissue ( n = 8 ) by RT-qPCR. miR-29a-3p mimic and inhibitor were transfected into keloid fibroblasts (KFs) to preliminary verify its targets, the prognostic value of which was estimated by the receiver operating characteristic (ROC) curve. Results. A total of 6 datasets involving 20 patients were included. 15 miRNAs and 12 target mRNAs were identified as potential biomarkers for keloid patients. The RT-qPCR results showed that miR-29a-3p, miR-92a-3p, and miR-143-3p were downregulated, and all their target mRNAs were upregulated in keloid tissue ( P < 0.05 ). The expression of COL1A1, COL1A2, COL3A1, COL5A1, and COL5A2 decreased when miR-29a-3p was overexpressed but increased when miR-29a-3p was knocked down ( P < 0.05 ). And these genes had a good performance in the diagnosis of keloid, especially when using keloid nonlesional skin or normal scar tissues as controls. Conclusion. The miRNA-mRNA network, especially miR-29a-3p and its targets, may provide insights into the underlying pathogenesis of keloid and serve as potential biomarkers for keloid treatment.

Effects of miR-214 on adenosine A2A receptor and carboxymethyl chitosan nanoparticles on the function of keloid fibroblasts and their mechanisms

“…Multiple research results suggest that miRNAs play a crucial role in scar formation and represent potential targets for scar treatment 28 , 29 . MiRNAs can slow down scar formation by inhibiting the target gene expression and activating or inhibiting related pathways 30 .…”

Characterization of the skin keloid microenvironment

“…MiR-NAs interact with the 3' untranslated region (UTR) of target mRNAs, thus regulating target gene expression [84]. He et al identified miRNA-29a-3p as a potential biomarker for keloid treatment through mRNA-miRNA network analysis, as it was associated with the progression of keloids [85]. An analysis with a pathway-focused lncRNA microarray identified four lncRNA biomarkers in keloids involved in the Wnt pathway [86].…”

Progress in the clinical treatment of keloids

“…Of these, three main types of ncRNAs, i.e., miRNA, lncRNA, and circRNA, have been reported to control fibroblast proliferation, migration, invasion, apoptosis, and collagen synthesis through different pathways (92), thereby participating in the keloidogenesis and developmental process. Some researchers consider miR-29a-3p to be a core miRNA that may play an important role in keloid development and progression and may therefore be an effective target for keloid therapy (93). Key processes involved in keloid regulation by lncRNA include the proliferation of fibroblasts, deposition of ECM (94), Wnt signaling (95), Hh signaling (96) and TGF-β signaling (97).…”