School of Medicine, Xiamen University, Xiamen, China.
Department of Burns and Plastic and Wound Repair Surgery, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China.
Biomed Res Int. 2022 Jul 13;2022:6487989. doi: 10.1155/2022/6487989. eCollection 2022.
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.
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 ( = 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.
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 ( < 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 ( < 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.
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.
瘢痕疙瘩给人们带来了极大的困扰，目前尚无统一有效的治疗方法。寻找新的靶点来有效阻止瘢痕疙瘩的进展迫在眉睫。目前的研究主要通过高通量测序技术和生物信息学分析技术来鉴定瘢痕疙瘩中的差异表达基因（DEGs），以筛选新的治疗靶点和潜在的生物标志物。然而，由于不同的样本、不同的对照组和小样本量，不同研究获得的测序结果差异很大，缺乏可靠性。有必要通过合理的方式对现有的数据集进行分析。
根据纳入和排除标准，在基因表达综合数据库（GEO）和 ArrayExpress 数据库中筛选与瘢痕疙瘩相关的数据集。利用发现数据集对显著的 DEGs 进行总结，利用验证数据集对 mRNA 和 miRNA 的表达水平进行验证。利用在线平台 Encyclopedia of RNA Interactomes（ENCORI）预测 miRNA 与其靶 mRNA 之间的相互作用。进行蛋白质-蛋白质相互作用网络（PPI 网络）分析和功能富集分析。利用 Cytoscape 软件建立 miRNA-mRNA 网络，并通过 RT-qPCR 在 8 例瘢痕疙瘩组织中进行验证。转染 miR-29a-3p 模拟物和抑制剂到瘢痕疙瘩成纤维细胞（KFs）中，初步验证其靶基因，通过受试者工作特征（ROC）曲线估计其预后价值。
共纳入 6 个涉及 20 例患者的数据集。鉴定出 15 个 miRNA 和 12 个靶 mRNA 作为瘢痕疙瘩患者的潜在生物标志物。RT-qPCR 结果显示，miR-29a-3p、miR-92a-3p 和 miR-143-3p 表达下调，其靶基因在瘢痕疙瘩组织中均上调（ < 0.05）。过表达 miR-29a-3p 时 COL1A1、COL1A2、COL3A1、COL5A1 和 COL5A2 的表达降低，而敲低 miR-29a-3p 时表达升高（ < 0.05）。这些基因在瘢痕疙瘩的诊断中表现良好，尤其是以瘢痕疙瘩非皮损皮肤或正常瘢痕组织作为对照时。
miRNA-mRNA 网络，特别是 miR-29a-3p 及其靶基因，可能为瘢痕疙瘩的潜在发病机制提供新的见解，并可作为瘢痕疙瘩治疗的潜在生物标志物。
