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基于miRNA阵列的牙周炎诊断标志物的开发

Development of an miRNA-Array-Based Diagnostic Signature for Periodontitis.

作者信息

Jin Su-Han, Zhou Jian-Guo, Guan Xiao-Yan, Bai Guo-Hui, Liu Jian-Guo, Chen Liang-Wen

机构信息

Department of Orthodontics, Affiliated Stomatological Hospital of Zunyi Medical University, Zunyi, China.

Department of Oncology, Affiliated Hospital of Zunyi Medical University, Zunyi, China.

出版信息

Front Genet. 2020 Dec 16;11:577585. doi: 10.3389/fgene.2020.577585. eCollection 2020.

Abstract

Periodontitis progression is accompanied by irreversible alveolar bone absorption and leads to tooth loss. Early diagnosis is important for tooth stability and periodontal tissue preservation. However, there is no recognized miRNA diagnostic signature with convincing sensitivity and specificity for periodontitis. In this study, we obtained miRNA array expression profiles of periodontitis from the Gene Expression Omnibus (GEO) database. After screening for differentially expressed miRNAs, the least absolute shrinkage and selection operator (LASSO) method was performed to identify and construct a 17-miRNA-based diagnostic signature (hsa-miR-3917, hsa-mir-4271, hsa-miR-3156, hsa-miR-3141, hsa-miR-1246, hsa-miR-125a-5p, hsa-miR-671-5p, hcmv-mir-UL70, hsa-miR-650, hsa-miR-497-3p, hsa-miR-145-3p, hsa-miR-141-3p, hsa-miR-210-3p, hsa-miR-204-3p, hsa-miR-203a-5p, hsa-miR-99a-3p, and hsa-miR-30a-3p). Periodontal tissue samples with higher risk scores were more likely to show symptoms of periodontitis. Then, the receiver operating characteristic (ROC) curves were used to assess the diagnostic value of the miRNA signature, which indicated that the optimum cutoff value in periodontitis diagnosis was 0.5056 with an area under the ROC curve (AUC) of 0.996, a sensitivity of 97.3%, a specificity of 100.0% in the training cohort; in the testing cohort, the corresponding values were as follows: an AUC of 0.998, a sensitivity of 97.9%, and a specificity of 91.7%. We next evaluated the efficacy of the signature in differentiating disease subtype and affected range. Furthermore, we conducted functional enrichment analysis of the 17 miRNA-targeted mRNAs, including the regulation of mTOR activity and cell autophagy, Th1/Th2 cell balance and immunoregulation, cell apoptosis, and so on. In summary, our study identified and validated a 17-miRNA diagnostic signature with convincing AUC, sensitivity, and specificity for periodontitis.

摘要

牙周炎的进展伴随着不可逆的牙槽骨吸收,并导致牙齿脱落。早期诊断对于牙齿稳固和牙周组织保存至关重要。然而,目前尚无公认的对牙周炎具有令人信服的敏感性和特异性的miRNA诊断标志物。在本研究中,我们从基因表达综合数据库(GEO)中获取了牙周炎的miRNA芯片表达谱。在筛选差异表达的miRNA后,采用最小绝对收缩和选择算子(LASSO)方法来识别并构建基于17种miRNA的诊断标志物(hsa-miR-3917、hsa-mir-4271、hsa-miR-3156、hsa-miR-3141、hsa-miR-1246、hsa-miR-125a-5p、hsa-miR-671-5p、hcmv-mir-UL70、hsa-miR-650、hsa-miR-497-3p、hsa-miR-145-3p、hsa-miR-141-3p、hsa-miR-210-3p、hsa-miR-204-3p、hsa-miR-203a-5p、hsa-miR-99a-3p和hsa-miR-30a-3p)。风险评分较高的牙周组织样本更有可能表现出牙周炎症状。然后,使用受试者工作特征(ROC)曲线来评估该miRNA标志物的诊断价值,结果表明在训练队列中,牙周炎诊断的最佳截断值为0.5056,ROC曲线下面积(AUC)为0.996,敏感性为97.3%,特异性为100.0%;在测试队列中,相应的值如下:AUC为0.998,敏感性为97.9%,特异性为91.7%。接下来,我们评估了该标志物在区分疾病亚型和受累范围方面的效能。此外,我们对17种miRNA靶向的mRNA进行了功能富集分析,包括mTOR活性和细胞自噬的调节、Th1/Th2细胞平衡和免疫调节、细胞凋亡等。总之,我们的研究识别并验证了一种对牙周炎具有令人信服的AUC、敏感性和特异性的17-miRNA诊断标志物。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f66/7772397/78ae49f801ec/fgene-11-577585-g001.jpg

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