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

被引:15
作者
Jin, Su-Han [1 ]
Zhou, Jian-Guo [2 ]
Guan, Xiao-Yan [1 ]
Bai, Guo-Hui [3 ,4 ]
Liu, Jian-Guo [3 ,4 ]
Chen, Liang-Wen [5 ]
机构
[1] Zunyi Med Univ, Dept Orthodont, Affiliated Stomatol Hosp, Zunyi, Guizhou, Peoples R China
[2] Zunyi Med Univ, Dept Oncol, Affiliated Hosp, Zunyi, Guizhou, Peoples R China
[3] Zunyi Med Univ, Sch Stomatol, Zunyi, Guizhou, Peoples R China
[4] Higher Educ Inst, Special Key Lab Oral Dis Res, Zunyi, Guizhou, Peoples R China
[5] Wuhan Univ, Sch & Hosp Stomatol, Dept Oral Implantol, Hubei MOST KLOS & KLOBM, Wuhan, Peoples R China
关键词
periodontitis; miRNA; diagnostic signature; LASSO method; ROC curve; AGGRESSIVE PERIODONTITIS; CREVICULAR FLUID; DIFFERENTIATION; CELLS; EPIDEMIOLOGY; BIOMARKERS; DISEASE; MARKERS; TARGET; MODEL;
D O I
10.3389/fgene.2020.577585
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
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.
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页数:9
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