鉴定和评估与人类牙周炎相关的差异表达的坏死性凋亡长非编码 RNA。
Identification and assessment of differentially expressed necroptosis long non-coding RNAs associated with periodontitis in human.
机构信息
Department of Orthodontics, Stomatological Hospital and Dental School of Tongji University, Shanghai Engineering Research Center of Tooth Restoration and Regeneration, Shanghai, 200072, China.
Department of Maxillofacial Surgery, Stomatological Hospital and Dental School of Tongji University, Shanghai Engineering Research Center of Tooth Restoration and Regeneration, Shanghai, 200072, China.
出版信息
BMC Oral Health. 2023 Sep 4;23(1):632. doi: 10.1186/s12903-023-03308-0.
BACKGROUND
Periodontitis is the most common oral disease and is closely related to immune infiltration in the periodontal microenvironment and its poor prognosis is related to the complex immune response. The progression of periodontitis is closely related to necroptosis, but there is still no systematic study of long non-coding RNA (lncRNA) associated with necroptosis for diagnosis and treatment of periodontitis.
MATERIAL AND METHODS
Transcriptome data and clinical data of periodontitis and healthy populations were obtained from the Gene Expression Omnibus (GEO) database, and necroptosis-related genes were obtained from previously published literature. FactoMineR package in R was used to perform principal component analysis (PCA) for obtaining the necroptosis-related lncRNAs. The core necroptosis-related lncRNAs were screened by the Linear Models for Microarray Data (limma) package in R, PCA principal component analysis and lasso algorithm. These lncRNAs were then used to construct a classifier for periodontitis with logistic regression. The receiver operating characteristic (ROC) curve was used to evaluate the sensitivity and specificity of the model. The CIBERSORT method and ssGSEA algorithm were used to estimate the immune infiltration and immune pathway activation of periodontitis. Spearman's correlation analysis was used to further verify the correlation between core genes and periodontitis immune microenvironment. The expression level of core genes in human periodontal ligament cells (hPDLCs) was detected by RT-qPCR.
RESULTS
A total of 10 core necroptosis-related lncRNAs (10-lncRNAs) were identified, including EPB41L4A-AS1, FAM30A, LINC01004, MALAT1, MIAT, OSER1-DT, PCOLCE-AS1, RNF144A-AS1, CARMN, and LINC00582. The classifier for periodontitis was successfully constructed. The Area Under the Curve (AUC) was 0.952, which suggested that the model had good predictive performance. The correlation analysis of 10-lncRNAs and periodontitis immune microenvironment showed that 10-lncRNAs had an impact on the immune infiltration of periodontitis. Notably, the RT-qPCR results showed that the expression level of the 10-lncRNAs obtained was consistent with the chip analysis results.
CONCLUSIONS
The 10-lncRNAs identified from the GEO dataset had a significant impact on the immune infiltration of periodontitis and the classifier based on 10-lncRNAs had good detection efficiency for periodontitis, which provided a new target for diagnosis and treatment of periodontitis.
背景
牙周炎是最常见的口腔疾病，与牙周微环境中的免疫浸润密切相关，其预后不良与复杂的免疫反应有关。牙周炎的进展与坏死性凋亡密切相关，但目前尚无针对坏死性凋亡相关长链非编码 RNA（lncRNA）的系统研究用于牙周炎的诊断和治疗。
材料和方法
从基因表达综合数据库（GEO）中获得牙周炎和健康人群的转录组数据和临床数据，并从已发表的文献中获得坏死性凋亡相关基因。使用 R 中的 FactoMineR 软件包进行主成分分析（PCA）以获得与坏死性凋亡相关的 lncRNA。使用 R 中的 Linear Models for Microarray Data（limma）软件包筛选核心与坏死性凋亡相关的 lncRNA。通过 PCA 主成分分析和套索算法筛选这些 lncRNA，然后使用逻辑回归构建牙周炎分类器。使用受试者工作特征（ROC）曲线评估模型的灵敏度和特异性。使用 CIBERSORT 方法和 ssGSEA 算法估计牙周炎的免疫浸润和免疫途径激活。Spearman 相关性分析进一步验证核心基因与牙周炎免疫微环境的相关性。通过 RT-qPCR 检测人牙周膜细胞（hPDLCs）中核心基因的表达水平。
结果
共鉴定出 10 个与坏死性凋亡相关的核心 lncRNA（10-lncRNA），包括 EPB41L4A-AS1、FAM30A、LINC01004、MALAT1、MIAT、OSER1-DT、PCOLCE-AS1、RNF144A-AS1、CARMN 和 LINC00582。成功构建了牙周炎分类器。曲线下面积（AUC）为 0.952，表明该模型具有良好的预测性能。10-lncRNA 与牙周炎免疫微环境的相关性分析表明，10-lncRNA 对牙周炎的免疫浸润有影响。值得注意的是，RT-qPCR 结果表明，芯片分析获得的 10-lncRNA 的表达水平与芯片分析结果一致。
结论
从 GEO 数据集鉴定的 10-lncRNA 对牙周炎的免疫浸润有显著影响，基于 10-lncRNA 的分类器对牙周炎具有良好的检测效率，为牙周炎的诊断和治疗提供了新的靶点。
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