Xiang Junwei, Huang Wenkai, He Yaodong, Li Yunshan, Wang Yuanyin, Chen Ran
College and Hospital of Stomatology, Anhui Medical University, Key Lab of Oral Diseases Research of Anhui Province, Hefei, China.
Front Genet. 2022 Nov 15;13:1041524. doi: 10.3389/fgene.2022.1041524. eCollection 2022.
Periodontitis is a chronic inflammatory disease leading to tooth loss in severe cases, and early diagnosis is essential for periodontitis prevention. This study aimed to construct a diagnostic model for periodontitis using a random forest algorithm and an artificial neural network (ANN). Gene expression data of two large cohorts of patients with periodontitis, GSE10334 and GSE16134, were downloaded from the Gene Expression Omnibus database. We screened for differentially expressed genes in the GSE10334 cohort, identified key periodontitis biomarkers using a Random Forest algorithm, and constructed a classification artificial neural network model, using receiver operating characteristic curves to evaluate its diagnostic utility. Furthermore, patients with periodontitis were classified using a consensus clustering algorithm. The immune infiltration landscape was assessed using CIBERSOFT and single-sample Gene Set Enrichment Analysis. A total of 153 differentially expressed genes were identified, of which 42 were downregulated. We utilized 13 key biomarkers to establish a periodontitis diagnostic model. The model had good predictive performance, with an area under the receiver operative characteristic curve (AUC) of 0.945. The independent cohort (GSE16134) was used to further validate the model's accuracy, showing an area under the receiver operative characteristic curve of 0.900. The proportion of plasma cells was highest in samples from patients with period ontitis, and 13 biomarkers were closely related to immunity. Two molecular subgroups were defined in periodontitis, with one cluster suggesting elevated levels of immune infiltration and immune function. We successfully identified key biomarkers of periodontitis using machine learning and developed a satisfactory diagnostic model. Our model may provide a valuable reference for the prevention and early detection of periodontitis.
牙周炎是一种慢性炎症性疾病，严重时会导致牙齿脱落，早期诊断对于预防牙周炎至关重要。本研究旨在使用随机森林算法和人工神经网络（ANN）构建牙周炎诊断模型。从基因表达综合数据库下载了两个牙周炎患者大样本队列（GSE10334和GSE16134）的基因表达数据。我们在GSE10334队列中筛选差异表达基因，使用随机森林算法识别关键的牙周炎生物标志物，并构建分类人工神经网络模型，使用受试者工作特征曲线评估其诊断效用。此外，使用一致性聚类算法对牙周炎患者进行分类。使用CIBERSOFT和单样本基因集富集分析评估免疫浸润情况。共鉴定出153个差异表达基因，其中42个下调。我们利用13个关键生物标志物建立了牙周炎诊断模型。该模型具有良好的预测性能，受试者工作特征曲线下面积（AUC）为0.945。使用独立队列（GSE16134）进一步验证模型的准确性，受试者工作特征曲线下面积为0.900。牙周炎患者样本中浆细胞比例最高，13个生物标志物与免疫密切相关。在牙周炎中定义了两个分子亚组，其中一个聚类表明免疫浸润和免疫功能水平升高。我们使用机器学习成功识别了牙周炎的关键生物标志物，并开发了一个令人满意的诊断模型。我们的模型可能为牙周炎的预防和早期检测提供有价值的参考。
