Shenzhen Stomatology Hospital (Pingshan), Southern Medical University, Shenzhen, China.
Department of Stomatology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Front Immunol. 2022 Aug 9;13:963123. doi: 10.3389/fimmu.2022.963123. eCollection 2022.
Recently, there are many researches on signature molecules of periodontitis derived from different periodontal tissues to determine the disease occurrence and development, and deepen the understanding of this complex disease. Among them, a variety of omics techniques have been utilized to analyze periodontitis pathology and progression. However, few accurate signature molecules are known and available. Herein, we aimed to screened and identified signature molecules suitable for distinguishing periodontitis patients using machine learning models by integrated analysis of TMT proteomics and transcriptomics with the purpose of finding novel prediction or diagnosis targets. Differential protein profiles, functional enrichment analysis, and protein-protein interaction network analysis were conducted based on TMT proteomics of 15 gingival tissues from healthy and periodontitis patients. DEPs correlating with periodontitis were screened using LASSO regression. We constructed a new diagnostic model using an artificial neural network (ANN) and verified its efficacy based on periodontitis transcriptomics datasets (GSE10334 and GSE16134). Western blotting validated expression levels of hub DEPs. TMT proteomics revealed 5658 proteins and 115 DEPs, and the 115 DEPs are closely related to inflammation and immune activity. Nine hub DEPs were screened by LASSO, and the ANN model distinguished healthy from periodontitis patients. The model showed satisfactory classification ability for both training (AUC=0.972) and validation (AUC=0.881) cohorts by ROC analysis. Expression levels of the 9 hub DEPs were validated and consistent with TMT proteomics quantitation. Our work reveals that nine hub DEPs in gingival tissues are closely related to the occurrence and progression of periodontitis and are potential signature molecules involved in periodontitis.
最近，有许多研究利用来自不同牙周组织的牙周炎特征分子来确定疾病的发生和发展，并加深对这种复杂疾病的理解。其中，各种组学技术已被用于分析牙周炎的病理和进展。然而，已知和可用的准确特征分子很少。在此，我们旨在通过整合 TMT 蛋白质组学和转录组学分析，利用机器学习模型筛选和鉴定适合区分牙周炎患者的特征分子，以期找到新的预测或诊断靶标。根据 15 例健康和牙周炎患者的牙龈组织 TMT 蛋白质组学数据，进行差异蛋白谱分析、功能富集分析和蛋白质-蛋白质相互作用网络分析。采用 LASSO 回归筛选与牙周炎相关的 DEP。我们使用人工神经网络（ANN）构建了一个新的诊断模型，并基于牙周炎转录组数据集（GSE10334 和 GSE16134）验证其疗效。Western blot 验证了核心 DEP 的表达水平。TMT 蛋白质组学揭示了 5658 种蛋白质和 115 个 DEP，这些 DEP 与炎症和免疫活性密切相关。通过 LASSO 筛选出 9 个核心 DEP，ANN 模型可区分健康和牙周炎患者。ROC 分析显示，该模型对训练（AUC=0.972）和验证（AUC=0.881）队列均具有令人满意的分类能力。9 个核心 DEP 的表达水平通过 Western blot 得到验证，并与 TMT 蛋白质组学定量结果一致。我们的工作表明，牙龈组织中 9 个核心 DEP 与牙周炎的发生和发展密切相关，是牙周炎潜在的特征分子。
