Department of Stomatology, The Affiliated Hospital of Yunnan University/The 2nd People's Hospital of Yunnan Province, Kunming, Yunnan, China.
Department of Prosthodontics, The Affiliated Stomatology Hospital of Kunming Medical University, Kunming, Yunnan, China.
Technol Health Care. 2022;30(5):1209-1221. doi: 10.3233/THC-THC213662.
Periodontitis is a common oral immune inflammatory disease and early detection plays an important role in its prevention and progression. However, there are no accurate biomarkers for early diagnosis.
This study screened periodontitis-related diagnostic biomarkers based on weighted gene correlation network analysis and machine algorithms.
Transcriptome data and sample information of periodontitis and normal samples were obtained from the Gene Expression Omnibus (GEO) database, and key genes of disease-related modules were obtained by bioinformatics. The key genes were subjected to Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and 5 machine algorithms: Logistic Regression (LR), Random Forest (RF), Gradient Boosting Decisio Tree (GBDT), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM). Expression and correlation analysis were performed after screening the optimal model and diagnostic biomarkers.
A total of 47 candidate genes were obtained, and the LR model had the best diagnostic efficiency. The COL15A1, ICAM2, SLC15A2, and PIP5K1B were diagnostic biomarkers for periodontitis, and all of which were upregulated in periodontitis samples. In addition, the high expression of periodontitis biomarkers promotes positive function with immune cells.
COL15A1, ICAM2, SLC15A2 and PIP5K1B are potential diagnostic biomarkers of periodontitis.
牙周炎是一种常见的口腔免疫炎症性疾病，早期发现对其预防和进展具有重要意义。然而，目前尚无准确的生物标志物用于早期诊断。
本研究通过加权基因相关网络分析和机器学习算法筛选牙周炎相关诊断生物标志物。
从基因表达综合数据库（GEO）中获取牙周炎和正常样本的转录组数据和样本信息，通过生物信息学获取疾病相关模块的关键基因。对关键基因进行基因本体（GO）富集分析、京都基因与基因组百科全书（KEGG）富集分析和 5 种机器学习算法（Logistic Regression，LR；Random Forest，RF；Gradient Boosting Decision Tree，GBDT；Extreme Gradient Boosting，XGBoost；Support Vector Machine，SVM）分析。筛选出最优模型和诊断生物标志物后进行表达和相关性分析。
共获得 47 个候选基因，LR 模型的诊断效率最高。COL15A1、ICAM2、SLC15A2 和 PIP5K1B 是牙周炎的诊断生物标志物，在牙周炎样本中均呈上调表达。此外，牙周炎生物标志物的高表达促进了与免疫细胞的正功能。
COL15A1、ICAM2、SLC15A2 和 PIP5K1B 是牙周炎的潜在诊断生物标志物。
