Hospital of Stomatology, Jilin University, Changchun 130021, China.
Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China.
Dis Markers. 2022 Aug 29;2022:8611755. doi: 10.1155/2022/8611755. eCollection 2022.
To screen for potential endoplasmic reticulum stress- (ERS-) related biomarkers of periodontitis using machine learning methods and explore their relationship with immune cells.
Three datasets of periodontitis (GSE10334, GES16134, and GES23586) were obtained from the Gene Expression Omnibus (GEO), and the samples were randomly assigned to the training set or the validation set. ERS-related differentially expressed genes (DEGs) between periodontitis and healthy periodontal tissues were screened and analyzed for GO, KEGG, and DO enrichment. Key DEGs were screened by two machine learning algorithms, LASSO regression and support vector machine-recursive feature elimination (SVM-RFE); then, the potential biomarkers were identified through validation. The infiltration of immune cells of periodontitis was calculated using the CIBERSORT algorithm, and the correlation between immune cells and potential biomarkers was specifically analyzed through the Spearman method.
We obtained 36 ERS-related DEGs of periodontitis from the training set, from which 11 key DEGs were screened by further machine learning. SERPINA1, ERLEC1, and VWF showed high diagnostic values (AUC > 0.85), so they were considered as potential biomarkers for periodontitis. According to the results of the immune cell infiltration analysis, these three potential biomarkers showed marked correlations with plasma cells, neutrophils, resting dendritic cells, resting mast cells, and follicular helper T cells.
Three ERS-related genes, SERPINA1, ERLEC1, and VWF, showed valuable biomarker potential for periodontitis, which provide a target base for future studies on early diagnosis and treatment of periodontitis.
利用机器学习方法筛选牙周炎潜在的内质网应激（ERS）相关生物标志物，并探讨其与免疫细胞的关系。
从基因表达综合数据库（GEO）中获取三个牙周炎数据集（GSE10334、GES16134 和 GES23586），并将样本随机分配到训练集或验证集中。筛选并分析牙周炎与健康牙周组织之间的 ERS 相关差异表达基因（DEGs）的 GO、KEGG 和 DO 富集。使用两种机器学习算法（LASSO 回归和支持向量机递归特征消除（SVM-RFE））筛选关键 DEGs；然后，通过验证识别潜在的生物标志物。使用 CIBERSORT 算法计算牙周炎的免疫细胞浸润，通过 Spearman 方法特异性分析免疫细胞与潜在生物标志物之间的相关性。
我们从训练集中获得了 36 个牙周炎的 ERS 相关 DEGs，进一步通过机器学习筛选出 11 个关键 DEGs。SERPINA1、ERLEC1 和 VWF 显示出较高的诊断价值（AUC>0.85），因此被认为是牙周炎的潜在生物标志物。根据免疫细胞浸润分析的结果，这三个潜在的生物标志物与浆细胞、中性粒细胞、静息树突状细胞、静息肥大细胞和滤泡辅助 T 细胞呈显著相关。
三个 ERS 相关基因 SERPINA1、ERLEC1 和 VWF 对牙周炎具有有价值的生物标志物潜力，为牙周炎的早期诊断和治疗的未来研究提供了目标基础。
