Baheti Wufanbieke, Dong Diwen, Li Congcong, Chen Xiaotao
Department of Stomatology, People's Hospital of Xinjiang Autonomous Region, Urumqi City, China.
The Affiliated Hospital of Stomatology, School of Stomatology, Zhejiang University School of Medicine, Hangzhou, China.
BMC Oral Health. 2025 Jan 6;25(1):28. doi: 10.1186/s12903-024-05409-w.
The progression and severity of periodontitis (PD) are associated with the release of extracellular vesicles by periodontal tissue cells. However, the precise mechanisms through which exosome-related genes (ERGs) influence PD remain unclear. This study aimed to investigate the role and potential mechanisms of key exosome-related genes in PD using transcriptome profiling at the single-cell level.
The current study cited GSE16134, GSE10334, GSE171213 datasets and 19,643 ERGs. Initially, differential expression analysis, three machine learning (ML) models, gene expression analysis and receiver operating characteristic (ROC) analysis were proceeded to identify core genes. Subsequently, a core gene-based artificial neural network (ANN) model was built to evaluate the predictive power of core genes for PD. Gene set enrichment analysis (GSEA) and immunoinfiltration analysis were conducted based on core genes. To pinpoint key cell types influencing the progression of periodontal at the single-cell level, a series of single-cell analyses covering pseudo-time series analysis were accomplished. The expression verification of core genes was performed through quantitative reverse transcription polymerase chain reaction (qRT-PCR).
CKAP2, IGLL5, MZB1, CXCL6, and AADACL2 served as core genes diagnosing PD. Four core gene were elevated in the PD group in addition to down-regulated AADACL2. The core gene-based-ANN model had AUC values of 0.909 in GSE16134 dataset, which exceeded AUC of each core gene, highlighting the accurately and credibly predictive performance of ANN model. GSEA revealed that ribosome was co-enriched by 5 core genes, manifesting the expression of these genes might be critical for protein structure or function. Immunoinfiltration analysis found that CKAP2, IGLL5, MZB1, and CXCL6 exhibited positive correlations with most discrepant immune cells/discrepant stromal cells, which were highly infiltrated in PD. B cells and T cells holding crucial parts in PD were identified as key cell types. Pseudo-time series analysis revealed that the expression of IGLL5 and MZB1 increased during T cell differentiation, increased and then decreased during B cell differentiation. The qRT-PCR proved the mRNA expression levels of CKAP2 and MZB1 were increased in the blood of PD patients compared to controls. But the mRNA expression levels of AADACL2 was decreased in the PD patients compared to controls. This is consistent with the trend in the amount of expression in the dataset.
CKAP2, IGLL5, MZB1, CXCL6 and AADACL2 were identified as core genes associated with exosomes, helping us to understand the role of these genes in PD.
牙周炎(PD)的进展和严重程度与牙周组织细胞释放细胞外囊泡有关。然而,外泌体相关基因(ERGs)影响牙周炎的确切机制仍不清楚。本研究旨在通过单细胞水平的转录组分析,探讨关键外泌体相关基因在牙周炎中的作用及潜在机制。
本研究引用了GSE16134、GSE10334、GSE171213数据集和19,643个ERGs。首先,进行差异表达分析、三种机器学习(ML)模型、基因表达分析和受试者工作特征(ROC)分析以鉴定核心基因。随后,构建基于核心基因的人工神经网络(ANN)模型,以评估核心基因对牙周炎的预测能力。基于核心基因进行基因集富集分析(GSEA)和免疫浸润分析。为了在单细胞水平上确定影响牙周炎进展的关键细胞类型,完成了一系列涵盖伪时间序列分析的单细胞分析。通过定量逆转录聚合酶链反应(qRT-PCR)对核心基因进行表达验证。
CKAP2、IGLL5、MZB1、CXCL6和AADACL2作为诊断牙周炎的核心基因。除AADACL2下调外,PD组中四个核心基因升高。基于核心基因的ANN模型在GSE16134数据集中的AUC值为0.909,超过了每个核心基因的AUC,突出了ANN模型准确可靠的预测性能。GSEA显示核糖体被5个核心基因共同富集,表明这些基因的表达可能对蛋白质结构或功能至关重要。免疫浸润分析发现,CKAP2、IGLL5、MZB1和CXCL6与大多数差异免疫细胞/差异基质细胞呈正相关,这些细胞在牙周炎中高度浸润。在牙周炎中起关键作用的B细胞和T细胞被确定为关键细胞类型。伪时间序列分析显示,IGLL5和MZB1的表达在T细胞分化过程中增加,在B细胞分化过程中先增加后减少。qRT-PCR证明,与对照组相比,PD患者血液中CKAP2和MZB1的mRNA表达水平升高。但与对照组相比,PD患者中AADACL2的mRNA表达水平降低。这与数据集中的表达量趋势一致。
CKAP2、IGLL5、MZB1、CXCL6和AADACL2被确定为与外泌体相关的核心基因,有助于我们了解这些基因在牙周炎中的作用。