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整合批量和单细胞RNA数据并结合机器学习以探索牙周炎中的线粒体自噬。

Integrating bulk and single-cell RNA data with machine learning to explore mitophagy in periodontitis.

作者信息

Hu Qisheng, Zhang Yongheng, Ming Huawei, Yuan Zongyi, Chen Fangyuan, Hao Wenjie, Tan Xiaoyao, Zhang Xingan

机构信息

Department of Stomatology, North Sichuan Medical College, Nanchong, Sichuan, China.

Department of Oral and Maxillofacial Surgery, Beijing Anzhen Nanchong Hospital of Capital Medical University, Nanchong Central Hospital, The Second Clinical Medical College of North Sichuan Medical College, Nanchong, Sichuan, China.

出版信息

Medicine (Baltimore). 2025 Aug 22;104(34):e44002. doi: 10.1097/MD.0000000000044002.

Abstract

Periodontitis (PD) is a chronic inflammatory disease in which oxidative stress plays a crucial role in its progression. Mitophagy eliminates damaged mitochondria and alleviates oxidative stress; however, its specific regulatory mechanisms in PD remain unclear. This study utilized single-cell and bulk RNA sequencing data to identify core genes and investigate their potential roles. We utilized single-cell RNA sequencing data and applied 4 algorithms - area under the curve cell level enrichment, U-statistics-based single-cell signature scoring, single-sample gene set scoring, and AddModuleScore - to assess mitophagy activity and identify candidate genes. Subsequently, based on bulk RNA-seq data, 5 machine learning algorithms, including Least Absolute Shrinkage and Selection Operator Regression, random forest, Boruta, gradient boosting machine, and eXtreme Gradient Boosting, were employed to further screen core genes from the candidate gene set. Finally, immune infiltration analysis, cell communication analysis, and gene interaction network construction were integrated to systematically elucidate the regulatory mechanisms of core genes in the progression of PD. Single-cell RNA sequencing combined with multiple algorithms revealed significantly elevated mitophagy activity in PD tissues, particularly in monocytes/macrophages and endothelial cells. Additionally, we identified 4 core genes: BNIP3L, VPS13C, CTTN, and MAP1LC3B. BNIP3L and CTTN were downregulated in periodontitis, correlating negatively with disease prevalence, immune infiltration, and inflammatory pathways, whereas VPS13C and MAP1LC3B were upregulated, showing positive correlations. CellChat analysis highlighted monocytes/macrophages and endothelial cells with high core gene expression as key mediators of intercellular communication. This study identified BNIP3L, VPS13C, CTTN, and MAP1LC3B as core mitophagy-related genes associated with PD, and highlighted the pivotal roles of monocytes/macrophages and endothelial cells in disease progression. These findings provide new insights into the pathogenesis of PD and offer a theoretical foundation for mitophagy-targeted diagnosis, biomarker identification, and precision therapy.

摘要

牙周炎(PD)是一种慢性炎症性疾病,氧化应激在其进展中起关键作用。线粒体自噬可清除受损的线粒体并减轻氧化应激;然而,其在牙周炎中的具体调控机制仍不清楚。本研究利用单细胞和批量RNA测序数据来识别核心基因并研究其潜在作用。我们使用单细胞RNA测序数据并应用4种算法——曲线下面积细胞水平富集、基于U统计量的单细胞特征评分、单样本基因集评分和AddModuleScore——来评估线粒体自噬活性并识别候选基因。随后,基于批量RNA测序数据,采用包括最小绝对收缩和选择算子回归、随机森林、Boruta、梯度提升机和极端梯度提升在内的5种机器学习算法,从候选基因集中进一步筛选核心基因。最后,整合免疫浸润分析、细胞通讯分析和基因相互作用网络构建,以系统阐明核心基因在牙周炎进展中的调控机制。单细胞RNA测序结合多种算法显示,牙周炎组织中线粒体自噬活性显著升高,尤其是在单核细胞/巨噬细胞和内皮细胞中。此外,我们鉴定出4个核心基因:BNIP3L、VPS13C、CTTN和MAP1LC3B。BNIP3L和CTTN在牙周炎中表达下调,与疾病患病率、免疫浸润和炎症途径呈负相关,而VPS13C和MAP1LC3B上调呈正相关。CellChat分析强调核心基因高表达的单核细胞/巨噬细胞和内皮细胞是细胞间通讯的关键介质。本研究确定BNIP3L、VPS13C、CTTN和MAP1LC3B为与牙周炎相关的核心线粒体自噬相关基因,并强调了单核细胞/巨噬细胞和内皮细胞在疾病进展中的关键作用。这些发现为牙周炎的发病机制提供了新见解,并为线粒体自噬靶向诊断、生物标志物鉴定和精准治疗提供了理论基础。

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