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

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.

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Journal
Medicine
Published
2025-08-22
DOI
https://doi.org/10.1097/md.0000000000044002
Citations
1
Primary Topic
Immune cells in cancer
Type
article
Field-Weighted Citation Impact
0.80
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article

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

Wenjie Hao, Xiaoyao Tan, Huawei Ming, Qin Hu et al.
1 citations
Medicine
Immune cells in cancer
0.80
article

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

Wenjie Hao, Xiaoyao Tan, Huawei Ming, Qin Hu, Fangyuan Chen, Yongheng Zhang, Zong-Yi Yuan, Xingan Zhang
article en
1 citations

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.

MedicineVol. 104(34)
North Sichuan Medical University (CN), Nanchong Central Hospital (CN)
Openalex Percentile: Top 24%
Immune cells in cancer
0.80
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