Frontiers in immunology · 2025
Integrated machine learning and single-cell RNA sequencing reveal COL4A2 and CXCL6 as oxidative stress-associated biomarkers in periodontitis.
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Sun Siyu, Ren Jing, Zeng Xiujuan, Chen Yanbin, Zhou Qianbing, Yang Junying, Chen Shan
Аннотация
BACKGROUND: Periodontitis, recognized as the second most prevalent oral disease globally, is strongly linked to systemic disorders like diabetes and cardiovascular diseases, highlighting the critical need for effective prevention and treatment strategies. Oxidative stress plays an important role in periodontitis pathogenesis and progression, yet their specific association remains unclear. This study aims to explore the association between oxidative stress and periodontitis pathogenesis while identifying potential diagnostic biomarkers and therapeutic targets for this condition.
METHODS: Transcriptomic data from gingival tissues of periodontitis patients and controls were obtained from the Gene Expression Omnibus (GEO) database. Key genes linked to oxidative stress in periodontitis were identified through a comprehensive analytical approach, including differential expression analysis, weighted gene co-expression network analysis (WGCNA), gene set enrichment analysis (GSEA), and functional enrichment analyses (GO and KEGG). Machine learning algorithms were subsequently employed to refine the selection of key genes. The relationship between oxidative stress and the expression of these key genes was validated using external datasets and a periodontitis rat model. Additionally, single-cell RNA sequencing (scRNA-seq) data were interrogated to delineate the cellular subpopulations expressing the key genes, leveraging clustering and annotation approaches.
RESULTS: Comprehensive bioinformatics analysis identified COL4A2, CYR61, and CXCL6 as key genes associated with oxidative stress in periodontitis. Among these genes, COL4A2 and CXCL6 showed elevated expression levels in the gingival tissues of periodontitis rats. Single-cell RNA-seq analysis further demonstrated that COL4A2 exhibited predominant expression within endothelial and stromal cell clusters, whereas CXCL6 was predominantly localized to epithelial cell clusters.
CONCLUSIONS: This study demonstrates a correlation between oxidative stress and the progression of periodontitis. COL4A2 and CXCL6 were identified as potential therapeutic targets for the treatment of periodontitis.
METHODS: Transcriptomic data from gingival tissues of periodontitis patients and controls were obtained from the Gene Expression Omnibus (GEO) database. Key genes linked to oxidative stress in periodontitis were identified through a comprehensive analytical approach, including differential expression analysis, weighted gene co-expression network analysis (WGCNA), gene set enrichment analysis (GSEA), and functional enrichment analyses (GO and KEGG). Machine learning algorithms were subsequently employed to refine the selection of key genes. The relationship between oxidative stress and the expression of these key genes was validated using external datasets and a periodontitis rat model. Additionally, single-cell RNA sequencing (scRNA-seq) data were interrogated to delineate the cellular subpopulations expressing the key genes, leveraging clustering and annotation approaches.
RESULTS: Comprehensive bioinformatics analysis identified COL4A2, CYR61, and CXCL6 as key genes associated with oxidative stress in periodontitis. Among these genes, COL4A2 and CXCL6 showed elevated expression levels in the gingival tissues of periodontitis rats. Single-cell RNA-seq analysis further demonstrated that COL4A2 exhibited predominant expression within endothelial and stromal cell clusters, whereas CXCL6 was predominantly localized to epithelial cell clusters.
CONCLUSIONS: This study demonstrates a correlation between oxidative stress and the progression of periodontitis. COL4A2 and CXCL6 were identified as potential therapeutic targets for the treatment of periodontitis.