Mediators of inflammation · 2026
Exploring Matrix Stiffness-Related Gene in Periodontitis: A Comprehensive Multidataset Analysis.
СтатьяНазвание и аннотация: Перевод в очереди
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Peng Yanhui, Yang Xiaoqin, Fang Wei, Zhang Lifang, Guo Ting, Zheng Hongyu
Аннотация
BACKGROUND: Characterized by the progressive deterioration of periodontal supporting tissues, periodontitis (PD) is a prevalent oral disorder primarily instigated by dental plaque biofilms. Matrix stiffness (MS), a key extracellular matrix (ECM) property, regulates inflammatory cell function and tissue repair balance. Herein, we sought to screen for potential biomarkers linking MS to PD pathogenesis and characterize the molecular mechanisms governing their regulation.
METHODS: To detect robust biomarkers, we utilized differential expression analysis and machine learning approaches (LASSO, random forest, and Boruta), followed by rigorous validation. Beyond biomarker identification, the study involved functional enrichment, immune infiltration estimation, drug screening, and regulatory network mapping, all integrated with single-cell data.
RESULTS: Four upregulated MS-related biomarkers (COL15A1, COL4A1, CXCR4, and MMP7) were identified, enriched in chemokine signaling and natural killer (NK) cell cytotoxicity pathways. Immune infiltration revealed 15 differentially abundant cell types (e.g., neutrophils and resting NK cells). Regulatory networks (e.g., MALAT1-hsa-miR-124-3 p-COL4A1) and potential drugs (e.g., dipyridamole) were predicted. Single-cell analysis identified 13 cell types, with endothelial cells showing high activity in fatty acid metabolism pathways.
CONCLUSION: COL15A1, COL4A1, CXCR4, and MMP7 were identified as biomarkers associated with MS. Bioinformatics analysis suggested that they might be involved in the progression of PD through pathways such as immune infiltration, metabolism, and inflammatory chemotaxis, providing hypothetical clues regarding the link between MS and the pathogenesis of PD; however, their specific mechanisms of action still required experimental validation.
METHODS: To detect robust biomarkers, we utilized differential expression analysis and machine learning approaches (LASSO, random forest, and Boruta), followed by rigorous validation. Beyond biomarker identification, the study involved functional enrichment, immune infiltration estimation, drug screening, and regulatory network mapping, all integrated with single-cell data.
RESULTS: Four upregulated MS-related biomarkers (COL15A1, COL4A1, CXCR4, and MMP7) were identified, enriched in chemokine signaling and natural killer (NK) cell cytotoxicity pathways. Immune infiltration revealed 15 differentially abundant cell types (e.g., neutrophils and resting NK cells). Regulatory networks (e.g., MALAT1-hsa-miR-124-3 p-COL4A1) and potential drugs (e.g., dipyridamole) were predicted. Single-cell analysis identified 13 cell types, with endothelial cells showing high activity in fatty acid metabolism pathways.
CONCLUSION: COL15A1, COL4A1, CXCR4, and MMP7 were identified as biomarkers associated with MS. Bioinformatics analysis suggested that they might be involved in the progression of PD through pathways such as immune infiltration, metabolism, and inflammatory chemotaxis, providing hypothetical clues regarding the link between MS and the pathogenesis of PD; however, their specific mechanisms of action still required experimental validation.