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The role of ferro-aging genes in periodontitis: novel insights based on bioinformatics analysis
Poyi Chu1, Danhua Wu1, Jianwen Deng1,2
1Medical Center of Stomatology, The First Affiliated Hospital, Jinan University, Guangzhou, China.
Background:
The concentration of iron ions in gingival crevicular fluid and local periodontal tissues is significantly elevated in periodontitis (PD), presenting a disorder of iron metabolism characterized primarily by iron overload. Excessive free iron can continuously catalyze the generation of reactive oxygen species through the Fenton reaction, providing sustained chemical driving force for lipid peroxidation, thereby constituting a key biochemical basis for the initiation of ferro-aging. However, the specific role of ferro-aging in the pathogenesis of PD and its molecular regulatory network remains unclear.
Methods:
The study obtained PD-related transcriptomic datasets from the Gene Expression Omnibus database. By intersecting the ferro-aging-related gene set (FAG) with differentially expressed genes (DEGs), ferro-aging-related differentially expressed genes (FAGDEGs) in PD were screened. Subsequently, a comprehensive application of Weighted Gene Co-expression Network Analysis (WGCNA) and two machine learning algorithms-Random Forest (RF) and Least Absolute Shrinkage and Selection Operator (LASSO) regression-was employed to further screen key ferroptosis-associated genes (KFAGs). Receiver Operating Characteristic (ROC) curve was used to evaluate the diagnostic performance of KFAGs, and experimental validation was performed using quantitative real-time polymerase chain reaction (RT-qPCR). Additionally, immune cell infiltration analysis was conducted to explore potential associations between the expression levels of KFAGs and immune cell types.
Results:
This study identified a total of 1379 DEGs, and after intersecting them with FAG, 11 FAGDEGs were obtained. Following screening by WGCNA and machine learning algorithms, 4 KFAGs were ultimately identified: IRF1, SLC1A5, PDE4B, and IL1B. ROC curve analysis results showed that the Area Under the Curve (AUC) of the above four genes was greater than 0.6, indicating their good potential value in the diagnosis of PD. RT-qPCR experimental results demonstrated that the expression trends of these four KFAGs in PD were consistent with the database analysis results. Immune cell infiltration analysis further revealed significant correlations between the expression levels of KFAGs and the abundance or activation status of various immune cell subsets.
Conclusion:
The results of this study indicate that IRF1, SLC1A5, PDE4B, and IL1B are key biomarkers associated with the ferro-aging process in PD. This finding provides new molecular evidence and research directions for further elucidating the synergistic role of iron metabolism disorders and ferro-aging in the pathogenesis of PD.
