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Updated: Aug 16, 2026
Isolation, Processing and Analysis of Murine Gingival Cells
Published on: July 2, 2013
Combined bioinformatics and experimental validation identifies m1A-modified genes as potential diagnostic biomarkers
Kai Wang1, Aiguo Wang2, Liewang Qiu3
1College of Stomatology, Chongqing Medical University, 401147, China; Chongqing Key Laboratory of Oral Diseases, 401147, China; Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, 401147, China; Chongqing Municipal Health Commission Key Laboratory of Oral Biomedical Engineering, 401147, China.
Objective:
To identify m1A-modified genes with diagnostic potential linking periodontitis and type 2 diabetes mellitus (T2DM) by integrating bioinformatics and experimental validation.
Design:
Transcriptomic data for periodontitis and T2DM patients were integrated from the GEO database to analyze m1A-related gene expression. A diagnostic model was constructed using ridge and logistic regression. Gene function enrichment, immune infiltration, and protein-protein interaction analyses explored m1A regulatory mechanisms based on m1A scoring and patient clustering models. Gingival tissue samples were collected from periodontitis patients and healthy controls, and a streptozotocin-induced diabetic β-cell model was established. qRT-PCR was performed to validate candidate genes (RRP8, ALKBH3, MAK16, and DDX18). Statistical comparisons were conducted using the non-parametric Mann-Whitney U test.
Results:
Several m1A-related genes were differentially expressed in both periodontitis and T2DM. RRP8 and ALKBH3 had high predictive value, with area under the curve (AUC) values of 0.80 (periodontitis) and 0.72 (T2DM). m1A scoring and patient clustering models effectively distinguished patient groups with distinct transcriptomic and immune profiles. Hub genes MAK16 and DDX18 showed consistent expression patterns and strong correlations with immune infiltration. qRT-PCR confirmed significant downregulation of RRP8, ALKBH3, MAK16, and DDX18 in inflamed gingival tissues, and upregulation in the diabetic cell model (p < 0.05), supporting the bioinformatics findings.
Conclusions:
This integrative study identifies key m1A-modified genes potentially linking periodontitis and diabetes. The combination of bioinformatics analysis and experimental validation highlights their potential as diagnostic biomarkers and provides novel insights into shared molecular mechanisms of these comorbid conditions.
