Kai Wang , Aiguo Wang , Liewang Qiu , Ling Xu , Jingjing Wei
{"title":"结合生物信息学和实验验证确定m1a修饰基因作为牙周炎和糖尿病的潜在诊断生物标志物。","authors":"Kai Wang , Aiguo Wang , Liewang Qiu , Ling Xu , Jingjing Wei","doi":"10.1016/j.archoralbio.2025.106428","DOIUrl":null,"url":null,"abstract":"<div><h3>Objective</h3><div>To identify m1A-modified genes with diagnostic potential linking periodontitis and type 2 diabetes mellitus (T2DM) by integrating bioinformatics and experimental validation.</div></div><div><h3>Design</h3><div>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 (<em>RRP8</em>, <em>ALKBH3</em>, <em>MAK16</em>, and <em>DDX18</em>). Statistical comparisons were conducted using the non-parametric Mann–Whitney U test.</div></div><div><h3>Results</h3><div>Several m1A-related genes were differentially expressed in both periodontitis and T2DM. <em>RRP8</em> and <em>ALKBH3</em> 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 <em>MAK16</em> and <em>DDX18</em> showed consistent expression patterns and strong correlations with immune infiltration. qRT-PCR confirmed significant downregulation of <em>RRP8</em>, <em>ALKBH3</em>, <em>MAK16</em>, and <em>DDX18</em> in inflamed gingival tissues, and upregulation in the diabetic cell model (<em>p</em> < 0.05), supporting the bioinformatics findings.</div></div><div><h3>Conclusions</h3><div>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.</div></div>","PeriodicalId":8288,"journal":{"name":"Archives of oral biology","volume":"180 ","pages":"Article 106428"},"PeriodicalIF":2.1000,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Combined bioinformatics and experimental validation identifies m1A-modified genes as potential diagnostic biomarkers in periodontitis and diabetes\",\"authors\":\"Kai Wang , Aiguo Wang , Liewang Qiu , Ling Xu , Jingjing Wei\",\"doi\":\"10.1016/j.archoralbio.2025.106428\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Objective</h3><div>To identify m1A-modified genes with diagnostic potential linking periodontitis and type 2 diabetes mellitus (T2DM) by integrating bioinformatics and experimental validation.</div></div><div><h3>Design</h3><div>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 (<em>RRP8</em>, <em>ALKBH3</em>, <em>MAK16</em>, and <em>DDX18</em>). Statistical comparisons were conducted using the non-parametric Mann–Whitney U test.</div></div><div><h3>Results</h3><div>Several m1A-related genes were differentially expressed in both periodontitis and T2DM. <em>RRP8</em> and <em>ALKBH3</em> 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 <em>MAK16</em> and <em>DDX18</em> showed consistent expression patterns and strong correlations with immune infiltration. qRT-PCR confirmed significant downregulation of <em>RRP8</em>, <em>ALKBH3</em>, <em>MAK16</em>, and <em>DDX18</em> in inflamed gingival tissues, and upregulation in the diabetic cell model (<em>p</em> < 0.05), supporting the bioinformatics findings.</div></div><div><h3>Conclusions</h3><div>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.</div></div>\",\"PeriodicalId\":8288,\"journal\":{\"name\":\"Archives of oral biology\",\"volume\":\"180 \",\"pages\":\"Article 106428\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2025-10-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Archives of oral biology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0003996925002560\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"DENTISTRY, ORAL SURGERY & MEDICINE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Archives of oral biology","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0003996925002560","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"DENTISTRY, ORAL SURGERY & MEDICINE","Score":null,"Total":0}
Combined bioinformatics and experimental validation identifies m1A-modified genes as potential diagnostic biomarkers in periodontitis and diabetes
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.
期刊介绍:
Archives of Oral Biology is an international journal which aims to publish papers of the highest scientific quality in the oral and craniofacial sciences. The journal is particularly interested in research which advances knowledge in the mechanisms of craniofacial development and disease, including:
Cell and molecular biology
Molecular genetics
Immunology
Pathogenesis
Cellular microbiology
Embryology
Syndromology
Forensic dentistry