ArticleBMC oral health2024
Prediction of interactomic hub genes in PBMC cells in type 2 diabetes mellitus, dyslipidemia, and periodontitis.
Article in BMC oral health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 19 citations in OpenAlex.
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- Global Salivary Proteomic Profiling of Individuals with the Co-Occurrence of Type 2 Diabetes Mellitus, Dyslipidemia, and Periodontitis.International journal of molecular sciences · 2025Article
- A Molecular Perspective on the Intricate Interplay Among Exosomes, Bioenergetic Metabolism, and the Pathogenesis of Diabetic Cardiomyopathy.Journal of cardiovascular translational research · 2025Review
- Analysis of the correlation between glycemic variability indices and disease severity as well as inflammatory status in patients with type 2 diabetes mellitus and periodontitis.Clinical oral investigations · 2025Article
- Identification and validation of endoplasmic reticulum autophagy-related potential biomarkers in periodontitis.Scientific reports · 2025Article
- Identification and Validation of Aging- and Endoplasmic Reticulum Stress-Related Genes in Periodontitis Using a Competing Endogenous RNA Network.Inflammation · 2025Article
- Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning.Discover oncology · 2025Article
- Multi-omics integration to identify immune-associated biomarkers and potential therapeutics in periodontitis.Frontiers in medicine · 2025Article
- Analysis of the basement membrane-related genes ITGA7 and its regulatory role in periodontitis via machine learning: a retrospective study.BMC oral health · 2024Article
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- Artificial intelligence-powered innovations in periodontal diagnosis: a new era in dental healthcare.Frontiers in medical technology · 2024Article
- Graph attention network predicts drug-gene associations of matrix metalloproteinases 9-based host modulation in periodontitis.Journal of Indian Society of PeriodontologyArticle
- Comparing gradient boosting and neural networks in the prediction of intersecting genes in gingival epithelial immunity.Journal of Indian Society of PeriodontologyArticle
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Authors and funding
8 authors at 7 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
objectiveIn recent years, the complex interplay between systemic health and oral well-being has emerged as a focal point for researchers and healthcare practitioners. Among the several important connections, the convergence of Type 2 Diabetes Mellitus (T2DM), dyslipidemia, chronic periodontitis, and peripheral blood mononuclear cells (PBMCs) is a remarkable example. These components collectively contribute to a network of interactions that extends beyond their domains, underscoring the intricate nature of human health. In the current study, bioinformatics analysis was utilized to predict the interactomic hub genes involved in type 2 diabetes mellitus (T2DM), dyslipidemia, and periodontitis and their relationships to peripheral blood mononuclear cells (PBMC) by machine learning algorithms. MATERIALS AND
methodsGene Expression Omnibus datasets were utilized to identify the genes linked to type 2 diabetes mellitus(T2DM), dyslipidemia, and Periodontitis (GSE156993).Gene Ontology (G.O.) Enrichr, Genemania, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were used for analysis for identification and functionalities of hub genes. The expression of hub D.E.G.s was confirmed, and an orange machine learning tool was used to predict the hub genes.
resultThe decision tree, AdaBoost, and Random Forest had an A.U.C. of 0.982, 1.000, and 0.991 in the R.O.C. curve. The AdaBoost model showed an accuracy of (1.000). The findings imply that the AdaBoost model showed a good predictive value and may support the clinical evaluation and assist in accurately detecting periodontitis associated with T2DM and dyslipidemia. Moreover, the genes with p-value < 0.05 and A.U.C.>0.90, which showed excellent predictive value, were thus considered hub genes.
conclusionThe hub genes and the D.E.G.s identified in the present study contribute immensely to the fundamentals of the molecular mechanisms occurring in the PBMC associated with the progression of periodontitis in the presence of T2DM and dyslipidemia. They may be considered potential biomarkers and offer novel therapeutic strategies for chronic inflammatory diseases.
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