Prediction of interactomic hub genes in PBMC cells in type 2 diabetes mellitus, dyslipidemia, and periodontitis
Background and objective In 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 Methods Gene 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. Result The 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. Conclusion The 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..
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:24 |
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| Enthalten in: |
BMC oral health - 24(2024), 1 vom: 26. März |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
yadalam, Pradeep kumar [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| BKL: | |
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| Themen: |
Bioinformatics |
| Anmerkungen: |
© The Author(s) 2024 |
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| doi: |
10.1186/s12903-024-04041-y |
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| funding: |
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| Förderinstitution / Projekttitel: |
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SPR055309399 |
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| 520 | |a Background and objective In 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 Methods Gene 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. Result The 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. Conclusion The 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. | ||
| 650 | 4 | |a Bioinformatics |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Chronic periodontitis |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Dyslipidemia |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Hub gene |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Immunity |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Inflammation |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Peripheral blood mononuclear cells |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Type 2 diabetes mellitus |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Arumuganainar, Deepavalli |4 aut | |
| 700 | 1 | |a Ronsivalle, Vincenzo |4 aut | |
| 700 | 1 | |a Di Blasio, Marco |4 aut | |
| 700 | 1 | |a Badnjevic, Almir |4 aut | |
| 700 | 1 | |a Marrapodi, Maria Maddalena |4 aut | |
| 700 | 1 | |a Cervino, Gabriele |4 aut | |
| 700 | 1 | |a Minervini, Giuseppe |4 aut | |
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