Gene expression analyses of gingival tissue of patients with periodontitis using public transcriptomic data
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| Autores: | , , , |
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| Formato: | artículo original |
| Fecha de Publicación: | 2022 |
| Descripción: | Abstract—Periodontal disease (PD) is a multifactorial and chronic condition of infection and inflammation of the gingival tissue. However, many of the biological and molecular mechanisms regarding the development of this disease remain unclear. To contribute to the understanding of PD, we developed a bioinformatic pipeline to identify differentially expressed genes (DEG) in public transcriptomic data from gingival tissue in patients with or without the disease, with subsequent analyses to characterize gene interactions and biological functions. After gene expression analysis, a total of 221 genes showed significant expression differences in gingival tissue from patients with periodontal condition compared to unaffected cases. In the annotation of the biological processes associated with these genes, a diversity of signal transduction and metabolic pathways were evidenced, highlighting those associated with immune response and extracellular matrix metabolism. In the interactome model with all the 221 differentially expressed genes, 17 were recognized as hub or central genes. Biological functions for hub genes resulted in line with the annotations for the whole network. Thus, these molecules are predicted to be useful as possible biomarkers for the periodontal condition. Further analyses are required to validate the possible role of these candidate genes as possible markers for diagnosis, prognosis, or therapeutic targets. |
| País: | Kérwá |
| Institución: | Universidad de Costa Rica |
| Repositorio: | Kérwá |
| Lenguaje: | Inglés |
| OAI Identifier: | oai:kerwa.ucr.ac.cr:10669/89249 |
| Acceso en línea: | https://ieeexplore.ieee.org/abstract/document/10032477 https://hdl.handle.net/10669/89249 |
| Access Level: | acceso abierto |
| Palabra clave: | Periodontal disease gene expression transcriptomics bioinformatics system biology |