Exploring the potential link between MitoEVs and the immune microenvironment of periodontitis based on machine learning and bioinformatics methods
Abstract Background Periodontitis is a chronic inflammatory condition triggered by immune system malfunction. Mitochondrial extracellular vesicles (MitoEVs) are a group of highly heterogeneous extracellular vesicles (EVs) enriched in mitochondrial fractions. The objective of this research was to exa...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2024-02-01
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| Series: | BMC Oral Health |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12903-024-03912-8 |
| _version_ | 1827325982681333760 |
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| author | Haoran Yang Anna Zhao Yuxiang Chen Tingting Cheng Jianzhong Zhou Ziliang Li |
| author_facet | Haoran Yang Anna Zhao Yuxiang Chen Tingting Cheng Jianzhong Zhou Ziliang Li |
| author_sort | Haoran Yang |
| collection | DOAJ |
| description | Abstract Background Periodontitis is a chronic inflammatory condition triggered by immune system malfunction. Mitochondrial extracellular vesicles (MitoEVs) are a group of highly heterogeneous extracellular vesicles (EVs) enriched in mitochondrial fractions. The objective of this research was to examine the correlation between MitoEVs and the immune microenvironment of periodontitis. Methods Data from MitoCarta 3.0, GeneCards, and GEO databases were utilized to identify differentially expressed MitoEV-related genes (MERGs) and conduct functional enrichment and pathway analyses. The random forest and LASSO algorithms were employed to identify hub MERGs. Infiltration levels of immune cells in periodontitis and healthy groups were estimated using the CIBERSORT algorithm, and phenotypic subgroups of periodontitis based on hub MERG expression levels were explored using a consensus clustering method. Results A total of 44 differentially expressed MERGs were identified. The random forest and LASSO algorithms identified 9 hub MERGs (BCL2L11, GLDC, CYP24A1, COQ2, MTPAP, NIPSNAP3A, FAM162A, MYO19, and NDUFS1). ROC curve analysis showed that the hub gene and logistic regression model presented excellent diagnostic and discriminating abilities. Immune infiltration and consensus clustering analysis indicated that hub MERGs were highly correlated with various types of immune cells, and there were significant differences in immune cells and hub MERGs among different periodontitis subtypes. Conclusion The periodontitis classification model based on MERGs shows excellent performance and can offer novel perspectives into the pathogenesis of periodontitis. The high correlation between MERGs and various immune cells and the significant differences between immune cells and MERGs in different periodontitis subtypes can clarify the regulatory roles of MitoEVs in the immune microenvironment of periodontitis. Future research should focus on elucidating the functional mechanisms of hub MERGs and exploring potential therapeutic interventions based on these findings. |
| first_indexed | 2024-03-07T14:38:07Z |
| format | Article |
| id | doaj.art-be74a8c6758241ddbaba3be0df38bb17 |
| institution | Directory Open Access Journal |
| issn | 1472-6831 |
| language | English |
| last_indexed | 2024-03-07T14:38:07Z |
| publishDate | 2024-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Oral Health |
| spelling | doaj.art-be74a8c6758241ddbaba3be0df38bb172024-03-05T20:34:22ZengBMCBMC Oral Health1472-68312024-02-0124111610.1186/s12903-024-03912-8Exploring the potential link between MitoEVs and the immune microenvironment of periodontitis based on machine learning and bioinformatics methodsHaoran Yang0Anna Zhao1Yuxiang Chen2Tingting Cheng3Jianzhong Zhou4Ziliang Li5Affiliated Stomatology Hospital of Kunming Medical UniversityAffiliated Stomatology Hospital of Kunming Medical UniversityAffiliated Stomatology Hospital of Kunming Medical UniversityAffiliated Stomatology Hospital of Kunming Medical UniversityChuxiong Medical CollegeAffiliated Stomatology Hospital of Kunming Medical UniversityAbstract Background Periodontitis is a chronic inflammatory condition triggered by immune system malfunction. Mitochondrial extracellular vesicles (MitoEVs) are a group of highly heterogeneous extracellular vesicles (EVs) enriched in mitochondrial fractions. The objective of this research was to examine the correlation between MitoEVs and the immune microenvironment of periodontitis. Methods Data from MitoCarta 3.0, GeneCards, and GEO databases were utilized to identify differentially expressed MitoEV-related genes (MERGs) and conduct functional enrichment and pathway analyses. The random forest and LASSO algorithms were employed to identify hub MERGs. Infiltration levels of immune cells in periodontitis and healthy groups were estimated using the CIBERSORT algorithm, and phenotypic subgroups of periodontitis based on hub MERG expression levels were explored using a consensus clustering method. Results A total of 44 differentially expressed MERGs were identified. The random forest and LASSO algorithms identified 9 hub MERGs (BCL2L11, GLDC, CYP24A1, COQ2, MTPAP, NIPSNAP3A, FAM162A, MYO19, and NDUFS1). ROC curve analysis showed that the hub gene and logistic regression model presented excellent diagnostic and discriminating abilities. Immune infiltration and consensus clustering analysis indicated that hub MERGs were highly correlated with various types of immune cells, and there were significant differences in immune cells and hub MERGs among different periodontitis subtypes. Conclusion The periodontitis classification model based on MERGs shows excellent performance and can offer novel perspectives into the pathogenesis of periodontitis. The high correlation between MERGs and various immune cells and the significant differences between immune cells and MERGs in different periodontitis subtypes can clarify the regulatory roles of MitoEVs in the immune microenvironment of periodontitis. Future research should focus on elucidating the functional mechanisms of hub MERGs and exploring potential therapeutic interventions based on these findings.https://doi.org/10.1186/s12903-024-03912-8MitochondriaExtracellular vesiclesPeriodontitisImmune microenvironmentMachine learningBioinformatics |
| spellingShingle | Haoran Yang Anna Zhao Yuxiang Chen Tingting Cheng Jianzhong Zhou Ziliang Li Exploring the potential link between MitoEVs and the immune microenvironment of periodontitis based on machine learning and bioinformatics methods BMC Oral Health Mitochondria Extracellular vesicles Periodontitis Immune microenvironment Machine learning Bioinformatics |
| title | Exploring the potential link between MitoEVs and the immune microenvironment of periodontitis based on machine learning and bioinformatics methods |
| title_full | Exploring the potential link between MitoEVs and the immune microenvironment of periodontitis based on machine learning and bioinformatics methods |
| title_fullStr | Exploring the potential link between MitoEVs and the immune microenvironment of periodontitis based on machine learning and bioinformatics methods |
| title_full_unstemmed | Exploring the potential link between MitoEVs and the immune microenvironment of periodontitis based on machine learning and bioinformatics methods |
| title_short | Exploring the potential link between MitoEVs and the immune microenvironment of periodontitis based on machine learning and bioinformatics methods |
| title_sort | exploring the potential link between mitoevs and the immune microenvironment of periodontitis based on machine learning and bioinformatics methods |
| topic | Mitochondria Extracellular vesicles Periodontitis Immune microenvironment Machine learning Bioinformatics |
| url | https://doi.org/10.1186/s12903-024-03912-8 |
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