Identification of inflammation-related DNA methylation biomarkers in periodontitis patients based on weighted co-expression analysis

Methods in DNA methylation array dataset analysis: A review

Understanding the intricate relationships between gene expression levels and epigenetic modifications in a genome is crucial to comprehending the pathogenic mechanisms of many diseases. With the advancement of DNA Methylome Profiling techniques, the emphasis on identifying Differentially Methylated Regions (DMRs/DMGs) has become crucial for biomarker discovery, offering new insights into the etiology of illnesses. This review surveys the current state of computational tools/algorithms for the analysis of microarray-based DNA methylation profiling datasets, focusing on key concepts underlying the diagnostic/prognostic CpG site extraction. It addresses methodological frameworks, algorithms, and pipelines employed by various authors, serving as a roadmap to address challenges and understand changing trends in the methodologies for analyzing array-based DNA methylation profiling datasets derived from diseased genomes. Additionally, it highlights the importance of integrating gene expression and methylation datasets for accurate biomarker identification, explores prognostic prediction models, and discusses molecular subtyping for disease classification. The review also emphasizes the contributions of machine learning, neural networks, and data mining to enhance diagnostic workflow development, thereby improving accuracy, precision, and robustness.

The Genetic Aspects of Periodontitis Pathogenesis and the Regenerative Properties of Stem Cells

Periodontitis (PD) is a prevalent and chronic inflammatory disease with a complex pathogenesis, and it is associated with the presence of specific pathogens, such as Porphyromonas gingivalis. Dysbiosis and dysregulated immune responses ultimately lead to chronic inflammation as well as tooth and alveolar bone loss. Multiple studies have demonstrated that genetic polymorphisms may increase the susceptibility to PD. Furthermore, gene expression is modulated by various epigenetic mechanisms, such as DNA methylation, histone modifications, or the activity of non-coding RNA. These processes can also be induced by PD-associated pathogens. In this review, we try to summarize the genetic processes that are implicated in the pathogenesis of PD. Furthermore, we discuss the use of these mechanisms in diagnosis and therapeutic purposes. Importantly, novel treatment methods that could promote tissue regeneration are greatly needed in PD. In this paper, we also demonstrate current evidence on the potential use of stem cells and extracellular vesicles to stimulate tissue regeneration and suppress inflammation. The understanding of the molecular mechanisms involved in the pathogenesis of PD, as well as the impact of PD-associated bacteria and stem cells in these processes, may enhance future research and ultimately improve long-term treatment outcomes.

The rosetta stone of successful ageing: does oral health have a role?

Ageing is an inevitable aspect of life and thus successful ageing is an important focus of recent scientific efforts. The biological process of ageing is mediated through the interaction of genes with environmental factors, increasing the body's susceptibility to insults. Elucidating this process will increase our ability to prevent and treat age-related disease and consequently extend life expectancy. Notably, centenarians offer a unique perspective on the phenomenon of ageing. Current research highlights several age-associated alterations on the genetic, epigenetic and proteomic level. Consequently, nutrient sensing and mitochondrial function are altered, resulting in inflammation and exhaustion of regenerative ability.Oral health, an important contributor to overall health, remains underexplored in the context of extreme longevity. Good masticatory function ensures sufficient nutrient uptake, reducing morbidity and mortality in old age. The relationship between periodontal disease and systemic inflammatory pathologies is well established. Diabetes, rheumatoid arthritis and cardiovascular disease are among the most significant disease burdens influenced by inflammatory oral health conditions. Evidence suggests that the interaction is bi-directional, impacting progression, severity and mortality. Current models of ageing and longevity neglect an important factor in overall health and well-being, a gap that this review intends to illustrate and inspire avenues for future research.

Immune infiltration and diagnostic value of immune-related genes in periodontitis using bioinformatics analysis

BACKGROUND AND OBJECTIVES

Periodontitis, which is a chronic inflammatory periodontal disease resulting in destroyed periodontal tissue, is the leading cause of tooth loss in adults. Many studies have found that inflammatory immune responses are involved in the risk of periodontal tissue damage. Therefore, we analyzed the association between immunity and periodontitis using bioinformatics methods to further understand this disease.

MATERIALS AND METHODS

First, the expression profiles of periodontitis and healthy samples were downloaded from the GEO database, including a training dataset GSE16134 and an external validation dataset GSE10334. Then, differentially expressed genes were identified using the limma package. Subsequently, immune cell infiltration was calculated by using the CIBERSORT algorithm. We further identified genes linking periodontitis and immunity from the ImmPort and DisGeNet databases. In addition, some of them were selected to construct a diagnostic model via a logistic stepwise regression analysis.

RESULTS AND CONCLUSIONS

Two hundred sixty differentially expressed genes were identified and found to be involved in responses to bacterial and immune-related processes. Subsequently, immune cell infiltration analysis demonstrates significant differences in the abundance of most immune cells between periodontitis and healthy samples, especially in plasma cells. These results suggested that immunity doses play a non-negligible role in periodontitis. Twenty-one genes linking periodontitis and immunity were further identified. And nine hub genes of them were identified that may be key genes involved in the development of periodontitis. Gene ontology analyses showed that these genes are involved in response to molecules of bacterial origin, cell chemotaxis, and response to chemokines. In addition, three genes of them were selected to construct a diagnostic model. And its good diagnostic performance was demonstrated by the receiver operating characteristic curves, with an area under the curve of 0.9424 for the training dataset and 0.9244 for the external validation dataset.

Identification of key module and hub genes in pulpitis using weighted gene co-expression network analysis

BACKGROUND

Pulpitis is a common disease mainly caused by bacteria. Conventional approaches of diagnosing the state of dental pulp are mainly based on clinical symptoms, thereby harbor deficiencies. The accurate and rapid diagnosis of pulpitis is important for choosing the suitable therapy. The study aimed to identify pulpits related key genes by integrating micro-array data analysis and systems biology network-based methods such as weighted gene co-expression network analysis (WGCNA).

METHODS

The micro-array data of 13 inflamed pulp and 11 normal pulp were acquired from Gene Expression Omnibus (GEO). WGCNA was utilized to establish a genetic network and categorize genes into diverse modules. Hub genes in the most associated module to pulpitis were screened out using high module group members (MM) methods. Pulpitis model in rat was constructed and iRoot BP plus was applied to cap pulp. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was used for validation of hub genes.

RESULTS

WGCNA was established and genes were categorized into 22 modules. The darkgrey module had the highest correlation with pulpitis among them. A total of 5 hub genes (HMOX1, LOX, ACTG1, STAT3, GNB5) were identified. RT-qPCR proved the differences in expression levels of HMOX1, LOX, ACTG1, STAT3, GNB5 in inflamed dental pulp. Pulp capping reversed the expression level of HMOX1, LOX, ACTG1.

CONCLUSION

The study was the first to produce a holistic view of pulpitis, screen out and validate hub genes involved in pulpitis using WGCNA method. Pulp capping using iRoot BP plus could reverse partial hub genes.

Construction of artificial neural network diagnostic model and analysis of immune infiltration for periodontitis

Background: Periodontitis is a chronic inflammatory disease leading to tooth loss in severe cases, and early diagnosis is essential for periodontitis prevention. This study aimed to construct a diagnostic model for periodontitis using a random forest algorithm and an artificial neural network (ANN). Methods: Gene expression data of two large cohorts of patients with periodontitis, GSE10334 and GSE16134, were downloaded from the Gene Expression Omnibus database. We screened for differentially expressed genes in the GSE10334 cohort, identified key periodontitis biomarkers using a Random Forest algorithm, and constructed a classification artificial neural network model, using receiver operating characteristic curves to evaluate its diagnostic utility. Furthermore, patients with periodontitis were classified using a consensus clustering algorithm. The immune infiltration landscape was assessed using CIBERSOFT and single-sample Gene Set Enrichment Analysis. Results: A total of 153 differentially expressed genes were identified, of which 42 were downregulated. We utilized 13 key biomarkers to establish a periodontitis diagnostic model. The model had good predictive performance, with an area under the receiver operative characteristic curve (AUC) of 0.945. The independent cohort (GSE16134) was used to further validate the model's accuracy, showing an area under the receiver operative characteristic curve of 0.900. The proportion of plasma cells was highest in samples from patients with period ontitis, and 13 biomarkers were closely related to immunity. Two molecular subgroups were defined in periodontitis, with one cluster suggesting elevated levels of immune infiltration and immune function. Conclusion: We successfully identified key biomarkers of periodontitis using machine learning and developed a satisfactory diagnostic model. Our model may provide a valuable reference for the prevention and early detection of periodontitis.

Screening of periodontitis-related diagnostic biomarkers based on weighted gene correlation network analysis and machine algorithms

BACKGROUND

Periodontitis is a common oral immune inflammatory disease and early detection plays an important role in its prevention and progression. However, there are no accurate biomarkers for early diagnosis.

OBJECTIVE

This study screened periodontitis-related diagnostic biomarkers based on weighted gene correlation network analysis and machine algorithms.

METHODS

Transcriptome data and sample information of periodontitis and normal samples were obtained from the Gene Expression Omnibus (GEO) database, and key genes of disease-related modules were obtained by bioinformatics. The key genes were subjected to Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and 5 machine algorithms: Logistic Regression (LR), Random Forest (RF), Gradient Boosting Decisio Tree (GBDT), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM). Expression and correlation analysis were performed after screening the optimal model and diagnostic biomarkers.

RESULTS

A total of 47 candidate genes were obtained, and the LR model had the best diagnostic efficiency. The COL15A1, ICAM2, SLC15A2, and PIP5K1B were diagnostic biomarkers for periodontitis, and all of which were upregulated in periodontitis samples. In addition, the high expression of periodontitis biomarkers promotes positive function with immune cells.

CONCLUSION

COL15A1, ICAM2, SLC15A2 and PIP5K1B are potential diagnostic biomarkers of periodontitis.