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

Abstract

Evidence from past research has shown that DNA methylation plays a key role in the pathogenesis of periodontitis, regulating gene expression levels and thereby affecting the occurrence of various diseases. Three sample sets of methylation data and gene expression data were downloaded from Gene Expression Omnibus (GEO) database. A diagnostic classifier is established based on gene expression data and CpG methylation data. Abnormal expression of immune-related pathways and methyltransferase-related genes in patients with periodontitis was detected. A total of 8,029 differentially expressed CpG (DMP) was annotated to the promoter region of 4,940 genes, of which 295 immune genes were significantly enriched. The CpG sites of 23 differentially co-expressed immune gene promoter regions were identified, and 13 CpG were generally hypermethylated in healthy group samples, while some were methylated in most patients. Five CpGs were screened as robust periodontitis biomarkers. The accuracy in the training data set, the two external verification data sets, and in the transcriptome was 95.5%, 80% and 78.3%, and 82.6%, respectively. This study provided new features for the diagnosis of periodontitis, and contributed to the personalized treatment of periodontitis.

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

“…In this study, we used 13 inflamed vs 11 normal microarray datasets to carry out WGCNA. The original research containing above two data sets, finished by Galicia et al [ 26 ] and Huang et al [ 27 ], revealed the diversities of genetic expression profiles between inflamed and normal pulps, and indicated that pulpitis was related to differential genetic expression, which was associated with immune system process and cardiac ventricle development [ 27 ], as well as Beta2 integrin cell surface interactions [ 11 ]. The important differences between the modules in this paper and the outcomes from the research of Galicia et al [ 26 ] and Huang et al [ 27 ] was that our research more systematically applied analysis approach, WGCNA, determining the darkgray module as the most relative module with pulpitis with high enrichment in methyltransferase activity, triglyceride metabolism and positive modulation of pri-miRNA transcription from RNA polymerase II promotor.…”

“…WGCNA is applied to integrate gene expression and examine potential mechanisms of gene networks [ 9 ]. WGCNA is utilized to study the co-expression modules, genetic network hallmark, and core genes participating in certain oral disease, like Sjögren’s syndrome [ 10 ] and periodontitis [ 11 ]. Therefore, WGCNA has potential to effectively process data to determine function pathways and promising markers in and identify the hub genes and gene-network signature of pulpitis [ 12 ].…”

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

“…In the present study, we applied an innovative combination of RF and ANN algorithms to the early diagnosis of periodontitis, demonstrating excellent diagnostic performance in a large cohort. Although DNA methylation biomarkers and miRNA-based models for periodontitis classification have been developed, these studies used small sample sizes and showed modest performance ( Jin et al, 2020 ; Wang et al, 2021 ). In addition, we analyzed the association of key biomarkers with inflammatory processes and explained the rationality of marker selection.…”

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

“…This disease begins with damage to periodontal tissue, including gingival recession, loss of attachment, and irreversible alveolar bone resorption 3 . If left untreated, periodontitis will lead to tooth movement and subsequent tooth loss 4 . In addition, periodontitis is associated with systemic diseases, such as cardiovascular diseases, 5 Alzheimer's disease, 6 diabetes and insulin resistance among others 7 .…”