Comprehensive Analysis of the Mechanism of Periodontitis-Related mRNA Expression Combined with Upstream Methylation and ceRNA Regulation

Yuan, Yifang; Zhu, Bo; Xu, Shuai; Chen, Xiaotao

doi:10.1089/gtmb.2021.0090

Cited by 1 publication

(2 citation statements)

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Section: Discussionmentioning

confidence: 99%

“…At present, there are three main comprehensive gene expression/transcriptome profiles for healthy and periodontal gingival tissues, GSE10334, GSE16134, and GSE23586, in the GEO database. Scientists have utilized these datasets to identify specific biological processes involved in periodontitis and analyze significantly DEGs that belong to pathological pathways in the disease (31). Additionally, Kim H et al investigated differential DNA methylation in gingival tissues of periodontal health, gingivitis, and periodontitis and its association with differential mRNA expression (32), and Richter et al identified biologically active methylation marks of the oral masticatory mucosa by an epigenome-wide association study (EWAS) (33).…”

Section: Discussionmentioning

confidence: 99%

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Identification of nine signature proteins involved in periodontitis by integrated analysis of TMT proteomics and transcriptomics

et al. 2022

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Recently, there are many researches on signature molecules of periodontitis derived from different periodontal tissues to determine the disease occurrence and development, and deepen the understanding of this complex disease. Among them, a variety of omics techniques have been utilized to analyze periodontitis pathology and progression. However, few accurate signature molecules are known and available. Herein, we aimed to screened and identified signature molecules suitable for distinguishing periodontitis patients using machine learning models by integrated analysis of TMT proteomics and transcriptomics with the purpose of finding novel prediction or diagnosis targets. Differential protein profiles, functional enrichment analysis, and protein–protein interaction network analysis were conducted based on TMT proteomics of 15 gingival tissues from healthy and periodontitis patients. DEPs correlating with periodontitis were screened using LASSO regression. We constructed a new diagnostic model using an artificial neural network (ANN) and verified its efficacy based on periodontitis transcriptomics datasets (GSE10334 and GSE16134). Western blotting validated expression levels of hub DEPs. TMT proteomics revealed 5658 proteins and 115 DEPs, and the 115 DEPs are closely related to inflammation and immune activity. Nine hub DEPs were screened by LASSO, and the ANN model distinguished healthy from periodontitis patients. The model showed satisfactory classification ability for both training (AUC=0.972) and validation (AUC=0.881) cohorts by ROC analysis. Expression levels of the 9 hub DEPs were validated and consistent with TMT proteomics quantitation. Our work reveals that nine hub DEPs in gingival tissues are closely related to the occurrence and progression of periodontitis and are potential signature molecules involved in periodontitis.

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Section: Discussionmentioning

confidence: 99%