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• Research Paper Volume 13, Issue 15 pp 19678-19695

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

  Relevance score: 8.04797

  Pengcheng Wang, Bingbing Wang, Zheng Zhang, Zuomin Wang

  Keywords: methylation, CpGs, periodontitis, immune, diagnostic markers

  Published in Aging on August 4, 2021

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  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.

• Research Paper Volume 12, Issue 23 pp 23917-23930

  Molecular subtypes based on DNA promoter methylation predict prognosis in lung adenocarcinoma patients

  Relevance score: 6.690856

  Shanping Shi, Mingjun Xu, Yang Xi

  Keywords: lung adenocarcinoma, CpGs, DNA methylation, TCGA, the prognostic prediction model

  Published in Aging on November 24, 2020

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  Background: The heterogeneity of lung adenocarcinoma (LADC) makes the early diagnosis and treatment of the disease difficult. Gene silencing of DNA methylation is an important mechanism of tumorigenesis. A combination of methylation and clinical features can improve the classification of LADC heterogeneity.

  Results: We investigated the prognostic significance of 335 specimen subgroups of Lung adenocarcinoma based on the DNA methylation level. The differences in DNA methylation levels were related to the TNM stage classification, age, gender, and prognostic values. Seven subtypes were determined using 774 CpG sites that significantly affected the survival rate based on the consensus clustering. Finally, we constructed a prognostic model that performed well and further verified it in our test group.

  Conclusions: This study shows that classification based on DNA methylation might aid in demonstrating heterogeneity within formerly characterized LADC molecular subtypes, assisting in the development of efficient, personalized therapy.

  Methods: Methylation data of lung adenocarcinoma were downloaded from the University of California Santa Cruz (UCSC) cancer browser, and the clinical patient information and RNA-seq archives were acquired from the Cancer Genome Atlas (TCGA). CpG sites were identified based on the significant correlation with the prognosis and used further to cluster the cases uniformly into several subtypes.