A Comparison of Co-methylation Relationships Between Rheumatoid Arthritis and Parkinson's Disease

Tang, Guoping; Qiu, Ren; Xu, Liangde; Feng, Rennan; Jiang, Yixiang; Kong, Fanzhi; Hu, Sen

doi:10.3389/fnins.2018.01001

Cited by 10 publications

(13 citation statements)

References 25 publications

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“…KEGG pathway analysis of DEGs demonstrated relatively high enrichment in the pathway concerning Alzheimer disease; although the enrichment in Parkinson disease was less significant, these pathways were strongly correlated with neurodegenerations. Their association with RA was supported by several publications; specifically, a Mendelian randomization study proposed a correlation between RA and Alzheimer disease (Bae and Lee, 2019 ); co-methylation relation study revealed that samples of RA and Parkinson disease shared 337 significantly altered (vs. their respective control) methylation gene pairs (Tang et al, 2018 ).…”

Section: Discussionmentioning

confidence: 85%

Coupling of Co-expression Network Analysis and Machine Learning Validation Unearthed Potential Key Genes Involved in Rheumatoid Arthritis

Xiao¹,

Wang

Cai³

et al. 2021

Front. Genet.

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Rheumatoid arthritis (RA) is an incurable disease that afflicts 0.5–1.0% of the global population though it is less threatening at its early stage. Therefore, improved diagnostic efficiency and prognostic outcome are critical for confronting RA. Although machine learning is considered a promising technique in clinical research, its potential in verifying the biological significance of gene was not fully exploited. The performance of a machine learning model depends greatly on the features used for model training; therefore, the effectiveness of prediction might reflect the quality of input features. In the present study, we used weighted gene co-expression network analysis (WGCNA) in conjunction with differentially expressed gene (DEG) analysis to select the key genes that were highly associated with RA phenotypes based on multiple microarray datasets of RA blood samples, after which they were used as features in machine learning model validation. A total of six machine learning models were used to validate the biological significance of the key genes based on gene expression, among which five models achieved good performances [area under curve (AUC) >0.85], suggesting that our currently identified key genes are biologically significant and highly representative of genes involved in RA. Combined with other biological interpretations including Gene Ontology (GO) analysis, protein–protein interaction (PPI) network analysis, as well as inference of immune cell composition, our current study might shed a light on the in-depth study of RA diagnosis and prognosis.

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Section: Discussionmentioning

confidence: 85%

Coupling of Co-expression Network Analysis and Machine Learning Validation Unearthed Potential Key Genes Involved in Rheumatoid Arthritis

Xiao¹,

Wang

Cai³

et al. 2021

Front. Genet.

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“…Although differential methylation regions (DMRs) in different autoimmune diseases are distinct, numerous of DMRs and pathways are found to be shared by different autoimmune diseases. For example, recent study identified 337 differentially methylated genes are shared by RA and Parkinson's Disease (PD) which provides new evidence for the shared biological mechanism between RA and PD (Tang et al, 2018). Further study find mitochondrial L-carnitine shuttle pathway and PTEN signaling pathway are simultaneously differentially expressed in RA, systemic sclerosis, and SLE (Hudson et al, 2017).…”

Section: Genome-wide Methylation Profiling To Identify Rheumatoid Artmentioning

confidence: 97%

Epigenetic Regulation Mediated by Methylation in the Pathogenesis and Precision Medicine of Rheumatoid Arthritis

Guo

Chang

et al. 2020

Preprint

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Rheumatoid arthritis (RA) as a complex disease is thought triggered by interaction between genetics and environment, especially the shared epitope (SE) and cell surface calreticulin (CSC) theory. However, all the evidence shows genetic diversity and environment exposure cannot explain all the clinical characteristics heterogeneity of rheumatoid arthritis. In contrast, recent studies demonstrate that epigenetics play important roles in the pathogenesis of rheumatoid arthritis, especially DNA methylation and histone modification. DNA methylation and histone methylation are involved in innate and adoptive immune cell differentiation and the migration, proliferation, apoptosis, and mesenchymal characteristics of fibroblast-like synoviocytes (FLS). Epigenetic mediated regulation to immune genes and inflammation pathway provides well explanation to dynamic expression network of rheumatoid arthritis. In this review, we summarized the comprehensive evidence to show methylation modification occurred in DNA and histone are significantly involved in the pathogenesis of rheumatoid arthritis and could be applied as the promising biomarker in the disease activity and drug response prediction. We also explained the opportunity and challenge of the current epigenetics research in rheumatoid arthritis. In summary, epigenetic modules provide possible interface through which genetic and environmental risk factors connect together to contribute to the susceptibility and pathogenesis of RA. Meanwhile epigenetic regulators provided promising drug targets to develop novel therapeutic drugs for rheumatoid arthritis. Finally, DNA methylation and histone modification will be important features to provide better rheumatoid arthritis subtype identification to accelerate personalized treatment and precision medicine.

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“…To further investigate the relationship between the tumor and normal samples, we examine how the correlation coe cient of each CG pair changes by comparing the normal with the tumor samples. Following the example of a previous publication [17], Table 5 The number of CG pairs whose correlation coe cients fall within certain intervals. ) in the normal data to very high negative correlations ([-1.0, -0.75)) in the tumor data.…”

Section: Highly Changing Cg Pairsmentioning

confidence: 99%

Thorough statistical analyses of breast cancer co-methylation patterns

Sun¹,

Dammann²,

Lai

et al. 2021

Preprint

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Background Breast cancer is one of the most commonly diagnosed cancers. It is associated with DNA methylation, an epigenetic event with a methyl group added to a cytosine paired with a guanine, i.e., a CG site. The methylation levels of different genes in a genome are correlated in certain ways that affect gene functions. This correlation pattern is known as co-methylation. It is still not clear how different genes co-methylate in the whole genome of breast cancer samples. Previous studies are conducted using relatively small datasets (Illumina 27K data). In this study, we analyze much larger datasets (Illumina 450K data). Results Our key findings are summarized below. First, normal samples have more highly correlated, or co-methylated, CG pairs than tumor samples. Both tumor and normal have more than 93% of positive co-methylation, but normal samples have significantly more negatively correlated CG sites than tumor samples (6.6% vs. 2.8%). Second, both tumor and normal samples have about 94% of co-methylated CG pairs on different chromosomes, but normal samples have 470 million more CG pairs. Highly co-methylated pairs on the same chromosome tend to be close to each other. Third, a small proportion of CG sites’ co-methylation patterns change dramatically from normal to tumor. The percentage of differentially methylated (DM) sites among them is larger than the overall DM rate. Fourth, certain CG sites are highly correlated with many CG sites; the top 100 of such super-connector CG sites in tumor and normal samples have no overlaps. Fifth, both highly changing sites and super-connector sites’ locations are significantly different from the genome-wide CG sites’ locations. Sixth, chromosome X co-methylation patterns are very different from other chromosomes. Finally, the network analyses of genes associated with several sets of co-methylated CG sites identified above show that tumor and normal samples have different patterns. Conclusions Our findings will provide researchers with a new understanding of co-methylation patterns in breast cancer. Our ability to thoroughly analyze co-methylation of large datasets will allow researchers to study relationships and associations between different genes in breast cancer.

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A Comparison of Co-methylation Relationships Between Rheumatoid Arthritis and Parkinson's Disease

Cited by 10 publications

References 25 publications

Coupling of Co-expression Network Analysis and Machine Learning Validation Unearthed Potential Key Genes Involved in Rheumatoid Arthritis

Coupling of Co-expression Network Analysis and Machine Learning Validation Unearthed Potential Key Genes Involved in Rheumatoid Arthritis

Epigenetic Regulation Mediated by Methylation in the Pathogenesis and Precision Medicine of Rheumatoid Arthritis

Thorough statistical analyses of breast cancer co-methylation patterns

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