Alterations of Cyclooxygenase-2 Methylation Levels Before and After Intervention Trial to Prevent Gastric Cancer in a Chinese Population

Epigenetic Control of Vascular Smooth Muscle Cell Function in Atherosclerosis: A Role for DNA Methylation

Atherosclerosis is a complex vascular inflammatory disease in which multiple cell types are involved, including vascular smooth muscle cells (VSMCs). In response to vascular injury and inflammatory stimuli, VSMCs undergo a "phenotypic switching" characterized by extracellular matrix secretion, loss of contractility, and abnormal proliferation and migration, which play a key role in the progression of atherosclerosis. DNA methylation modification is an important epigenetic mechanism that plays an important role in atherosclerosis. Studies investigating abnormal DNA methylation in patients with atherosclerosis have determined a specific DNA methylation profile, and proposed multiple pathways and genes involved in the etiopathogenesis of atherosclerosis. Recent studies have also revealed that DNA methylation modification controls VSMC function by regulating gene expression involved in atherosclerosis. In this review, we summarize the recent advances regarding the epigenetic control of VSMC function by DNA methylation in atherosclerosis and provide insights into the development of VSMC-centered therapeutic strategies.

A predictive model for assessing prognostic risks in gastric cancer patients using gene expression and methylation data

BACKGROUND

The role(s) of epigenetic reprogramming in gastric cancer (GC) remain obscure. This study was designed to identify methylated gene markers with prognostic potential for GC.

METHODS

Five datasets containing gene expression and methylation profiles from GC samples were collected from the GEO database, and subjected to meta-analysis. All five datasets were subjected to quality control and then differentially expressed genes (DEGs) and differentially expressed methylation genes (DEMGs) were selected using MetaDE. Correlations between gene expression and methylation status were analysed using Pearson coefficient correlation. Then, enrichment analyses were conducted to identify signature genes that were significantly different at both the gene expression and methylation levels. Cox regression analyses were performed to identify clinical factors and these were combined with the signature genes to create a prognosis-related predictive model. This model was then evaluated for predictive accuracy and then validated using a validation dataset.

RESULTS

This study identified 1565 DEGs and 3754 DEMGs in total. Of these, 369 were differentially expressed at both the gene and methylation levels. We identified 12 signature genes including VEGFC, FBP1, NR3C1, NFE2L2, and DFNA5 which were combined with the clinical data to produce a novel prognostic model for GC. This model could effectively split GC patients into two groups, high- and low-risk with these observations being confirmed in the validation dataset.

CONCLUSION

The differential methylation of the 12 signature genes, including VEGFC, FBP1, NR3C1, NFE2L2, and DFNA5, identified in this study may help to produce a functional predictive model for evaluating GC prognosis in clinical samples.