Bioinformatic analysis and experimental validation identified DNA methylation–Related biomarkers and immune-cell infiltration of atherosclerosis

Abstract

Background: DNA methylation is an important form of epigenetic regulation and is closely related to atherosclerosis (AS). The purpose of this study was to identify DNA methylation–related biomarkers and explore the immune-infiltrate characteristics of AS based on methylation data.Methods: DNA methylation data of 15 atherosclerotic and paired healthy tissues were obtained from Gene Expression Omnibus database. Differential methylation positions (DMPs) and differential methylation regions (DMRs) were screened by the ChAMP R package. The methylation levels of DMPs located on CpG islands of gene promoter regions were averaged. The limma R package was used to screen differentially methylated genes in the CpG islands of the promoter regions. The diagnostic values of the methylation levels were evaluated using the pROC R package. The EpiDISH algorithm was applied to quantify the infiltration levels of seven types of immune cells. Subsequently, three pairs of clinical specimens of coronary atherosclerosis with Stary’s pathological stage III were collected, and the methylation levels were detected by the methylation-specific PCR (MS-PCR) assay. Western blot was performed to detect the protein expression levels of monocyte markers.Results: A total of 110, 695 DMPs, and 918 DMRs were screened in the whole genome. Also, six genes with significant methylation differences in the CpG islands of the promoter regions were identified, including 49 DMPs. In total, three genes (GRIK2, HOXA2, and HOXA3) had delta beta greater than 0.2. The infiltration level of monocytes was significantly upregulated in AS tissues. MS-PCR assay confirmed the methylation status of the aforementioned three genes in AS samples. The Western blot results showed that the expression levels of the monocyte marker CD14 and M1-type macrophage marker CD86 were significantly increased in AS while M2-type macrophage marker protein CD206 was significantly decreased.Conclusion: This study identified potential DNA methylation–related biomarkers and revealed the role of monocytes in early AS.

Transcriptome Sequencing Reveals Autophagy Networks in Rat Livers during the Development of NAFLD and Identifies Autophagy Hub Genes

“…It plays a crucial role in regulating the proliferation and differentiation of embryonic and adult cells by binding to specific sites on DNA and initiating the transcription of target genes. Studies have shown that the expression of the Hoxa2 in liver cancer samples is significantly upregulated [ 45 ], and in the progression of mild fibrosis and severe fibrosis, Hoxa2 DNA methylation is significantly increased [ 46 ]. These results suggested that Hoxa2 might play a role in promoting the progression of hepatocellular carcinoma and become a potential target for the treatment of hepatocellular carcinoma.…”

Identification of ferroptosis related markers by integrated bioinformatics analysis and In vitro model experiments in rheumatoid arthritis

“…The overlapping genes obtaining from the results of the two algorithms were are preliminarily identified as the ferroptosis-related biomarkers, which were validated by the GSE77298 and GSE93272 dataset. Using the pROC package [ 26 ] (version 1.18.0), the receiver operating characteristic (ROC) curve were drew to evaluate its diagnostic value.…”

Methods in DNA methylation array dataset analysis: A review

“…The major algorithms, which are among the most popularly used ones in published research for pre-processing of the raw data include SWAN-subset-quantile within array normalisation [53] , [54] , [55] , FunNorm- Functional normalization [54] , [56] , pQuantile-stratified quantile normalization [57] , noob-normal-exponential using out-of-band probes [54] , [58] , RCP-Regression on Correlated Probes [59] , BMIQ-Beta-Mixture Quantile Method [50] , [60] , [61] , [62] , [63] and the combination of noob and BMIQ, that was shown to give better performance than others [58] . Further detailing of the normalization algorithms and packages can be found in Supplementary Table III.…”