A Bayesian hierarchical model to estimate DNA methylation conservation in colorectal tumors

EpigenPlot: An interactive web platform for DNA methylation-based biomarker and drug target discovery in colorectal cancer

BACKGROUND AND PURPOSE

Genome-wide methylation studies have significantly advanced our understanding of colorectal adenocarcinoma progression and biomarker discovery. Aberrant DNA methylation plays a crucial role in gene expression regulation during cancer transformation, highlighting the need to identify differentially methylated regions (DMRs) as potential diagnostic and therapeutic markers. However, an integrated resource to explore and validate methylation alterations across colorectal cancer stages has been lacking. We aimed to develop a platform that integrates existing methylation data, systematically identifies DMRs and provides a tool for further investigation.

EXPERIMENTAL APPROACH

We created a database combining Illumina HumanMethylation450K and EPIC data from normal colon, adenoma and adenocarcinoma tissues, comprising 2346 samples from 19 datasets. Methylation levels were analysed in six gene regions, and comparisons between tissue types were made using Mann-Whitney, Kruskal-Wallis and ROC tests.

KEY RESULTS

Both adenoma and adenocarcinoma samples exhibited a general decrease in methylation compared to healthy tissue. Differential methylation in genes such as ITGA4, NPY, IGFL1 and LRRC4 was validated. The strongest DMRs were observed in the C1orf70 gene's 5'UTR and TSS200 regions, with AUC values of 0.98 in both of the HM450K and EPIC datasets. We established an interactive web-based platform accessible at https://epigenplot.com/ enabling future analysis of individual gene regions.

CONCLUSIONS AND IMPLICATIONS

Our study provides an integrated database of DNA methylation profiles across normal, adenoma and adenocarcinoma tissues, offering a valuable resource for biomarker discovery. The integrated web platform can serve as a tool for the development of methylation-based therapies in the future.

Cigarette Smoking and E-cigarette Use Induce Shared DNA Methylation Changes Linked to Carcinogenesis

Tobacco use is a major modifiable risk factor for adverse health outcomes, including cancer, and elicits profound epigenetic changes thought to be associated with long-term cancer risk. While electronic cigarettes (e-cigarettes) have been advocated as harm reduction alternatives to tobacco products, recent studies have revealed potential detrimental effects, highlighting the urgent need for further research into the molecular and health impacts of e-cigarettes. Here, we applied computational deconvolution methods to dissect the cell- and tissue-specific epigenetic effects of tobacco or e-cigarette use on DNA methylation (DNAme) in over 3,500 buccal/saliva, cervical, or blood samples, spanning epithelial and immune cells at directly and indirectly exposed sites. The 535 identified smoking-related DNAme loci [cytosine-phosphate-guanine sites (CpG)] clustered into four functional groups, including detoxification or growth signaling, based on cell type and anatomic site. Loci hypermethylated in buccal epithelial cells of smokers associated with NOTCH1/RUNX3/growth factor receptor signaling also exhibited elevated methylation in cancer tissue and progressing lung carcinoma in situ lesions, and hypermethylation of these sites predicted lung cancer development in buccal samples collected from smokers up to 22 years prior to diagnosis, suggesting a potential role in driving carcinogenesis. Alarmingly, these CpGs were also hypermethylated in e-cigarette users with a limited smoking history. This study sheds light on the cell type-specific changes to the epigenetic landscape induced by smoking-related products.

SIGNIFICANCE

The use of both cigarettes and e-cigarettes elicits cell- and exposure-specific epigenetic effects that are predictive of carcinogenesis, suggesting caution when broadly recommending e-cigarettes as aids for smoking cessation.

Multi-View Clustering for Integration of Gene Expression and Methylation Data With Tensor Decomposition and Self-Representation Learning

The accumulated DNA methylation and gene expression provide a great opportunity to exploit the epigenetic patterns of genes, which is the foundation for revealing the underlying mechanisms of biological systems. Current integrative algorithms are criticized for undesirable performance because they fail to address the heterogeneity of expression and methylation data, and the intrinsic relations among them. To solve this issue, a novel multi-view clustering with self-representation learning and low-rank tensor constraint (MCSL-LTC) is proposed for the integration of gene expression and DNA methylation data, which are treated as complementary views. Specifically, MCSL-LTC first learns the low-dimensional features for each view with the linear projection, and then these features are fused in a unified tensor space with low-rank constraints. In this case, the complementary information of various views is precisely captured, where the heterogeneity of omic data is avoided, thereby enhancing the consistency of different views. Finally, MCSL-LTC obtains a consensus cluster of genes reflecting the structure and features of various views. Experimental results demonstrate that the proposed approach outperforms state-of-the-art baselines in terms of accuracy on both the social and cancer data, which provides an effective and efficient method for the integration of heterogeneous genomic data.

Four methylation-driven genes detected by linear discriminant analysis model from early-stage colorectal cancer and their methylation levels in cell-free DNA

The process of colorectal cancer (CRC) formation is considered a typical model of multistage carcinogenesis in which aberrant DNA methylation plays an important role. In this study, 752 methylation-driven genes (MDGs) were identified by the MethylMix package based on methylation and gene expression data of CRC in The Cancer Genome Atlas (TCGA). Iterative recursive feature elimination (iRFE) based on linear discriminant analysis (LDA) was used to determine the minimum MDGs (iRFE MDGs), which could distinguish between cancer and cancer-adjacent tissues. Further analysis indicated that the changes in methylation levels of the four iRFE MDGs, ADHFE1-Cluster1, CNRIP1-Cluster1, MAFB, and TNS4, occurred in adenoma tissues, while changes did not occur until stage IV in cell-free DNA. Furthermore, the methylation levels of iRFE MDGs were correlated with the genes involved in the reprogramming process of somatic cells to pluripotent stem cells, which is considered the common signature of cancer cells and embryonic stem cells. The above results indicated that the four iRFE MDGs may play roles in the early stage of colorectal carcinogenesis and highlighted the complicated relationship between tissue DNA and cell-free DNA (cfDNA).