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Application of deep learning in cancer epigenetics through DNA methylation analysis.

ABSTRACT: DNA methylation is a fundamental epigenetic modification involved in various biological processes and diseases. Analysis of DNA methylation data at a genome-wide and high-throughput level can provide insights into diseases influenced by epigenetics, such as cancer. Recent technological advances have led to the development of high-throughput approaches, such as genome-scale profiling, that allow for computational analysis of epigenetics. Deep learning (DL) methods are essential in facilitating computational studies in epigenetics for DNA methylation analysis. In this systematic review, we assessed the various applications of DL applied to DNA methylation data or multi-omics data to discover cancer biomarkers, perform classification, imputation and survival analysis. The review first introduces state-of-the-art DL architectures and highlights their usefulness in addressing challenges related to cancer epigenetics. Finally, the review discusses potential limitations and future research directions in this field.

SUBMITTER: Yassi M

PROVIDER: S-EPMC10661960 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

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Application of deep learning in cancer epigenetics through DNA methylation analysis.

Yassi Maryam M Chatterjee Aniruddha A Parry Matthew M

Briefings in bioinformatics 20230901 6

DNA methylation is a fundamental epigenetic modification involved in various biological processes and diseases. Analysis of DNA methylation data at a genome-wide and high-throughput level can provide insights into diseases influenced by epigenetics, such as cancer. Recent technological advances have led to the development of high-throughput approaches, such as genome-scale profiling, that allow for computational analysis of epigenetics. Deep learning (DL) methods are essential in facilitating co ...[more]

PMID: 37985455

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Project description:Recent research suggests that epigenetics, especially DNA methylation, plays a mechanistic role in aging. Epigenetic clocks, which measure changes in a few hundred specific CpG sites, can accurately predict chronological age in a variety of species, including humans. These clocks are currently the best biomarkers for predicting mortality in humans. Additionally, several studies have characterized the effects of aging across the methylome in a wide variety of tissues from humans and mice. A small fraction (~2%) of the CpG sites show age-related changes, either hypermethylation or hypomethylation with aging. Evaluation of non-CpG site methylation has only been examined in a few studies, with about ~0.5% of these sites showing a change with age. Therefore, while only a small fraction of cytosines in the genome show changes in DNA methylation with age, this represents 2 to 3 million cytosines in the genome. Importantly, the only study to compare the effect of aging on DNA methylation in male and female mice and humans found that >95% of the age-related changes in DNA methylation in the hippocampus were sexually divergent, i.e., the methylation did not differ between males and females at young age but age-related changes occurred in one sex but not the other. The age-related changes in DNA methylation tend to be enriched and under-represented in specific genomic contexts, with some commonalities between tissues and species that require further investigation. The strongest evidence that the age-related changes in DNA methylation play a role in aging comes from studies of anti-aging interventions (e.g., caloric restriction, dwarfism, and rapamycin treatment) in mice. These anti-aging interventions deaccelerate the epigenetic clocks and reverse/prevent 20 to 40% of the age-related changes in DNA methylation. It will be important in the future to demonstrate that at least some of the age-related changes in DNA methylation directly lead to alterations in the transcriptome of cells/tissues that could potentially contribute to aging.

Dataset Information

Application of deep learning in cancer epigenetics through DNA methylation analysis.

Publications

Application of deep learning in cancer epigenetics through DNA methylation analysis.

Similar Datasets

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