Sep 2026· Cellular and Molecular Life Sciences· 0 citations
CRISPR and Genetic Engineering
TL;DR
This review synthesizes current advances in CRISPR-based DNA methylation editing for cancer epigenomics, critically evaluates key challenges, including off-target activity, context-dependent outcomes, delivery and durability, and outlines future directions integrating locus-specific editing with multi-omics profiling, machine-learning-guided target selection, and rational combination therapies to accelerate clinical translation.
Abstract
Aberrant DNA methylation is a fundamental hallmark of cancer, characterized by widespread global hypomethylation, focal hypermethylation of key regulatory regions, and extensive disruption of gene regulatory and epigenomic networks. Despite comprehensive methylome profiling and the development of diagnostic and prognostic assays, most cancer-associated methylation changes remain purely correlational, obscuring which marks function as true drivers of tumor phenotypes. Conventional hypomethylating agents can partially reverse these alterations and are clinically effective in selected settings, but their genome-wide activity and lack of locus specificity limit mechanistic insight and precision therapeutic deployment. CRISPR-based DNA methylation editing now offers a powerful strategy to move from association to causality by enabling potentially reversible, locus-specific installation or removal of methylation marks without altering the underlying DNA sequence. By coupling catalytically inactive Cas proteins to methylation “writers” or “erasers” and leveraging modular recruitment platforms, these systems can quantitatively tune methylation at promoters, enhancers, and CpG shores to interrogate gene function, chemoresistance, and immune modulation in diverse cancer models. This review synthesizes current advances in CRISPR-based DNA methylation editing for cancer epigenomics, critically evaluates key challenges, including off-target activity, context-dependent outcomes, delivery and durability, and outlines future directions integrating locus-specific editing with multi-omics profiling, machine-learning-guided target selection, and rational combination therapies to accelerate clinical translation.
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