Integration of dCas9-Based Methylation Editing with GPS Identifies Dynamic Changes of mrDEGs in BRCA

Breast carcinoma (BRCA) has emerged as the leading cancer worldwide with the highest incidence. The malignant transformation of normal breast epithelial cells is a crucial prerequisite for the initiation of breast cancer. However, the underlying epigenomic mechanism remains poorly understood. Here, we utilized guide positioning sequencing (GPS) to conduct whole-genome DNA methylation analysis in an MCF10 series of cell lines, a typical model of malignant progression including normal breast epithelial MCF10A, premalignant MCF10AT, low-grade metastatic MCF10CA1h, and high-grade metastatic MCF10CA1a. By integrating mRNA-seq with matched clinical data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO), six representative methylation-related differentially expressed genes (mrDEGs) were screened, including CAVIN2, ARL4D, DUSP1, TENT5B, P3H2, and MMP28. To mimic tumor progression in vitro, we independently optimized and employed the site-specific dCas9-DNMT3L-DNMT3A system to artificially downregulate TENT5B in MCF10A cells, demonstrating that transcriptional silence of TENT5B accelerates cell proliferation in MCF10A cells owing to the crosstalk between hypermethylation and histone deacetylation. Our data highlight the practical implications of DNA methylation dynamics in reshaping epigenomic features during the BRCA malignant progression. The site-specific methylation technique would help gain a comprehensive understanding of pathogenic mechanisms and facilitate future therapeutic approaches in epigenome editing. In order to analyze the dynamic atlas of DNA methylation patterns in breast cancer progression, we conducted guide positioning sequencing (GPS) to obtain genome-wide methylation data for each CpG site on four cell lines under the same culture conditions during exponential growth phase. We then performed a comprehensive analysis of DNA methylation data using dispersion shrinkage for sequencing data (DSS) to identify DMRs in three distinct comparisons: MCF10A vs. MCF10AT, MCF10A vs. MCF10CA1h, and MCF10A vs. MCF10CA1a.