# Exploring the Impact of DNA Methylation on Gene Expression in CRC: A Computational Approach for Identifying Epigenetically Regulated Genes in Multi-Omic Datasets

## TL;DR

This study uses computational methods to find genes affected by DNA methylation in colorectal cancer, aiming to improve patient classification and identify potential biomarkers.

## Contribution

A promoter-centric, regression-based framework is proposed to identify epigenetically regulated genes in CRC using multi-omic data.

## Key findings

- The framework identifies genes with methylation-expression associations enriched in CRC-related pathways.
- The method is validated across multiple cancer types, showing robustness and generalizability.
- Candidate genes are prioritized for biomarker discovery and epigenetic therapy studies.

## Abstract

DNA methylation, a process that controls how genes are turned on or off, can become disrupted in colorectal cancer and contribute to tumor development. Some tumors show widespread methylation changes that may silence key genes involved in cell regulation. This study aims to systematically identify genes whose activity is influenced by DNA methylation by integrating large-scale molecular data from colorectal cancer patients. Different computational methods were compared to determine the most effective approach for detecting methylation-related changes in gene expression, and the results were validated in other cancer types. The findings establish a promoter-centric, regression-based framework that prioritizes candidate genes whose expression variability is strongly explained by promoter methylation across CIMP-stratified tumors, which could: (i) refine molecular subclassification of patients beyond traditional CIMP status, (ii) identify candidate diagnostic or prognostic methylation-based biomarkers, and (iii) prioritize genes for functional validation in epigenetic therapy studies.

Background/Objectives: DNA methylation is a key epigenetic process that regulates gene expression and is often disrupted in colorectal cancer (CRC). Aberrant methylation of promoter CpG islands can silence tumor suppressor genes and drive tumorigenesis. A subset of CRCs exhibits the CpG Island Methylator Phenotype (CIMP), characterized by widespread hypermethylation and distinct clinical outcomes. Identifying genes whose expression is epigenetically regulated by methylation is important for prioritizing candidate biomarkers and therapeutic targets in CRC. Methods: We developed and compared a series of computational approaches to identify genes whose expression is regulated by DNA methylation in The Cancer Genome Atlas (TCGA) cohort of Colon Adenocarcinoma (COAD) patients. Samples were stratified according to their CpG Island Methylator Phenotype (CIMP) level to capture distinct epigenetic subgroups. The proposed framework integrates methylation and transcriptomic data to systematically detect methylation–expression associations indicative of epigenetic regulation. Results: The best-performing method identified gene sets strongly associated with promoter methylation–expression relationships and enriched for pathways relevant to colorectal cancer progression and patient stratification. To evaluate the robustness and transferability of the approach, it was further validated on independent datasets, including Stomach Adenocarcinoma (STAD), Glioblastoma Multiforme (GBM), and Mesothelioma (MESO), supporting its robustness and potential generalizability across multiple tumor types. Conclusions: Our study highlights the potential of computational pipelines to uncover epigenetically regulated genes in colorectal cancer. The identified candidate genes provide a hypothesis-generating foundation for refining molecular stratification and guiding future studies aimed at epigenetic biomarker discovery and therapeutic hypothesis development.