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

Our plain-English summary of Exploring the Impact of DNA Methylation on Gene Expression in CRC: A Computational Approach for Identifying Epigenetically Regulated Genes in Multi-Omic Datasets, by A.S. Blindu, S. Berardelli, F. De Paoli, et al (Cancers, 2026).

The study

• Authors: A.S. Blindu, S. Berardelli, F. De Paoli, F. Manai, R. Tricarico, S. Zucca, P. Magni
• Journal: Cancers
• Published: 2026;18(2)
• Read it: PubMed · Publisher · Free full text

Abstract

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.

In plain English

This computational study examined DNA methylation, a chemical tag on DNA, and gene activity in tumors. It did not study asbestos or any workplace exposure. Using public Cancer Genome Atlas data, the authors looked for genes whose activity appeared linked to methylation in their control regions. They compared several methods on a colon adenocarcinoma dataset of 292 primary tumors, analyzing 43 highly methylated (CIMP-H) against 102 Non-CIMP tumors. They then applied the chosen method to stomach adenocarcinoma, glioblastoma and mesothelioma data. Of 87 mesothelioma tumors, 23 CIMP-H and 26 Non-CIMP were compared. The selected regression method identified 39 colon cancer genes, with MLH1 scoring highest (adjusted R2 of 0.916). It identified 23 stomach cancer genes, 11 of them shared with colon cancer. In mesothelioma, five genes were listed, led by NMNAT3, and none overlapped with the gastrointestinal genes. The authors stated that correlation does not establish causality and that mesothelioma had very few CIMP-H cases, so its results should be considered exploratory. They concluded that the framework is a hypothesis-generating tool, not a diagnostic or predictive model.

We wrote this plain-English summary from the full article with the help of AI, and a second AI model checked each sentence against the article. It is not the authors' wording; the abstract and the article are their own account.

What the study did and found

• Design: Computational analysis of publicly available tumor DNA methylation and gene expression data, described by the authors as an unsupervised discovery framework
• Who was studied: Primary tumor samples from The Cancer Genome Atlas, from the 29 March 2024 data release. Colon adenocarcinoma (COAD) was the primary cohort: 292 primary tumors (43 CIMP-H, 116 CIMP-L, 102 Non-CIMP). Stomach adenocarcinoma (STAD, 373), glioblastoma multiforme (GBM, 60) and mesothelioma (MESO, 87 primary tumors: 23 CIMP-H, 24 CIMP-L, 26 Non-CIMP) were used for validation. Analysis was restricted to the CIMP-H and Non-CIMP groups, so in COAD 43 CIMP-H and 102 Non-CIMP tumors were compared, and in MESO 23 CIMP-H and 26 Non-CIMP.
• Exposure: No asbestos or other external exposure was studied. The factor examined was DNA methylation at gene promoter CpG sites (Illumina 450K array) in relation to gene expression (RNA-seq), comparing CIMP-H with Non-CIMP tumors.
• Main results:
  • In colon adenocarcinoma, 956 genes were significantly downregulated and 394 upregulated in CIMP-H compared with Non-CIMP tumors.
  • The Regression-single method was selected. In colon adenocarcinoma it identified 39 genes, with 95% overlap with the other methods. MLH1 had the highest adjusted R2, 0.916.
  • 11 genes overlapped between the 39 colon adenocarcinoma genes and the 23 stomach adenocarcinoma genes.
  • In the mesothelioma dataset, five genes were listed: NMNAT3 (adjusted R2 0.7119), TMEM220, RNF208, OCIAD2 and CMBL.
  • None of the genes identified in the colon and stomach datasets overlapped with those identified in the glioblastoma and mesothelioma datasets.
  • 69.8% of CIMP-H colon adenocarcinoma samples and 69.2% of CIMP-H stomach adenocarcinoma samples were classified as MSI-H.
• The authors' conclusion: The authors concluded that their framework should be read as a hypothesis-generating tool for finding genes regulated by methylation, not as a diagnostic or predictive model.
• Limitations the authors state:
  • Correlation does not establish causality. Associations can arise from confounding factors, so some candidate genes may be false positives.
  • Small datasets and an imbalance between CIMP-H and Non-CIMP cases.
  • The mesothelioma dataset had very few CIMP-H cases. The glioblastoma dataset had only 3 CIMP-H samples. Conclusions for these two cancers should be considered exploratory.
  • The 450K methylation array covers only a small fraction of CpG sites, so some contributions may be missed.
  • Batch effects and covariates such as sex were not explicitly modeled.
  • There is no gold-standard gene set, so sensitivity, specificity and predictive accuracy cannot be estimated.

Topics

Medical terms in this study, explained in our Lexicon: Colorectal Cancer, Tumor Suppressor Gene, Carcinogenesis, Biomarker, Adenocarcinoma, Mesothelioma, Discovery.

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