Evidence map›Paper›PMID 41595132›Full record

ArticleCancers2026

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

Andrei Stefan Blindu, Silvia Berardelli, Federica De Paoli, Federico Manai, Rossella Tricarico, Susanna Zucca, Paolo Magni

Abstract read
In one paragraph

Article in Cancers, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

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No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Andrei Stefan BlinduDepartment of Electrical, Computer and Biomedical Engineering, University of Pavia, Via Ferrata, 5, 27100 Pavia, Italy.ORCID 0009-0004-4974-3101
Silvia BerardelliDepartment of Electrical, Computer and Biomedical Engineering, University of Pavia, Via Ferrata, 5, 27100 Pavia, Italy.ORCID 0009-0009-5468-1466
Federica De PaolienGenome s.r.l., Via Ferrata, 5, 27100 Pavia, Italy.ORCID 0000-0002-8027-5666
Federico ManaiDepartment of Biology and Biotechnology "L. Spallanzani", University of Pavia, 27100 Pavia, Italy.ORCID 0000-0002-7288-9414
Rossella TricaricoDepartment of Biology and Biotechnology "L. Spallanzani", University of Pavia, 27100 Pavia, Italy.ORCID 0000-0001-7055-7208
Susanna ZuccaenGenome s.r.l., Via Ferrata, 5, 27100 Pavia, Italy.ORCID 0000-0001-8465-632X
Paolo MagniDepartment of Electrical, Computer and Biomedical Engineering, University of Pavia, Via Ferrata, 5, 27100 Pavia, Italy.ORCID 0000-0002-8931-4676

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND/

objectivesDNA 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.

methodsWe 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.

resultsThe 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.

conclusionsOur 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.

Indexed as

biomarker discoverycolorectal cancercomputational analysisCpG island methylator phenotypeDNA methylationepigenetic regulationgene expressionmethylation-associated genesmulti-omics integration

Identifiers

PMID41595132
PMCPMC12839322

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