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Epigenetic Regulation

Epigenetic Regulation

Epigenetic changes alter the physical structure of the DNA without changing the genetic sequence and often regulate whether genes are turned on or off. This regulation ensures that each cell produces only proteins necessary for its function. For example, proteins that promote bone growth are not produced in muscle cells. Epigenetic mechanisms play an essential role in healthy development. Conversely, precisely regulated epigenetic mechanisms are disrupted in diseases like cancer.
X-chromosome...

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Related Experiment Video

Updated: Jun 25, 2025

Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer

Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer

Published on: September 18, 2020

Elucidating Cancer Subtypes by Using the Relationship between DNA Methylation and Gene Expression.

Muneeba Jilani¹, David Degras², Nurit Haspel¹

¹Department of Computer Science, University of Massachusetts Boston, Boston, MA 02125, USA.

|May 25, 2024

Summary

This summary is machine-generated.

This study introduces sCClust, a new method combining gene expression and DNA methylation data to better classify cancer subtypes. The approach improves disease categorization for precision medicine applications.

Keywords:

cancer subtypes data integration multi-omics

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Last Updated: Jun 25, 2025

Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer

Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer

Published on: September 18, 2020

Methyl-binding DNA capture Sequencing for Patient Tissues

Methyl-binding DNA capture Sequencing for Patient Tissues

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Enhanced Reduced Representation Bisulfite Sequencing for Assessment of DNA Methylation at Base Pair Resolution

Enhanced Reduced Representation Bisulfite Sequencing for Assessment of DNA Methylation at Base Pair Resolution

Published on: February 24, 2015

Area of Science:

Computational Biology and Bioinformatics
Genomics and Epigenomics
Cancer Research

Background:

Next-generation sequencing (NGS) generates extensive multi-omics data, posing challenges for integrating diverse datasets like epigenome and transcriptome.
Accurate classification of disease subtypes is critical for advancing precision medicine, requiring robust methods to reconcile information from different molecular layers.

Purpose of the Study:

To develop and apply a novel technique, sCClust, for integrating high-dimensional epigenomic and transcriptomic data.
To enhance the classification of cancer subtypes by leveraging combined omics information.
To identify potential therapeutic targets through pathway analysis of integrated data.

Main Methods:

Utilized sparse canonical correlation analysis (sCCA) to maximize correlations between gene expression and DNA methylation datasets.
Applied clustering to the integrated data in a reduced-dimensional space derived from sCCA.
Validated the method on three Cancer Genome Atlas (TCGA) datasets: glioblastoma multiforme (GBM), lung cancer, and colon cancer.

Main Results:

sCClust successfully integrated gene expression and DNA methylation data, revealing distinct cancer subtypes.
Identified subtypes demonstrated improved clinical associations compared to single-omics or existing multi-omics approaches, validated by Kaplan-Meier plots and hazard ratio analysis.
Pathway over-representation analysis identified potential up-regulated and down-regulated genes as candidate drug targets.

Conclusions:

The sCClust method provides an effective strategy for integrating multi-omics data, enhancing cancer subtype elucidation.
Improved subtype classification holds significant implications for personalized treatment strategies in oncology.
The integration of epigenomic and transcriptomic data is crucial for a comprehensive understanding of cancer heterogeneity.