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Epigenetic Regulation01:37

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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.
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Related Experiment Video

Updated: Sep 13, 2025

Systems Biology of Metabolic Regulation by Estrogen Receptor Signaling in Breast Cancer
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Unveiling Epigenetic Regulatory Elements Associated with Breast Cancer Development.

Marta Jardanowska-Kotuniak1,2, Michał Dramiński1, Michal Wlasnowolski3

  • 1Computational Biology Group, Institute of Computer Science of the Polish Academy of Sciences, 01-248 Warsaw, Poland.

International Journal of Molecular Sciences
|July 29, 2025
PubMed
Summary
This summary is machine-generated.

This study identifies key epigenetic changes in breast cancer, revealing new biomarkers and a novel computational method. The findings offer a robust framework for understanding gene regulation and reducing data complexity for further research.

Keywords:
MCFS-IDMonte Carlo Feature SelectionNKAPLNRF1Natural Language Processingbreast cancerchromatin structureepigenetic regulationtranscription factor

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Area of Science:

  • Genomics
  • Epigenetics
  • Bioinformatics

Background:

  • Breast cancer impacts millions globally, necessitating advanced research into its underlying mechanisms.
  • Identifying reliable biomarkers and understanding gene expression regulation are critical for effective treatment strategies.

Purpose of the Study:

  • To uncover epigenetic mechanisms influencing breast cancer gene expression.
  • To discover novel breast cancer biomarkers.
  • To develop an integrated bioinformatic approach combining feature selection, Natural Language Processing, and 3D chromatin analysis.

Main Methods:

  • Utilized The Cancer Genome Atlas (TCGA) multi-omics data (mRNA, miRNA, DNA methylation) from over 800 samples.
  • Applied Monte Carlo Feature Selection and Interdependency Discovery to reduce 417,486 features to 2701 significant ones.
  • Integrated Natural Language Processing and 3D chromatin structure analysis.

Main Results:

  • Achieved high classification accuracy between cancer and control samples using selected features.
  • Observed generally lower differentially expressed gene (DEG) expression and increased differentially methylated site (DMS) β-values in cancer samples.
  • Identified specific DMSs impacting transcription factor binding (NRF1, MXI1) and altering gene expression (NKAPL, PITX1).
  • 3D chromatin models revealed looser packing in cancer cells.

Conclusions:

  • The study highlights complex regulatory dependencies in breast cancer.
  • The proposed bioinformatic approach effectively reduces data dimensionality and identifies key features.
  • Findings provide a foundation for experimental validation of identified biomarkers and regulatory pathways.