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Related Experiment Video

Updated: Mar 10, 2026

Detecting the Ligand-binding Domain Dimerization Activity of Estrogen Receptor Alpha Using the Mammalian Two-Hybrid Assay
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Non-coding single nucleotide variants affecting estrogen receptor binding and activity.

Amir Bahreini1,2,3, Kevin Levine3,4, Lucas Santana-Santos5,6

  • 1Deparmtent of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA.

Genome Medicine
|December 15, 2016
PubMed
Summary
This summary is machine-generated.

Researchers identified single nucleotide variants (SNVs) in estrogen receptor (ER) binding sites that may impact breast cancer progression. This study developed a computational method to find these regulatory SNVs (regSNVs) and linked them to gene expression and patient outcomes.

Keywords:
Breast cancerDNA bindingEstrogen receptorIGF1RNon-coding SNVs

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Area of Science:

  • Genomics
  • Cancer Biology
  • Bioinformatics

Background:

  • Estrogen receptor (ER) activity is crucial in most breast cancers.
  • ER DNA binding influences gene expression and cell behavior.
  • Investigating single nucleotide variants (SNVs) in ER binding sites (regSNVs) may reveal new mechanisms of ER action and disease progression.

Purpose of the Study:

  • To develop a computational pipeline for identifying SNVs within ER binding sites (regSNVs).
  • To assess the impact of regSNVs on ER binding and gene expression in ER-positive breast cancer.
  • To explore the potential clinical relevance of regSNVs in breast cancer prognosis.

Main Methods:

  • Utilized ER chromatin immunoprecipitation sequencing (ChIP-seq) data from ER-positive breast cancer models.
  • Employed GATK for SNV identification and MACS for DNA-binding site calling.
  • Integrated TCGA data, multi-omics, and clinical data; validated findings with ChIP and luciferase assays.

Main Results:

  • Identified an intronic SNV (rs62022087) in IGF1R that enhances ER binding.
  • Discovered 17 regSNVs associated with altered gene expression in ER-positive breast cancer.
  • Found a promoter regSNV in GSTM1 linked to higher GSTM1 expression and better patient survival.

Conclusions:

  • Developed a computational approach to identify putative regSNVs in ER ChIP-binding sites.
  • These non-coding variants may regulate target genes and influence breast cancer prognosis.
  • regSNVs represent a potential new avenue for understanding and predicting clinical outcomes in breast cancer.