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Related Experiment Video

Updated: May 9, 2026

Automated Dissection Protocol for Tumor Enrichment in Low Tumor Content Tissues
06:44

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Published on: March 29, 2021

Technical considerations for analyzing EMT-MET data from surgical samples.

Kazuhiko Aoyagi1, Masashi Tamaoki, Takao Nishumura

  • 1Division of Genetics, National Cancer Center Research Institute, Chuo-ku, Tokyo 104-0045, Japan.

Cancer Letters
|August 13, 2013
PubMed
Summary
This summary is machine-generated.

Surgical biopsies may alter gene expression, causing artificially induced epithelial-mesenchymal transition (aiEMT). This finding impacts cancer research, suggesting potential underestimation of prognostic markers from biopsy samples.

Keywords:
Artificially induced EMTEsophageal cancerExpression profileSurgical sample

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Area of Science:

  • Oncology
  • Molecular Biology
  • Genomics

Background:

  • Neoadjuvant therapy increases the need for biopsy samples in cancer research.
  • Gene expression profiles differ between surgical and endoscopic biopsy samples, but the extent is unclear.

Purpose of the Study:

  • To investigate differences in gene expression profiles between surgical and endoscopic biopsy samples.
  • To identify potential biases in gene expression profiling from biopsy samples.

Main Methods:

  • Comparative analysis of gene expression profiles from surgical and endoscopic esophageal biopsy samples.
  • Induction of epithelial-mesenchymal transition (EMT) in mouse models under ischemic conditions.

Main Results:

  • Artificially induced epithelial-mesenchymal transition (aiEMT) was observed in surgical samples.
  • aiEMT was also detected in mouse epithelium under ischemic conditions.
  • Biopsy samples may introduce biases in gene expression profiling.

Conclusions:

  • Surgical biopsy procedures can induce aiEMT, potentially affecting gene expression profiles.
  • Past cancer research using biopsy samples may have underestimated the prognostic power of EMT markers.
  • Careful consideration of biopsy methods is crucial for accurate in vivo expression profiling.