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Related Concept Videos

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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
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Related Experiment Video

Updated: Apr 17, 2026

Flow-sorting and Exome Sequencing of the Reed-Sternberg Cells of Classical Hodgkin Lymphoma
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Gene expression profiling in non-Hodgkin lymphomas.

Joo Y Song1, Jianbo Yu, Wing C Chan

  • 1Department of Pathology, City of Hope Medical Center, 1500 East Duarte Road, Duarte, CA, 91010, USA, josong@coh.org.

Cancer Treatment and Research
|February 7, 2015
PubMed
Summary
This summary is machine-generated.

Gene expression profiling (GEP) refines lymphoma classification and prognostication. This technology enhances understanding of non-Hodgkin lymphomas (NHL) and guides targeted therapies.

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Area of Science:

  • Hematology
  • Oncology
  • Genomics

Background:

  • Current WHO classification for hematolymphoid neoplasms requires refinement for therapeutic and prognostic accuracy.
  • High-throughput gene expression profiling (GEP) technology has advanced significantly over the past two decades.

Purpose of the Study:

  • To review the impact of GEP on lymphoma classification and understanding.
  • To discuss GEP's role in improving prognostication and identifying targeted therapeutics for non-Hodgkin lymphomas (NHL).
  • To explore the integration of GEP findings into clinical practice and future research directions.

Main Methods:

  • Review of high-throughput gene expression profiling (GEP) technology and its applications.
  • Analysis of GEP's contribution to understanding lymphoma pathophysiology and treatment response.
  • Discussion of current clinical adaptation and future technological evolution.

Main Results:

  • GEP has significantly improved the classification and prognostication of NHL.
  • GEP enhances the understanding of lymphoma pathophysiology.
  • GEP provides insights into novel targeted therapeutics for lymphomas.

Conclusions:

  • GEP is a transformative tool revolutionizing lymphoma research and clinical practice.
  • Continued evolution of GEP technology promises further advancements in lymphoma diagnosis and treatment.
  • Adapting GEP findings to routine clinical practice is crucial for optimizing patient outcomes.