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Related Concept Videos

mRNA Stability and Gene Expression02:51

mRNA Stability and Gene Expression

The structure and stability of mRNA molecules regulates gene expression, as mRNAs are a key step in the pathway from gene to protein. In eukaryotes, the half-life of mRNA varies from a few minutes up to several days. mRNA stability is essential in growth and development. The absence of the proteins regulating its stability, such as tristetraprolin in mice, can cause systemic issues, including bone marrow overgrowth, inflammation, and autoimmunity.
Cis-acting Elements involved in mRNA stability
mRNA Stability and Gene Expression02:51

mRNA Stability and Gene Expression

The structure and stability of mRNA molecules regulates gene expression, as mRNAs are a key step in the pathway from gene to protein. In eukaryotes, the half-life of mRNA varies from a few minutes up to several days. mRNA stability is essential in growth and development. The absence of the proteins regulating its stability, such as tristetraprolin in mice, can cause systemic issues, including bone marrow overgrowth, inflammation, and autoimmunity.
Cis-acting Elements involved in mRNA stability

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Quantifying stability in gene list ranking across microarray derived clinical biomarkers.

Sebastian Schneckener1, Nilou S Arden, Andreas Schuppert

  • 1Bayer AG, Bayer Technology Services, 51368 Leverkusen, Germany.

BMC Medical Genomics
|October 15, 2011
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Summary

A new information ratio (IR) metric helps identify stable gene biomarkers from tumor expression data. Lower IR values indicate higher biomarker transferability and predictive accuracy across studies, crucial for cancer research.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Identifying stable gene lists for cancer diagnosis, prognosis, and treatment is challenging.
  • Microarray gene expression data offers predictive potential but often lacks power and consistency across studies.
  • Robust biomarker identification methods are needed to overcome inconsistencies in gene expression profiling.

Purpose of the Study:

  • To develop a method for identifying stable gene lists and robust biomarkers from microarray data.
  • To assess the impact of study size and clinical phenotype on gene list stability and predictive power.
  • To introduce a quantitative metric for evaluating information content in gene expression data.

Main Methods:

  • Projecting genomic tumor expression data into a lower-dimensional space.
  • Defining an information ratio (IR) based on the partition between projected and residual data spaces.
  • Correlating IR values with biomarker robustness and predictive accuracy across different phenotypes and datasets.

Main Results:

  • Higher IR values correlated with phenotypes yielding less robust biomarkers.
  • Lower IR values demonstrated higher transferability of biomarkers across studies.
  • The IR metric was found to be correlated with predictive accuracy and identified information-rich data.

Conclusions:

  • The information ratio (IR) provides a quantitative measure of information content in gene expression data relative to specific phenotypes.
  • This metric aids in identifying stable biomarkers and assessing data quality for clinical applications in cancer research.