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A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
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A Bioinformatic Algorithm for Analyzing Cell Signaling Using Temporal Proteomic Data.

Chunchao Zhang1, Yue Chen1, Xinfang Mao2

  • 1Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX, USA.

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|September 24, 2017
PubMed
Summary

A new IQR algorithm analyzes proteomic data variability for significance testing. This method identified gefitinib-targeted factors and ErbB pathway inhibition in EGF-stimulated HeLa cells.

Keywords:
EGF signalingHeLagefitinibmass spectrometrytranscription factors

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Area of Science:

  • Proteomics
  • Bioinformatics
  • Cancer Biology

Background:

  • Proteomic data from LC-MS/MS exhibits significant variability, complicating statistical analysis and protein quantification.
  • Existing model-based statistical inference methods are limited by skewed protein quantification data.
  • Accurate significance analysis is crucial for identifying drug targets and understanding cellular responses.

Purpose of the Study:

  • To develop a robust, non-parametric statistical algorithm for analyzing temporal proteomic data.
  • To identify gefitinib-targeted transcription factors and coregulators in Epidermal Growth Factor (EGF)-stimulated HeLa cells.
  • To explore potential druggable pathways in gefitinib-resistant or insensitive cancer patients.

Main Methods:

  • Developed the Interquartile Range (IQR) algorithm, a non-parametric statistical approach for significance analysis.
  • Utilized a reference group of multiple datasets to capture biological variations on a quartile scale.
  • Applied a stratified strategy considering six categories and signal strength for proteins of varying abundances.

Main Results:

  • Successfully applied the IQR algorithm to identify known EGF responders (e.g., EGR1, JUN, FOSB) and novel gefitinib-induced factors in HeLa cells.
  • Confirmed ErbB signaling pathway as a major inhibitory target of gefitinib through gene set enrichment analysis.
  • Identified several gefitinib-inducible transcription factors, suggesting alternative signaling pathways.

Conclusions:

  • The IQR algorithm provides a reliable method for significance analysis of variable proteomic data.
  • Gefitinib's primary target is the ErbB pathway, but induced transcription factors suggest alternative mechanisms.
  • The identified factors may represent potential therapeutic targets for gefitinib-resistant cancers.