Novel Membrane-Associated Targets for Diagnosis and Treatment of Breast Cancer

Abstract

Differentially expressed proteins localized to the cell membrane or secreted show great promise as therapeutic targets and diagnostic markers because of their easy accessibility. However, determining protein localization by traditional methods is a difficult process. We predict protein localization in the MCF7 breast cancer cell line by analysis of differential hybridization levels of RNAs that have been physically separated with a sucrose density gradient by virtue of their association with polysomes on the endoplasmic reticulum. Assignment to membrane-associated and secreted class membership is based on both the differential hybridization levels and an expression threshold, which are calculated empirically from data collected on a reference set of known cytoplasmic and membrane proteins. Applied to the unknown set, these criteria identified 755 probe sets as potentially MS for which this annotation did not previously exist. The data were used to filter a previously reported expression dataset to identify MS genes which are associated with poor prognosis in breast cancer and represent potential targets for diagnosis and treatment. This approach provides a useful tool for the analysis of gene expression patterns, to identity genes encoding membrane-associated or secreted proteins with biological relevance that have the potential for clinical applications in diagnosis or treatment.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 2005
Accession Number
ADA438330

Entities

People

  • Brenton G. Mar

Organizations

  • University of Illinois Urbana–Champaign

Tags

DTIC Thesaurus Topics

  • Animal Structures
  • Biomedical Research
  • Breast Cancer
  • Cancer
  • Cell Line
  • Cell Membrane
  • Cell Physiological Processes
  • Cells
  • Data Sets
  • Endoplasmic Reticulum
  • Gene Expression
  • Hybridization
  • Literature
  • Membrane Proteins
  • Neoplasms
  • Proteins
  • Training

Fields of Study

  • Biology

Readers

  • Molecular Biology and Genetics
  • Molecular and Cellular Biochemistry
  • Oncology and Biomarker-Based Cancer Detection.