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Related Experiment Video

Updated: Jul 17, 2026

Colorectal Cancer Cell Surface Protein Profiling Using an Antibody Microarray and Fluorescence Multiplexing
15:17

Colorectal Cancer Cell Surface Protein Profiling Using an Antibody Microarray and Fluorescence Multiplexing

Published on: September 25, 2011

Multiple gene expression classifiers from different array platforms predict poor prognosis of colorectal cancer.

Yu-Hsin Lin1, Jan Friederichs, Michael A Black

  • 1Authors' Affiliations: Cancer Genetics Laboratory and Departments of Biochemistry, Medical and Surgical Sciences, and Pathology, University of Otago.

Clinical Cancer Research : an Official Journal of the American Association for Cancer Research
|January 27, 2007
PubMed
Summary

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Gene classifiers accurately predict colorectal cancer recurrence, showing prognostic power across different patient cohorts and microarray technologies. This supports gene expression profiling for personalized cancer outcome prediction.

Area of Science:

  • Oncology
  • Genomics
  • Bioinformatics

Background:

  • Colorectal cancer (CRC) recurrence prediction is crucial for patient management.
  • Accurate prognostic markers are needed to improve upon traditional clinical staging.
  • Gene expression profiling offers a potential avenue for enhanced predictive accuracy.

Purpose of the Study:

  • To develop and validate gene classifiers for predicting colorectal cancer recurrence.
  • To assess the independent validation of classifiers across different tumor series and microarray platforms.
  • To evaluate the utility of gene expression data in improving predictive models for CRC recurrence.

Main Methods:

  • Colorectal tumor samples from New Zealand (n=149) and Germany (n=55) with ≥5 years follow-up were analyzed.

Related Experiment Videos

Last Updated: Jul 17, 2026

Colorectal Cancer Cell Surface Protein Profiling Using an Antibody Microarray and Fluorescence Multiplexing
15:17

Colorectal Cancer Cell Surface Protein Profiling Using an Antibody Microarray and Fluorescence Multiplexing

Published on: September 25, 2011

  • RNA was profiled using oligonucleotide printed microarrays and Affymetrix arrays.
  • Gene classifiers were developed using clinical and gene expression data, and cross-validated between cohorts.

    • Main Results:

    • Gene-based classifiers achieved prediction rates of 77% (New Zealand) and 84% (Germany).
    • Classifiers retained prognostic power when applied to independent, cross-platform datasets.
    • Gene classifiers provided superior patient stratification compared to traditional clinical staging.

    Conclusions:

    • Reciprocal validation of gene classifiers across diverse cohorts and platforms confirms microarray technology's utility.
    • Identified genes possess known biological functions supporting their role in CRC progression and outcome.
    • Gene expression profiling holds significant potential for individualized outcome prediction in colorectal cancer patients.