Tests for finding complex patterns of differential expression in cancers: towards individualized medicine
10.1186/1471-2105-5-110
Crossref journal-article
Springer Science and Business Media LLC
BMC Bioinformatics (297)
Abstract

Abstract Background Microarray studies in cancer compare expression levels between two or more sample groups on thousands of genes. Data analysis follows a population-level approach (e.g., comparison of sample means) to identify differentially expressed genes. This leads to the discovery of 'population-level' markers, i.e., genes with the expression patterns A > B and B > A. We introduce the PPST test that identifies genes where a significantly large subset of cases exhibit expression values beyond upper and lower thresholds observed in the control samples. Results Interestingly, the test identifies A > B and B < A pattern genes that are missed by population-level approaches, such as the t-test, and many genes that exhibit both significant overexpression and significant underexpression in statistically significantly large subsets of cancer patients (ABA pattern genes). These patterns tend to show distributions that are unique to individual genes, and are aptly visualized in a 'gene expression pattern grid'. The low degree of among-gene correlations in these genes suggests unique underlying genomic pathologies and high degree of unique tumor-specific differential expression. We compare the PPST and the ABA test to the parametric and non-parametric t-test by analyzing two independently published data sets from studies of progression in astrocytoma. Conclusions The PPST test resulted findings similar to the nonparametric t-test with higher self-consistency. These tests and the gene expression pattern grid may be useful for the identification of therapeutic targets and diagnostic or prognostic markers that are present only in subsets of cancer patients, and provide a more complete portrait of differential expression in cancer.

Bibliography

Lyons-Weiler, J., Patel, S., Becich, M. J., & Godfrey, T. E. (2004). Tests for finding complex patterns of differential expression in cancers: towards individualized medicine. BMC Bioinformatics, 5(1).

Authors 4
  1. James Lyons-Weiler (first)
  2. Satish Patel (additional)
  3. Michael J Becich (additional)
  4. Tony E Godfrey (additional)
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Dates
Type When
Created 21 years ago (Aug. 17, 2004, 2:22 a.m.)
Deposited 10 months, 2 weeks ago (Oct. 7, 2024, 8:16 a.m.)
Indexed 3 months ago (May 21, 2025, 2:11 a.m.)
Issued 21 years ago (Aug. 12, 2004)
Published 21 years ago (Aug. 12, 2004)
Published Online 21 years ago (Aug. 12, 2004)
Funders 0

None

@article{Lyons_Weiler_2004, title={Tests for finding complex patterns of differential expression in cancers: towards individualized medicine}, volume={5}, ISSN={1471-2105}, url={http://dx.doi.org/10.1186/1471-2105-5-110}, DOI={10.1186/1471-2105-5-110}, number={1}, journal={BMC Bioinformatics}, publisher={Springer Science and Business Media LLC}, author={Lyons-Weiler, James and Patel, Satish and Becich, Michael J and Godfrey, Tony E}, year={2004}, month=aug }