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Updated: May 28, 2026
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Quantitative Mass Spectrometric Profiling of Cancer-cell Proteomes Derived From Liquid and Solid Tumors
Published on: February 27, 2015
Reproducible cancer biomarker discovery in SELDI-TOF MS using different pre-processing algorithms
Jinfeng Zou1, Guini Hong, Xinwu Guo
1Bioinformatics Centre, School of Life Science, University of Electronic Science and Technology of China, Chengdu, People's Republic of China.
Plos One
|October 25, 2011
Summary
Biomarker identification reproducibility in mass spectrometry (MS) is inconsistent due to data pre-processing. Stratified false discovery rate (FDR) control improves detection power and reproducibility for cancer biomarkers.
Area of Science:
- Biomarker discovery
- Mass spectrometry (MS)
- Proteomics
Background:
- Biomarker identification from mass spectrometry (MS) studies is crucial for differentiating diseased and normal samples.
- Irreproducibility in biomarker identification, particularly concerning data pre-processing algorithms, hinders disease-specific biomarker discovery.
- Lack of a universally accepted standard for peak profile extraction impacts biomarker reliability.
Purpose of the Study:
- To investigate the consistency of biomarker identification using differentially expressed (DE) peaks from different pre-processing algorithms.
- To identify factors affecting the consistency of DE peak identification across various algorithms.
- To evaluate methods for improving DE peak detection power and reproducibility.
Main Methods:
- Analysis of SELDI-TOF MS data for prostate and breast cancers.
- Comparison of three widely used average spectrum-dependent pre-processing algorithms.
- Application of stratified false discovery rate (FDR) control for DE peak detection.
Main Results:
- Inconsistent DE peak identification across different pre-processing algorithms was observed.
- DE peaks from one profile were not always detected in others, and large profiles showed low statistical power.
- Stratified FDR control improved DE peak detection power and reproducibility in large profiles.
Conclusions:
- Evaluating pre-processing algorithms based on reproducibility aids in algorithm selection.
- DE peaks from smaller profiles tend to be more reproducibly detected in larger ones.
- Suitable pre-processing algorithms should yield sufficient peaks for identifying robust and reproducible biomarkers.
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