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Updated: May 25, 2026
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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
Core module biomarker identification with network exploration for breast cancer metastasis
Ruoting Yang1, Bernie J Daigle, Linda R Petzold
1Institute for Collaborative Biotechnologies, University of California Santa Barbara, Santa Barbara, CA 93106-5080, USA.
BMC Bioinformatics
|January 20, 2012
Summary
We developed COMBINER, a novel method to identify core disease genes and biomarkers. This approach significantly improves reproducibility and identifies key cancer-related genes, aiding in disease prognosis and diagnosis.
Area of Science:
- Genomics
- Systems Biology
- Bioinformatics
Background:
- Complex diseases alter gene expression, forming disease modules with driver, core, and passenger genes.
- Distinguishing driver/core genes from passenger genes is crucial for accurate disease prognosis and diagnosis.
Purpose of the Study:
- To develop and validate COMBINER (COre Module Biomarker Identification with Network ExploRation), a novel pathway-based approach.
- To identify reproducible prognostic biomarkers and driver genes in complex diseases, specifically breast cancer.
Main Methods:
- Developed COMBINER, a pathway-based computational approach.
- Applied COMBINER to three benchmark breast cancer datasets.
- Constructed global regulatory networks and identified driver genes.
Main Results:
- COMBINER biomarkers showed 10-fold higher reproducibility and significant enrichment for cancer-related and breast cancer-specific genes.
- Identified 13 confident driver genes associated with breast cancer metastasis.
- Highlighted hallmarks of cancer by overlaying identified modules onto intracellular pathway maps.
Conclusions:
- COMBINER efficiently and robustly identifies disease core modules and their regulatory networks.
- The method is potentially applicable to a wide range of diseases detectable via microarrays.
