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Meta-Analysis of Microdissected Breast Tumors Reveals Genes Regulated in the Stroma but Hidden in Bulk Analysis
Aurora Savino1, Niccolò De Marzo1, Paolo Provero2,3
1Molecular Biotechnology Center, Department of Molecular Biotechnology and Health Sciences, University of Turin, Via Nizza 52, 10126 Turin, Italy.
Abstract:
Transcriptome data provide a valuable resource for the study of cancer molecular mechanisms, but technical biases, sample heterogeneity, and small sample sizes result in poorly reproducible lists of regulated genes. Additionally, the presence of multiple cellular components contributing to cancer development complicates the interpretation of bulk transcriptomic profiles. To address these issues, we collected 48 microarray datasets derived from laser capture microdissected stroma or epithelium in breast tumors and performed a meta-analysis identifying robust lists of differentially expressed genes. This was used to create a database with carefully harmonized metadata that we make freely available to the research community. As predicted, combining the results of multiple datasets improved statistical power. Moreover, the separate analysis of stroma and epithelium allowed the identification of genes with different contributions in each compartment, which would not be detected by bulk analysis due to their distinct regulation in the two compartments. Our method can be profitably used to help in the discovery of biomarkers and the identification of functionally relevant genes in both the stroma and the epithelium. This database was made to be readily accessible through a user-friendly web interface.
Insights
This study enhances cancer research by meta-analyzing breast tumor transcriptome data. It identifies robust gene lists and creates a freely accessible database for discovering biomarkers in tumor stroma and epithelium.
Area of Science:
- Oncology
- Bioinformatics
- Genomics
Background:
- Transcriptome data are crucial for understanding cancer molecular mechanisms.
- Technical biases, sample heterogeneity, and small sample sizes limit gene list reproducibility.
- Interpreting bulk transcriptomic profiles is complicated by multiple contributing cellular components.
Purpose of the Study:
- To address limitations in reproducibility and interpretation of cancer transcriptome data.
- To identify robust lists of differentially expressed genes in breast tumors.
- To create a harmonized, accessible database for the research community.
Main Methods:
- Collected 48 microarray datasets from laser capture microdissected breast tumor stroma and epithelium.
- Performed a meta-analysis to identify robust lists of differentially expressed genes.
- Created a database with harmonized metadata and a user-friendly web interface.
Main Results:
- Meta-analysis improved statistical power for identifying regulated genes.
- Separate analysis of stroma and epithelium revealed compartment-specific gene contributions.
- Identified robust gene lists not detectable by bulk analysis due to distinct regulation.
Conclusions:
- The developed meta-analysis method enhances statistical power and identifies compartment-specific genes.
- The freely available database facilitates biomarker discovery and functional gene identification in breast tumor stroma and epithelium.
- This approach offers a valuable resource for advancing cancer molecular mechanism research.
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