Next Article in Journal
Pursuit of Gene Fusions in Daily Practice: Evidence from Real-World Data in Wild-Type and Microsatellite Instable Patients
Next Article in Special Issue
The Genetic Analyses of French Canadians of Quebec Facilitate the Characterization of New Cancer Predisposing Genes Implicated in Hereditary Breast and/or Ovarian Cancer Syndrome Families
Previous Article in Journal
Alpha-Fetoprotein- and CD40Ligand-Expressing Dendritic Cells for Immunotherapy of Hepatocellular Carcinoma
Previous Article in Special Issue
Differential Regulation of Lacto-/Neolacto- Glycosphingolipid Biosynthesis Pathway Reveals Transcription Factors as Potential Candidates in Triple-Negative Breast Cancer
 
 
Article

Meta-Analysis of Microdissected Breast Tumors Reveals Genes Regulated in the Stroma but Hidden in Bulk Analysis

1
Molecular Biotechnology Center, Department of Molecular Biotechnology and Health Sciences, University of Turin, Via Nizza 52, 10126 Turin, Italy
2
Department of Neurosciences “Rita Levi Montalcini”, University of Turin, Corso Massimo D’Azeglio 52, 10126 Turin, Italy
3
Center for Omics Sciences, Ospedale San Raffaele IRCCS, Via Olgettina 60, 20132 Milan, Italy
*
Authors to whom correspondence should be addressed.
Academic Editors: Nadège Presneau, Yves-Jean Bignon and Pinar Uysal Onganer
Cancers 2021, 13(13), 3371; https://doi.org/10.3390/cancers13133371
Received: 14 May 2021 / Revised: 22 June 2021 / Accepted: 29 June 2021 / Published: 5 July 2021
(This article belongs to the Special Issue Identification of Candidate Genes in Breast and Ovarian Cancer)
Gene expression profiling of tumors is an essential approach for the selection of biomarkers and the investigation of the molecular mechanisms of cancer, but transcriptomic results are often difficult to reproduce due to technical biases, sample heterogeneity, or small sample sizes. Combining many datasets can help to reduce artefacts and improve statistical power. Therefore, we aimed at creating a comprehensive resource of transcriptomic datasets investigating breast cancers, focusing on microdissected tumors, which enable the distinguishing of the contribution of the tumor microenvironment from that of cancer cells. We define robust lists of differentially expressed genes and describe their relationships with clinical features in each cellular compartment, identifying clinically relevant markers that can only be retrieved by measuring their expression in the sole tumor microenvironment.
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. View Full-Text
Keywords: tumor microenvironment; meta-analysis; tumor stroma; breast cancer; LCM; microdissection; transcriptomics; microarray; database tumor microenvironment; meta-analysis; tumor stroma; breast cancer; LCM; microdissection; transcriptomics; microarray; database
Show Figures

Figure 1

  • Externally hosted supplementary file 1
    Doi: 10.5281/zenodo.5013252
    Link: https://zenodo.org/record/5013252#.YNHsMegzZPY
    Description: Supplementary files of the manuscript "Meta-analysis of microdissected breast tumors reveals genes regulated in the stroma but hidden in bulk analysis".
MDPI and ACS Style

Savino, A.; De Marzo, N.; Provero, P.; Poli, V. Meta-Analysis of Microdissected Breast Tumors Reveals Genes Regulated in the Stroma but Hidden in Bulk Analysis. Cancers 2021, 13, 3371. https://doi.org/10.3390/cancers13133371

AMA Style

Savino A, De Marzo N, Provero P, Poli V. Meta-Analysis of Microdissected Breast Tumors Reveals Genes Regulated in the Stroma but Hidden in Bulk Analysis. Cancers. 2021; 13(13):3371. https://doi.org/10.3390/cancers13133371

Chicago/Turabian Style

Savino, Aurora, Niccolò De Marzo, Paolo Provero, and Valeria Poli. 2021. "Meta-Analysis of Microdissected Breast Tumors Reveals Genes Regulated in the Stroma but Hidden in Bulk Analysis" Cancers 13, no. 13: 3371. https://doi.org/10.3390/cancers13133371

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop