Related Experiment Video
Updated: Aug 23, 2026

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
Application of gene expression profiling for validating models of human breast cancer
Jeffrey E Green1, Kartiki V Desai, Yumei Ye
1Transgenic Oncogenesis Group, Laboratory of Cell Regulation and Carcinogenesis, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA. JEGreen@nih.gov
Abstract:
While classical histopathologic approaches are invaluable in classifying tumors and understanding aspects of cellular interactions, genomic approaches provide a means to molecularly dissect tumorigenesis. The relationship of gene expression to the development of neoplasia remains an area of intensive research. With the advent of large-scale genomic platforms, alterations in gene expression can be related to the morphological development of cancer. The feasibility of using large-scale genomic analysis platforms has dramatically changed the landscape of biological sciences, as cellular processes must be considered in the context of complex networks. Alterations in gene expression must now be understood in a systems approach in which the relationships between genes expression changes are studied by considering the interplay of multiple regulatory networks. Ultimately, such changes must be understood at the protein level. We have begun to apply this technology to determine changes in gene expression that differentiate various types of mammary cancers that arise in mouse models that have been initiated by different genetic alterations. Ultimately, a molecular catalogue of similarities and differences between rodent and human tumors can be created which will serve to validate or credential particular models for specific experimental purposes, such as preclinical testing. These approaches have led to new insights into molecular pathways involved in oncogenesis, new classifications of human breast cancer, and the identification of new genes that may be relevant to understanding and treating human cancer.

