Identification of novel targets for breast cancer by exploring gene switches on a genome scale
Year: 2011
Type: article
Abstract: BACKGROUND: An important feature that emerges from analyzing gene regulatory networks is the "switch-like behavior" or "bistability", a dynamic feature of a particular gene to preferentially toggle between two steady-states. The state of gene switches plays pivotal roles in cell fate decision, but identifying switches has been difficult. Therefore a challenge confronting the field is to be able to systematically identify gene switches. RESULTS: We propose a top-down mining approach to exploring gene switches on a genome-scale level. Theoretical analysis, proof-of-concept examples, and experimental studies demonstrate the ability of our mining approach to identify bistable genes by sampling across a variety of different conditions. Applying the approach to human breast cancer data identified genes that show bimodality within the cancer samples, such as estrogen receptor (ER) and ERBB2, as well as genes that show bimodality between cancer and non-cancer samples, where tumor-associated calcium signal transducer 2 (TACSTD2) is uncovered. We further suggest a likely transcription factor that regulates TACSTD2. CONCLUSIONS: Our mining approach demonstrates that one can capitalize on genome-wide expression profiling to capture dynamic properties of a complex network. To the best of our knowledge, this is the first attempt in applying mining approaches to explore gene switches on a genome-scale, and the identification of TACSTD2 demonstrates that single cell-level bistability can be predicted from microarray data. Experimental confirmation of the computational results suggest TACSTD2 could be a potential biomarker and attractive candidate for drug therapy against both ER+ and ER- subtypes of breast cancer, including the triple negative subtype.
Source: BMC Genomics
Authors: Ming Wu, Li Liu, Christina Chan
Institution: Michigan State University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,090
Awards: Development of a parallel, array-based transcription factor expression assay, CDI-Type II: Discovery of Biophysical Mechanisms Inducing Signaling and Cytotoxicity: An Experimental Approach Enabled by Cyber Tools, Low Temperature Transcriptional Networks, G316389574, EAGER: An Integrative Approach to Guide MSC Clonal Population Selection and Differentiation, Develop a Dynamic Model that Incoporates Text-mining to Reconstruct Networks, G6490296102, Maximizing siRNA Function through Mechanism-based Sequence and Vehicle Design
OpenAlex ID: W2096844207