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THURSDAY, Sept. 15 (HealthDay News) -- The use of large-scale breast cancer gene expression datasets can identify novel pathways and offer the potential of new therapeutic targets, according to a study presented at the American Association of Cancer Research International Conference on Frontiers in Basic Cancer Research, held from Sept. 14 to 18 in San Francisco.
Bin Zhang, Ph.D., from Sage Bionetworks in Seattle, and colleagues investigated gene expression in breast cancer progression, using four large-scale cancer genomic datasets. Gene modules of highly interconnected genes, which were significantly conserved in the multiple coexpression networks, were grouped into consensus modules. Bayesian networks (BNs) constructed from individual datasets were combined to form a super BN. Module-based regulatory subnetworks were derived using consensus gene modules, and key regulators were identified using key driver analysis. Novel pathways identified as essential for breast cancer progression were validated and siRNA experiments were used to validate predicted novel drivers.
The investigators found that the driver genes predicted survival time and chemotherapy response, and explained drug mechanisms. A diuretic drug used to treat hypertension and edema effectively and selectively inhibited proliferation of breast cancer cells through cell cycle activity inhibition without killing the majority of normal cells.
"Such an integrative network approach and the findings will have significant impact on breast cancer research and drug development, while more broadly facilitating study and treatment of complex human diseases," the authors write.
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