E-GEOD-49005 - Multiscale Gene Networks Dissect the Complexity of Breast Cancer
Released on 18 July 2013, last updated on 3 June 2014
Despite the enormous amounts of molecular, cellular, and clinical data that are increasingly available for many different types of cancer, it remains a challenge to integrate different dimensions of data to construct mechanistic models that can robustly distinguish key driver genes from passenger genes, predict tumor progression, and tailor therapies optimally for individual patients. We present an integrative biology approach to constructing and analyzing multiscale regulatory networks of breast cancer. We systematically uncover not only known and novel gene subnetworks (modules) linked to breast cancer, but also their key drivers, the majority of which are not transcription factors or signaling molecules. A number of independent lines of evidence support that the predicted key drivers play central roles in breast cancer biology. We predict and experimentally validate ARF1 as a key driver of breast tumor phenotypes, and then demonstrate the ARF1-controlled subnetwork is a novel regulator of intra- and inter- cellular vesicle dynamics involved in epithelial-mesenchymal transition (EMT). We aimed to study the underlying mechanism by which ARF1 has been predicted playing crucial role during carcinogenesis and metastasis. To attest the key function of ARF1 during epithelial mesenchymal transition (EMT), loss of function in vitro study by means of siRNA knockdown of ARF1 in MDA-MB-231 breast cancer cell line was designed. Genome-wide gene expression of nine ARF1 knockdown samples and three control samples were profiled by using the Illumina HumanHT-12 V4.0 expression beadchip platform.
transcription profiling by array
Yongzhong Zhao <firstname.lastname@example.org>, Bin Zhang