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Network Analysis of Breast Cancer Progression and Reversal with a Tree-Evolving Network Algorithm

ABSTRACT: The HMT3522 progression series of breast cells has been used to discover the roles of tissue architecture, microenvironment and signaling molecules in nonmalignant and malignant breast cell growth and behaviors, including the potential of various factors to cause phenotypic reversion of malignant cells to nonmalignant states. Despite many efforts to delineate key signaling pathways governing the malignant as well as the phenotypic reversion behaviors of T4 cells, much remains to be elucidated about the regulatory mechanisms underlying these cell states at the systems level. Here, we analyzed gene expression microarray profiles obtained from this progression series in both phenotypically malignant and reverted states using our newly developed tree-lineage-based network detection algorithm, Treegl. Our work suggests analysis of various conditions of reversion (including non-reverted) of the HMT3522 cells using Treegl can be a model system to study drug effects on breast cancer cells, and may become a tool for effective drug discovery and target identification. Gene expression microarray data was obtained using Affymetrix GeneChip Human Genome U133A arrays to analyze 15 total RNA samples prepared from the HMT3522 breast cells grown in 3D lrECM and treated with various reverting agents or vehicle control.

ORGANISM(S): Homo sapiens

SUBMITTER: Ankur Parikh

PROVIDER: E-GEOD-42125 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Network Analysis of Breast Cancer Progression and Reversal with a Tree-Evolving Network Algorithm

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