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Expression data from DKAT breast cancer cell line pre- and post-EMT

ABSTRACT: The DKAT cell line is a novel model of triple-negative breast cancer that was isolated from the pleural effusion of a 35 year-old caucasian woman with triple-negative breast cancer. When cultured in serum-free media (MEGM, Lonza) this cell line exhibits an epithelial morphology and gene expression pattern. However, when cultured in the presence of serum (SCGM, Lonza) it undergoes a reversible EMT. We used microarrays to look at gene expression changes in the DKAT cell line when cultured in Mammary Epithelial Growth Media (MEGM, where DKAT cells have an epithelial morphology) vs Stromal Cell Growth Media (SCGM, where DKAT cells have a mesenchymal morphology). DKAT breast cancer cells (passage 10) grown in MEGM (Lonza) were split and cultured in T75 flasks in either in MEGM or SCGM for 14 days. RNA was isolated using Qiagen RNeasy kit and hybridization on Affymetrix microarrays was performed. Three separate cDNA reactions were performed and these were run as technical replicates.

ORGANISM(S): Homo sapiens

SUBMITTER: D'Amato Nicholas

PROVIDER: S-ECPF-GEOD-33146 | biostudies-other |

REPOSITORIES: biostudies-other

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Project description:MicroRNAs (miRNAs) are small non-coding RNAs that regulate genes at the post-transcriptional level in spatiotemporal manner. Several miRNAs are identified as prognostic and diagnostic markers in many human cancers. Estimation of the temporal activities of the miRNAs is an important step in the way to understand the complex interactions of these important regulatory elements with transcription factors (TFs) and target genes (TGs). However, current research on miRNA activities excludes network dynamics from the studies, disregarding the important element of time in the regulatory network analysis.In the current study, we combined experimentally verified miRNA-TG interactions with breast cancer microarray TG expression data to identify key miRNAs and compute their temporal activity using network component analysis (NCA). The computed activities showed that miRNAs were regulated in a time dependent manner. Our results allowed constructing a synergistic network of miRNAs using the computed miRNA activities and their shared regulation of TGs. We further extended this network by incorporating miRNA-TG, miRNA-TF, TF-miRNA and TF-TG regulations in the context of breast cancer. Our integrated network identified several miRNAs known to be involved in breast cancer regulation and revealed several novel miRNAs. Our further analysis detected substantial involvement of the miRNAs miR-324, miR-93, miR-615 and miR-1 in breast cancer, which was not known previously. Next, combining our integrated networks with functional annotation of differentially expressed genes resulted in new sub-networks. These sub-networks allowed us to identify the key miRNAs and their interactions with TFs and TGs of several biological processes involved in breast cancer. The identified markers are validated for their potential as prognostic markers for breast cancer through survival analysis.Our dynamical analysis of the miRNA interactions greatly helps to discover new network based markers, and is highly applicable (but not limited) to cancer research.

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Expression data from DKAT breast cancer cell line pre- and post-EMT

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