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Expression data from MCF-7 cells stimulated by Estrogen or IGF-I

ABSTRACT: Although estrogen receptor (ER) and insulin-like growth factor (IGF) signaling are important for normal mammary development and breast cancer, cross-talk between these pathways, particularly at the level of gene transcription, remains poorly understood. We performed microarray analysis on MCF-7 breast cancer cells treated with estradiol (E2) or IGF-I for 3hr or 24hr. IGF-I regulated mRNA of 5-10-fold more genes than estradiol, and many genes were co-regulated by both ligands. Importantly, expression of these co-regulated genes correlated with poor prognosis of human breast cancer. Closer examination revealed enrichment of repressed transcripts. Interestingly, a number of potential tumor suppressors were down-regulated by IGF-I and estradiol. In fact, BLNK, one of the top repressed genes, is a potential growth suppressor in breast cancer cells. Analysis of three down-regulated genes showed that E2-mediated repression occurred independently of IGF-IR, and IGF-I-mediated repression occurred independently of ER. However, repression by IGF-I or estradiol required common downstream kinases. In conclusion, E2 and IGF-I co-regulate a set of genes that affect breast cancer outcome. There is enrichment of repressed transcripts, and the down-regulation is independent at the receptor level. This may be important clinically, as tumors with active ER and IGF-IR signaling may require co-targeting of both pathways. KEYWORDS: multiple group comparison Microarray analysis on MCF-7 breast cancer cells treated with estradiol (E2) or IGF-I for 3hr or 24hr.

ORGANISM(S): Homo sapiens

SUBMITTER: Casa AJ

PROVIDER: S-ECPF-GEOD-26834 | biostudies-other | 2012 Feb

REPOSITORIES: biostudies-other

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Project description:In the context of the Human Toxome project, mass spectroscopy-based metabolomics characterization of estrogen-stimulated MCF-7 cells was studied in order to support the untargeted deduction of pathways of toxicity. A targeted and untargeted approach using overrepresentation analysis (ORA), quantitative enrichment analysis (QEA) and pathway analysis (PA) and a metabolite network approach were compared. Any untargeted approach necessarily has some noise in the data owing to artifacts, outliers and misidentified metabolites. Depending on the chemical analytical choices (sample extraction, chromatography, instrument and settings, etc.), only a partial representation of all metabolites will be achieved, biased by both the analytical methods and the database used to identify the metabolites. Here, we show on the one hand that using a data analysis approach based exclusively on pathway annotations has the potential to miss much that is of interest and, in the case of misidentified metabolites, can produce perturbed pathways that are statistically significant yet uninformative for the biological sample at hand. On the other hand, a targeted approach, by narrowing its focus and minimizing (but not eliminating) misidentifications, renders the likelihood of a spurious pathway much smaller, but the limited number of metabolites also makes statistical significance harder to achieve. To avoid an analysis dependent on pathways, we built a de novo network using all metabolites that were different at 24 h with and without estrogen with a p value <0.01 (53) in the STITCH database, which links metabolites based on known reactions in the main metabolic network pathways but also based on experimental evidence and text mining. The resulting network contained a "connected component" of 43 metabolites and helped identify non-endogenous metabolites as well as pathways not visible by annotation-based approaches. Moreover, the most highly connected metabolites (energy metabolites such as pyruvate and alpha-ketoglutarate, as well as amino acids) showed only a modest change between proliferation with and without estrogen. Here, we demonstrate that estrogen has subtle but potentially phenotypically important alterations in the acyl-carnitine fatty acids, acetyl-putrescine and succinoadenosine, in addition to likely subtle changes in key energy metabolites that, however, could not be verified consistently given the technical limitations of this approach. Finally, we show that a network-based approach combined with text mining identifies pathways that would otherwise neither be considered statistically significant on their own nor be identified via ORA, QEA, or PA.

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Expression data from MCF-7 cells stimulated by Estrogen or IGF-I

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