Abstract 3575: Integration of estradiol signaling at the translational and transcriptional level in prostate cancer cells.
Bibliographic record
Abstract
Abstract Introduction The prostate is an organ where the dual actions of estrogen receptors (ERα and ERβ)are just beginning to be understood and appreciated in a physiological context. Steroid receptors can act outside of the nucleus by interacting with cytoplasmic or membrane bound proteins and activating or inhibiting signaling pathways in a ligand dependent or independent manner. However, our understanding of this non-nuclear activity remains partial. Here, we attempt to uncover how estrogens regulate post-transcriptional gene expression programs in prostate cancer. The rationale for studying impacts on post-transcriptional gene expression programs downstream of a transcription factor is that there is often no direct correlation between the level of an mRNA and the corresponding protein product. Additionally, several intracellular signaling pathways affected by estrogen signaling (e.g MAPK and PI3K pathways) directly regulate gene expression post-transcriptionally. One post-transcriptional mechanism that is tightly regulated by various intracellular signaling pathways is mRNA translation initiation - a highly controlled step that is regulated on multiple levels and is commonly deregulated in cancer. Many factors acting on mRNAs in cis and/or in trans, including RNA-binding proteins, RNA secondary structures or modifications of the cap structure by methylation can have strong impact on the efficiency of mRNA translation. Thus, estrogen signaling, by directly and indirectly affecting intracellular signaling can potentially regulate translational activity of its transcriptional targets. Approach We used prostate derived cell lines to assess how estrogens regulate proliferation and survival as a function of their ability to modulate transcription and translation. We performed polysome-microarray approach where the cytoplasmic mRNA population is separated based on the number of associated ribosomes followed by quantification of mRNA levels in pools of actively translating mRNAs. Using prostate cell lines, epithelial and stromal in origin, we first characterized the level of transcription and translation in unstimulated conditions to obtain a snapshot of the translational activity of each mRNA in baseline conditions. Secondly, we tested the impact on transcription and translation following stimulation for 2 hours with estradiol or with an ERβ-specific agonist (8β-VE2). We are currently validating ERα/β translational targets and identifying regulatory RNA sequences that enable ER-induced translational control. Conclusion We endeavor to determine how the gene expression programs regulated by ERα and ERβ are integrated at the transcriptional and translational level to direct proliferation, growth and survival in prostate tumors. Citation Format: Luc Furic, Ola Larsson, Ivan Topisirovic, Mark Frydenberg, John Pedersen, Gail Risbridger. Integration of estradiol signaling at the translational and transcriptional level in prostate cancer cells. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 3575. doi:10.1158/1538-7445.AM2013-3575
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".