Epithelial-Mesenchymal Transition (EMT) and Regulation of EMT Factors by Steroid Nuclear Receptors in Breast Cancer: A Review and in Silico Investigation
Bibliographic record
Abstract
Steroid Nuclear Receptors (SNRs) are transcription factors of the nuclear receptor super-family. Estrogen Receptor (ERα) is the best-studied and has a seminal role in the clinic both as a prognostic marker but also as a predictor of response to anti-estrogenic therapies. Progesterone Receptor (PR) is also used in the clinic but with a more debatable prognostic role and the role of the four other SNRs, ERβ, Androgen Receptor (AR), Glucocorticoid Receptor (GR) and Mineralocorticoid Receptor (MR), is starting only to be appreciated. ERα, but also to a certain degree the other SNRs, have been reported to be involved in virtually every cancer-enabling process, both promoting and impeding carcinogenesis. Epithelial-Mesenchymal Transition (EMT) and the reverse Mesenchymal Epithelial Transition (MET) are such carcinogenesis-enabling processes with important roles in invasion and metastasis initiation but also establishment of tumor in the metastatic site. EMT is governed by several signal transduction pathways culminating in core transcription factors of the process, such as Snail, Slug, ZEB1 and ZEB2, and Twist, among others. This paper will discuss direct regulation of these core transcription factors by SNRs in breast cancer. Interrogation of publicly available databases for binding sites of SNRs on promoters of core EMT factors will also be included in an attempt to fill gaps where other experimental data are not available.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".