Selective Loss of AKR1C1 and AKR1C2 in Breast Cancer and Their Potential Effect on Progesterone Signaling
Bibliographic record
Abstract
Progesterone plays an essential role in breast development and cancer formation. The local metabolism of progesterone may limit its interactions with the progesterone receptor (PR) and thereby act as a prereceptor regulator. Selective loss of AKR1C1, which encodes a 20alpha-hydroxysteroid dehydrogenase [20alpha-HSD (EC 1.1.1.149)], and AKR1C2, which encodes a 3alpha-hydroxysteroid dehydrogenase [3alpha-HSD (EC 1.1.1.52)], was found in 24 paired breast cancer samples as compared with paired normal tissues from the same individuals. In contrast, AKR1C3, which shares 84% sequence identity, and 5alpha-reductase type I (SRD5A1) were minimally affected. Breast cancer cell lines T-47D and MCF-7 also expressed reduced AKR1C1, whereas the breast epithelial cell line MCF-10A expressed AKR1C1 at levels comparable with those of normal breast tissues. Immunohistochemical staining confirmed loss of AKR1C1 expression in breast tumors. AKR1C3 and AKR1C1 were localized on the same myoepithelial and luminal epithelial cell layers. Suppression of ARK1C1 and AKR1C2 by selective small interfering RNAs inhibited production of 20alpha-dihydroprogesterone and was associated with increased progesterone in MCF-10A cells. Suppression of AKR1C1 alone or with AKR1C2 in T-47D cells led to decreased growth in the presence of progesterone. Overexpression of AKR1C1 and, to a lesser extent, AKR1C2 (but not AKR1C3) decreased progesterone-dependent PR activation of a mouse mammary tumor virus promoter in both prostate (PC-3) and breast (T-47D) cancer cell lines. We speculate that loss of AKR1C1 and AKR1C2 in breast cancer results in decreased progesterone catabolism, which, in combination with increased PR expression, may augment progesterone signaling by its nuclear receptors.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".