CAPER, a novel regulator of human breast cancer progression
Bibliographic record
Abstract
CAPER is an estrogen receptor (ER) co-activator that was recently shown to be involved in human breast cancer pathogenesis. Indeed, we reported increased expression of CAPER in human breast cancer specimens. We demonstrated that CAPER was undetectable or expressed at relatively low levels in normal breast tissue and assumed a cytoplasmic distribution. In contrast, CAPER was expressed at higher levels in ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC) specimens, where it assumed a predominantly nuclear distribution. However, the functional role of CAPER in human breast cancer initiation and progression remained unknown. Here, we used a lentiviral-mediated gene silencing approach to reduce the expression of CAPER in the ER-positive human breast cancer cell line MCF-7. The proliferation and tumorigenicity of MCF-7 cells stably expressing control or human CAPER shRNAs was then determined via both in vitro and in vivo experiments. Knockdown of CAPER expression significantly reduced the proliferation of MCF-7 cells in vitro. Importantly, nude mice injected with MCF-7 cells harboring CAPER shRNAs developed smaller tumors than mice injected with MCF-7 cells harboring control shRNAs. Mechanistically, tumors derived from mice injected with MCF-7 cells harboring CAPER shRNAs displayed reduced expression of the cell cycle regulators PCNA, MCM7, and cyclin D1, and the protein synthesis marker 4EBP1. In conclusion, knockdown of CAPER expression markedly reduced human breast cancer cell proliferation in both in vitro and in vivo settings. Mechanistically, knockdown of CAPER abrogated the activity of proliferative and protein synthesis pathways.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
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".