Deregulation of protein phosphatase 2A inhibitor SET is associated with malignant progression in breast cancer
Bibliographic record
Abstract
To understand the mechanism underlying metastasis, identification of a mechanism-based and common biomarker for circulating tumour cells (CTCs) in heterogenous breast cancer is needed. SET, an endogenous inhibitor of protein phosphatase 2A, was overexpressed in all subtypes of invasive breast carcinoma tissues. Treatment with SET-targeted siRNAs reduced the motility of MCF-7 and MDA-MB-231 cells in transwell assay. SET knockdown reduced the number of mammospheres by 60-70% in MCF-7 and MDA-MB-231 cells, which was associated with the downregulation of OCT4 and SLUG. Hence, we analysed the presence of SET-expressing CTCs (SET-CTCs) in 24 breast cancer patients. CTCs were enriched using a size-based method and then immunocytochemically analysed using an anti-SET antibody. SET-CTCs were detected in 6/6 (100%) patients with recurrent breast cancer with a median value of 12 (12 cells/3 mL blood), and in 13/18 (72.2%) patients with stage I-III breast cancer with a median value of 2.5, while the median value of healthy controls was 0. Importantly, high numbers of SET-CTCs were correlated with lymph node metastasis in patients with stage I-III disease. Our results indicate that SET contributes to breast cancer progression and can act as a potential biomarker of CTCs for the detection of metastasis.
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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 | 0.000 |
| 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".