Role of Secreted Frizzled-Related Protein 1 in Early Breast Carcinogenesis and Breast Cancer Aggressiveness
Bibliographic record
Abstract
A human transcriptome array on ERα-positive breast cancer continuum of risk identified Secreted Frizzled-Related Protein 1 (SFRP1) as decreased during breast cancer progression. In addition, SFRP1 was inversely associated with breast tissue age-related lobular involution, and differentially regulated in women with regard to their parity status and the presence of microcalcifications. The causal role of SFRP1 in breast carcinogenesis remains, nevertheless, not well understood. In this study, we characterized mammary epithelial cells from both nulliparous and multiparous mice in organoid culture ex vivo, in the presence of estradiol (E2) and/or hydroxyapatite microcalcifications (HA). Furthermore, we have modulated SFRP1 expression in breast cancer cell lines, including the MCF10A series, and investigated their tumoral properties. We observed that organoids obtained from multiparous mice were resistant to E2 treatment, while organoids obtained from nulliparous mice developed the luminal phenotype associated with a lower ratio between Sfrp1 and Esr1 expression. The decrease in SFRP1 expression in MCF10A and MCF10AT1 cell lines increased their tumorigenic properties in vitro. On the other hand, the overexpression of SFRP1 in MCF10DCIS, MCF10CA1a, and MCF7 reduced their aggressiveness. Our results support the hypothesis that a lack of SFRP1 could have a causal role in early breast carcinogenesis.
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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".