MicroRNA miR-30 family regulates non-attachment growth of breast cancer cells
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: A subset of breast cancer cells displays increased ability to self-renew and reproduce breast cancer heterogeneity. The characterization of these so-called putative breast tumor-initiating cells (BT-ICs) may open the road for novel therapeutic strategies. As microRNAs (miRNAs) control developmental programs in stem cells, BT-ICs may also rely on specific miRNA profiles for their sustained activity. To explore the notion that miRNAs may have a role in sustaining BT-ICs, we performed a comprehensive profiling of miRNA expression in a model of putative BT-ICs enriched by non-attachment growth conditions. RESULTS: We found breast cancer cells grown under non-attachment conditions display a unique pattern of miRNA expression, highlighted by a marked low expression of miR-30 family members relative to parental cells. We further show that miR-30a regulates non-attachment growth. A target screening revealed that miR-30 family redundantly modulates the expression of apoptosis and proliferation-related genes. At least one of these targets, the anti-apoptotic protein AVEN, was able to partially revert the effect of miR-30a overexpression. Finally, overexpression of miR-30a in vivo was associated with reduced breast tumor progression. CONCLUSIONS: miR30-family regulates the growth of breast cancer cells in non-attachment conditions. This is the first analysis of target prediction in a whole family of microRNAs potentially involved in survival of putative BT-ICs.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it