Tumour-suppressor microRNAs regulate ovarian cancer cell physical properties and invasive behaviour
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
The activities of pathways that regulate malignant transformation can be influenced by microRNAs (miRs). Recently, we showed that increased expression of five tumour-suppressor miRs, miR-508-3p, miR-508-5p, miR-509-3p, miR-509-5p and miR-130b-3p, correlate with improved clinical outcomes in human ovarian cancer patients, and that miR-509-3p attenuates invasion of ovarian cancer cell lines. Here, we investigate the mechanism underlying this reduced invasive potential by assessing the impact of these five miRs on the physical properties of cells. Human ovarian cancer cells (HEYA8, OVCAR8) that are transfected with miR mimics representing these five miRs exhibit decreased invasion through collagen matrices, increased cell size and reduced deformability as measured by microfiltration and microfluidic assays. To understand the molecular basis of altered invasion and deformability induced by these miRs, we use predicted and validated mRNA targets that encode structural and signalling proteins that regulate cell mechanical properties. Combined with analysis of gene transcripts by real-time PCR and image analysis of F-actin in single cells, our results suggest that these tumour-suppressor miRs may alter cell physical properties by regulating the actin cytoskeleton. Our findings provide biophysical insights into how tumour-suppressor miRs can regulate the invasive behaviour of ovarian cancer cells, and identify potential therapeutic targets that may be implicated in ovarian cancer progression.
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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