Tumour-suppressor microRNAs regulate ovarian cancer cell physical properties and invasive behaviour
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".