The long non‐coding RNA MALAT1 promotes ovarian cancer progression by regulating RBFOX2‐mediated alternative splicing
Bibliographic record
Abstract
Ovarian cancer metastasizes via direct seeding, whereby cancer cells shed from the primary site, resist cell death in the peritoneal cavity, then metastasize to peritoneal organs. We sought to identify molecular mechanisms that facilitate ovarian cancer cell anchorage independent survival. Gene expression profiling was performed on ovarian cancer cells grown in attached or forced suspension culture and confirmed by RT-qPCR. Anoikis was measured by Caspase 3/7 assay. Since the long non-coding RNA Metastasis Associated Lung Adenocarcinoma transcript 1 (MALAT1) was among the transcripts most highly increased in forced suspension culture, modified anti-sense oligonucleotides (ASO) were used to inhibit its expression. Knockdown of RBFOX2 and KIF1B was performed using shRNAs. Publically available datasets were analyzed for association of MALAT1 gene expression with clinicopathological variables. In multiple anoikis-resistant ovarian cancer cell lines MALAT1 expression increased after 24 and 48 h in forced suspension culture compared to attached culture. High MALAT1 is associated with increased stage, recurrence, and reduced survival in ovarian cancer, and in a small percentage of ovarian cancers MALAT1 is amplified. MALAT1 knockdown resulted in decreased proliferation, invasion, anchorage-independent growth, and increased anoikis. Suppression of MALAT1 also resulted in decreased expression of RBFOX2, and alternative processing of the pro-apoptotic tumor suppressor gene KIF1B. RBFOX2 suppression resulted in preferential splicing of the pro-apoptotic isoform of KIF1B (KIFB1B-beta) and increased anoikis. The lncRNA MALAT1 facilitates a pro-metastatic phenotype in ovarian cancer by promoting alternative RNA processing and differential expression of anti-apoptosis and epithelial to mesenchymal transition (EMT)-related genes.
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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) | 0.002 | 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".