miR-590-3p Promotes Ovarian Cancer Growth and Metastasis via a Novel FOXA2–Versican Pathway
Bibliographic record
Abstract
Abstract miRNAs play important roles in gene regulation, and their dysregulation is associated with many diseases, including epithelial ovarian cancer (EOC). In this study, we determined the expression and function of miR-590-3p in EOC. miR-590-3p levels were higher in high-grade carcinoma when compared with low-grade or tumors with low malignant potential. Interestingly, plasma levels of miR-590-3p were significantly higher in patients with EOC than in subjects with benign gynecologic disorders. Transient transfection of miR-590-3p mimics or stable transfection of mir-590 increased cell proliferation, migration, and invasion. In vivo studies revealed that mir-590 accelerated tumor growth and metastasis. Using a cDNA microarray, we identified forkhead box A2 (FOXA2) and versican (VCAN) as top downregulated and upregulated genes by mir-590, respectively. miR-590-3p targeted FOXA2 3′ UTR to suppress its expression. In addition, knockdown or knockout of FOXA2 enhanced cell proliferation, migration, and invasion. Overexpression of FOXA2 decreased, whereas knockout of FOXA2 increased VCAN mRNA and protein levels, which was due to direct binding and regulation of the VCAN gene by FOXA2. Interrogation of the TCGA ovarian cancer database revealed a negative relationship between FOXA2 and VCAN mRNA levels in EOC tumors, and high FOXA2/low VCAN mRNA levels in tumors positively correlated with patient survival. Finally, overexpression of FOXA2 or silencing of VCAN reversed the effects of mir-590. These findings demonstrate that miR-590-3p promotes EOC development via a novel FOXA2–VCAN pathway. Significance: Low FOXA2/high VCAN levels mediate the tumor-promoting effects of miR-590-3p and negatively correlate with ovarian cancer survival. Cancer Res; 78(15); 4175–90. ©2018 AACR.
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) | 0.001 | 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".