Data from Extracellular Vesicle–Packaged circATP2B4 Mediates M2 Macrophage Polarization via miR-532-3p/SREBF1 Axis to Promote Epithelial Ovarian Cancer Metastasis
Bibliographic record
Abstract
Abstract Ovarian cancer is one of the most common gynecologic malignancies with a highly immunosuppressive tumor microenvironment (TME) and poor prognosis. Circular RNA (circRNA) is a type of noncoding RNA with high stability, which has been shown to play an important role in biological processes and TME reprogramming in a variety of tumors. The biological function of a novel circRNA, circATP2B4, in epithelial ovarian cancer (EOC) was detected and evaluated. Transmission electron microscopy, differential ultracentrifugation and qRT-PCR were used to verify the existence of extracellular vesicles (EV)-packaged circATP2B4. Macrophage uptake of circATP2B4 was determined by EVs tracing. Dual luciferase reporter, FISH, Western blotting, and flow cytometry assays were used to investigate the interactions between circATP2B4 and miR-532-3p as well as sterol regulatory element-binding factor 1 (SREBF1) expression in macrophages. CircATP2B4 was upregulated in EOC tissues and positively correlated with ovarian cancer progression. Functionally, circATP2B4 promoted carcinogenic progression and metastasis of EOC both in vitro and in vivo. Mechanistically, EV-packaged circATP2B4 in EOC could be transmitted to infiltrated macrophages and acted as competing endogenous RNA of miR-532-3p to relieve the repressive effect of miR-532-3p on its target SREBF1. Furthermore, circATP2B4 induced macrophage M2 polarization by regulating the miR-532-3p/SREBF1/PI3Kα/AKT axis, thereby leading to immunosuppression and ovarian cancer metastasis. Collectively, these data indicate that circATP2B4-containing EVs generated by EOC cells promoted M2 macrophages polarization and malignant behaviors of EOC cells. Thus, targeting EVs-packaged circATP2B4 may provide a potential diagnosis and treatment strategy for ovarian cancer.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".