Long noncoding RNA ERICD interacts with ARID3A via E2F1 and regulates migration and proliferation of osteosarcoma cells
Bibliographic record
Abstract
Long noncoding RNA (lncRNA) dysregulation is known to be taking part in majority of cancers, including osteosarcoma. In one of our previous studies, we showed that lncRNA MEG3 is being regulated by microRNA-664a (miR-664a) suppresses the migratory potential of osteosarcoma cells (U-2OS). We now report a novel lncRNA, namely, ERICD, which is linked to the transcription factor AT-rich interaction domain 3A (ARID3A) in U-2OS cells. We show that ARID3A binds to ERICD and indirectly interacts with each other via the E2F transcription factor 1 (E2F1). Furthermore, small interfering RNA (siRNA)-mediated knockdown of ERICD inhibited cell migration, formation of colonies, and proliferation in U-2OS cells. Overexpression of ARID3A inhibited cell migration, colony formation, and proliferation, whereas siRNA-mediated knockdown of ARID3A promoted cell migration, colony formation, and proliferation. Our findings indicate that ARID3A and lncRNA ERICD have plausible tumor suppressive and oncogenic functions, respectively, in osteosarcoma. Our data demonstrate the converse interaction between ARID3A and lncRNA ERICD that target DNA-binding proteins and dysregulation of their expression through E2F1 augments osteosarcoma progression. The cell rescue experiment also indicated E2F1 to be involved in the regulation of ARID3A and ERICD.
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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) | 0.001 | 0.001 |
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".