Long noncoding RNA NEAT1 promotes the growth of cervical cancer cells via sponging miR-9-5p
Bibliographic record
Abstract
Evidence has accumulated demonstrating that long noncoding RNAs (lncRNAs) participate in the initiation and progression of cancers. In this study, we found that the lncRNA nuclear paraspeckle assembly transcript 1 (NEAT1) is significantly increased in both cervical cancer tissues and cell lines. Overexpression of NEAT1 promoted the proliferation and migration of cervical cancer cells. Molecular studies uncovered that NEAT1 functions as competitive endogenous RNA (ceRNA), binding the micro-RNA miR-9-5p and suppressing its expression. However, we consistently found that when NEAT1 was highly expressed, it attenuated the inhibitory effect of miR-9-5p on the expression of PTEN and POU2F1, which are the targets of miR-9-5p. Consistent with the negative regulation of NEAT1 on miR-9-5p, restoration of miR-9-5p inhibited the growth-promoting effects of NEAT1 on cervical cancer cells. Taken together, these results indicated that NEAT1 plays an important role in the regulation cervical cancer cell growth by targeting miR-9-5p. Our findings characterized the possible mechanism of NEAT1 in cervical 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 | 0.000 | 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".