RP5-1120P11.3 promotes hepatocellular carcinoma development via the miR-196b-5p–WIPF2 axis
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) remains a huge threat to human health even though the diagnosis and treatment strategies have improved rapidly in the past few decades. Increasing evidence has illustrated the critical role noncoding RNA and their regulatory network play in the pathology of HCC. Here, we identified a novel long noncoding RNA, RP5-1120P11.3, that is ectopically expressed in HCC. Further characterization of RP5-1120P11.3 revealed that it promoted proliferation and invasion of HCC cells while inhibiting apoptosis. Importantly, our data revealed that miR-196b-5p interacted with and was regulated by RP5-1120P11.3 via a sponging mechanism. Inhibition of miR-196b-5p attenuated the phenotypes resulting from RP5-1120P11.3 inhibition. Moreover, our data showed that miR-196b-5p inhibited the expression of WIPF2 in HCC, illustrating a regulatory axis of RP5-1120P11.3-miR-196b-5p-WIPF2 that facilitated the progression of HCC. In addition, our data showed that RP5-1120P11.3 contributed to xenograft generation in vivo by regulating miR-196b-5p and WIPF2. These findings suggested that the RP5-1120P11.3-miR-196b-5p-WIPF2 axis is a potential target for treatment of HCC.
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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 | 0.001 |
| 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".