Prognostic Effect of Long Noncoding RNA NEAT1 Expression Depends on p53 Mutation Status in Cancer
Bibliographic record
Abstract
Recently, many studies have revealed that long noncoding RNAs (lncRNAs) play important roles in various biological and pathological processes. Our previous study reported that lncRNA NEAT1 is a direct transcriptional target of p53. NEAT1 is an essential component of paraspeckles, which have recently been identified as a novel type of nuclear compartment. Although our previous findings indicate that NEAT1 induction contributes to the tumor-suppressor function of p53, the role of NEAT1 in cancer is still controversial. In this study, we comprehensively analyzed the relationship between NEAT1 expression and p53 mutation status. Interestingly, survival analysis based on NEAT1 expression in several cancer tissues revealed that the p53 wild-type group with high NEAT1 expression had a good prognosis, while poor prognosis or no correlation between NEAT1 expression and survival was observed in the p53-mutated group. These results demonstrate that the tumor-suppressive effect of NEAT1 depends on p53 function and is consistent with our previous report showing that NEAT1 supports the tumor-suppressive function of p53. Specifically, NEAT1 seems to play a tumor-suppressive role only in the presence of wild-type p53. These results provide important clues to the roles of NEAT1 in 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".