Pan-cancer analysis of long non-coding RNA NEAT1 in various cancers
Bibliographic record
Abstract
Changes in the abundance and activity of long non-coding RNAs (lncRNAs) have an important impact on the development of cancer. The nuclear paraspeckle assembly transcript 1 (NEAT1) has been reported to be overexpressed in many types of cancer since its discovery. However, inconsistencies exist as NEAT1 can also function as a tumor suppressor in certain types of cancer, such as acute promyelocytic leukemia. Here we systematically describe our current understanding of NEAT1 in tumor initiation and progression. First, we analyzed the expression patterns of NEAT1 in various normal tissues and malignant cancers using data from public data portals, the Genotype-Tissue Expression Project (GTEx) and the Cancer Genome Atlas (TCGA), together with recent progress in the study of NEAT1 in various types of cancer. Second, we discussed the functions and mechanisms of NEAT1 in modulating tumor activity. Then, the upstream transcription factors and downstream microRNA targets of NEAT1 in the transcription cascade of cancers were also summarized. These data highlight the emerging role of NEAT1 in tumorigenesis, and present promising targetable pathways and clinical opportunities for tumor prevention and classifications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".