The long and short non-coding RNAs modulating EZH2 signaling in cancer
Bibliographic record
Abstract
Non-coding RNAs (ncRNAs) are a large family of RNA molecules with no capability in encoding proteins. However, they participate in developmental and biological processes and their abnormal expression affects cancer progression. These RNA molecules can function as upstream mediators of different signaling pathways and enhancer of zeste homolog 2 (EZH2) is among them. Briefly, EZH2 belongs to PRCs family and can exert functional roles in cells due to its methyltransferase activity. EZH2 affects gene expression via inducing H3K27me3. In the present review, our aim is to provide a mechanistic discussion of ncRNAs role in regulating EZH2 expression in different cancers. MiRNAs can dually induce/inhibit EZH2 in cancer cells to affect downstream targets such as Wnt, STAT3 and EMT. Furthermore, miRNAs can regulate therapy response of cancer cells via affecting EZH2 signaling. It is noteworthy that EZH2 can reduce miRNA expression by binding to promoter and exerting its methyltransferase activity. Small-interfering RNA (siRNA) and short-hairpin RNA (shRNA) are synthetic, short ncRNAs capable of reducing EZH2 expression and suppressing cancer progression. LncRNAs mainly regulate EZH2 expression via targeting miRNAs. Furthermore, lncRNAs induce EZH2 by modulating miRNA expression. Circular RNAs (CircRNAs), like lncRNAs, affect EZH2 expression via targeting miRNAs. These areas are discussed in the present review with a focus on molecular pathways leading to clinical translation.
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) | 0.000 | 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".