How MicroRNAs Command the Battle against Cancer
Bibliographic record
Abstract
MicroRNAs (miRNAs) are small RNA molecules that regulate more than 30% of genes in humans. Recent studies have revealed that miRNAs play a crucial role in tumorigenesis. Large sets of miRNAs in human tumors are under-expressed compared to normal tissues. Furthermore, experiments have shown that interference with miRNA processing enhances tumorigenesis. Multiple studies have documented the causal role of miRNAs in cancer, and miRNA-based anticancer therapies are currently being developed. This review primarily focuses on two key points: (1) miRNAs and their role in human cancer and (2) the regulation of tumor suppressors by miRNAs. The review discusses (a) the regulation of the tumor suppressor p53 by miRNA, (b) the critical role of the miR-144/451 cluster in regulating the Itch-p63-Ago2 pathway, and (c) the regulation of PTEN by miRNAs. Future research and the perspectives of miRNA in cancer are also discussed. Understanding these pathways will open avenues for therapeutic interventions targeting miRNA regulation.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".