MicroRNAs in clinical oncology: at the crossroads between promises and problems
Bibliographic record
Abstract
MicroRNAs (miRNAs) are small RNAs that do not code for proteins, but function by controlling protein expression of other genes. miRNAs have been shown to control cell growth, differentiation and apoptosis. Shortly after their discovery, miRNAs have been found to be associated with cancer. Earlier reports have shown that human cancers frequently show a distorted expression profile of miRNAs. In this review, the biogenesis of miRNAs and potential mechanisms of their dysregulation and involvement in cancer pathogenesis are discussed. The current literature on potential applications of miRNAs in the field of clinical oncology from diagnostic to prognostic and predictive applications at the tissue, and more recently, serum levels, is reviewed. The potential therapeutic applications of miRNAs and RNAi in the field of cancer are summarised. Finally, some of the potential challenges that face the transition of miRNAs from a research setting into a clinical application are highlighted, with a future prospective of the incorporation of miRNAs in cancer patient management.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| 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".