Micro-RNAs as diagnostic or prognostic markers in human epithelial malignancies
Bibliographic record
Abstract
Micro-RNAs (miRs) are important regulators of mRNA and protein expression; the ability of miR expression profilings to distinguish different cancer types and classify their sub-types has been well-described. They also represent a novel biological entity with potential value as tumour biomarkers, which can improve diagnosis, prognosis, and monitoring of treatment response for human cancers. This endeavour has been greatly facilitated by the stability of miRs in formalin-fixed paraffin-embedded (FFPE) tissues, and their detection in circulation. This review will summarize some of the key dysregulated miRs described to date in human epithelial malignancies, and their potential value as molecular bio-markers in FFPE tissues and blood samples. There remain many challenges in this domain, however, with the evolution of different platforms, the complexities of normalizing miR profiling data, and the importance of evaluating sufficiently-powered training and validation cohorts. Nonetheless, well-conducted miR profiling studies should contribute important insights into the molecular aberrations driving human cancer development and progression.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".