Micro-RNAs, the Cornerstones of the Future of Radiobiology in Head and Neck Cancers?
Bibliographic record
Abstract
Even though it is only the 6th most common malignancy at the modal level, head and neck cancers are distinguished by a considerable treatment failure rate, especially by locoregional recurrences, the intrinsic tumor radioresistance being one of the causes of this phenomenon. The efforts of radiobiological research of these cancers are oriented towards the identification of biomarkers associated with radioresistance and radiosensitivity in order to modulate the treatment so that the therapeutic benefit is maximum. Micro-RNAs (miRNAs, miRs), small single-stranded non-coding RNA molecules are currently being extensively evaluated as potential biomarkers in numerous diseases, including cancer. The evaluation of the potential of miRNAs to modulate or predict radiosensitivity or radioresistance, to anticipate the risk of recurrence and metastasis, and to differentiate different tumor subtypes is based on multiple mechanisms by which mRNAs control proliferation and apoptosis and interact with cell cycle phases or act as oncogenes with the potential to influence invasion promotion or tumor suppression. A refinement of radiosensitivity based on miRNAs with clinical and radiobiological application in head and neck cancers can lead to a personalization of radiotherapy. Thus, a miRNA signature can anticipate the risk of toxicity associated with chemoradiation, the possibility of obtaining locoregional control after treatment, and the recurrence and distant metastasis risk. The potential of miRNAs as an intrinsic predictor of sensitivity to chemotherapy may also guide the therapeutic decision toward choosing an escalation or de-escalation of concurrent or sequential systemic treatment. The choice of the irradiated dose, the fractional dose, the fractionation scheme, and the refining of the dose-volume constraints depending on the radiosensitivity of each tissue type estimated on a case-by-case basis by miRNAs profile are possible concepts for the future radiotherapy and radiobiology of head and neck cancers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".