MicroRNAs in nasopharyngeal carcinoma
Bibliographic record
Abstract
It is becoming increasingly evident that aberrantly expressed microRNAs (miRNAs) are responsible for a number of disease processes, including cancer initiation and progression. miRNAs have been implicated as key players in numerous neoplasms, including nasopharyngeal carcinoma (NPC). Functionally, deregulation of miRNAs that act either as tumour suppressors or oncogenes results in numerous cancer-associated phenomena, including changes in proliferation, migration, and cell survival. Furthermore, miRNA expression has been associated with chemoresistant or radioresistant phenotypes; highlighting the importance of miRNAs in mediating oncogenic processes. Prognostic and predictive miRNA signatures have been defined for a variety of cancer types, including NPC, whereby these signatures offer a potentially important clinical tool for assessing the disease state, as well as predicting treatment response and clinical outcome. Therefore, further examination and validation of miRNAs that are deregulated in NPC will provide insight into the fundamental drivers of this disease, which will aid in the identification of novel targeted treatments. This review summarizes recent advances in the study of miRNAs in NPC, with specific discussion on the role of miRNAs in NPC pathogenesis and the potential utility of miRNAs as prognostic biomarkers. Our increasing understanding of the role of miRNAs in NPC tumorigenesis and their application as novel biomarkers will undoubtedly prove useful in the stratification of future patients into clinically relevant treatment classifications, thereby improving and personalizing disease management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".