MicroRNAs as oncogenes or tumour suppressors in oesophageal cancer: potential biomarkers and therapeutic targets
Bibliographic record
Abstract
MicroRNAs are a class of small, non-coding RNAs that can negatively regulate protein-coding genes, and are associated with almost all known physiological and pathological processes, especially cancer. The number of studies documenting miRNA expression patterns in malignancy continues to expand rapidly, with continuously gained critical information regarding how aberrantly expressed miRNAs may contribute to carcinogenesis. miRNAs can influence cancer pathogenesis, playing a potential role as either oncogenes or tumour suppressors. Recently, several miRNAs have been reported to exert different regulatory functions in oesophageal cancer - the carcinoma typically arising from the epithelial lining of the oesophagus. These miRNAs also have potential clinical applications towards developing biomarkers or targets for possible use in diagnosis or therapy in oesophageal cancer. In this review, we have summarized the two (oncogenic or tumour suppressive) roles of miRNAs here, and their applications as potential biomarkers or therapeutic targets, which may illuminate future treatment for oesophageal cancer.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".