miR-638: A Promising Cancer Biomarker with Therapeutic Potential
Bibliographic record
Abstract
BACKGROUND: There is an unmet need to improve the diagnosis of cancer with precise treatment strategies. Therefore, more powerful diagnostic, prognostic, and therapeutic biomarkers are needed to overcome tumor cells. microRNAs (miRNAs, miRs), as a class of small non-coding RNAs, play essential roles in cancer through the tumor-suppressive or oncogenic effects by post-transcriptional regulation of their targets. Many studies have provided shreds of evidence on aberrantly expressed miRNAs in numerous cancers and have shown that miRNAs could play potential roles as diagnostic, prognostic, and even therapeutic biomarkers in patients with cancers. Findings have revealed that miR-638 over or underexpression might play a critical role in cancer initiation, development, and progression. However, the mechanistic effects of miR-638 on cancer cells are still controversial. CONCLUSION: In the present review, we have focused on the diagnostic, prognostic, and therapeutic potentials of miR-638 and discussed its mechanistic roles in various types of 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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".