MicroRNAs (miRNAs) in cancer metastasis: Molecular interactions and possible therapeutic targets
Bibliographic record
Abstract
As a global disease with a high mortality rate, cancer remains a difficult-to-treat disorder, which is still challenging despite the numerous studies carried out in this field. Cancers have a heterogeneous nature which helps them to escape from the immune system and therapies. Conventional cancer therapies include surgery, radiation therapy, and chemotherapy. However, studies of targeted therapy and combinational therapies show promising results. Non-coding RNAs are molecules with critical regulatory functions in cells and can be divided into two main types: short non-coding RNAs including microRNAs (miRNAs) and long non-coding RNAs. miRNAs regulate gene expression at transcription and post-transcription levels and regulate protein function. They also play key regulatory roles in cancer and mediate various hallmarks of cancer including metastasis. For survival, cancer cells alter their microenvironment and neighboring cellular metabolism, with their increasing need for energy. Epithelial–mesenchymal transition is one of the mechanisms that enhance metastasis and invasion of tumor cells. This chapter discusses our current knowledge of miRNAs’ regulatory role in cancer metastasis.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".