Cancer Stem-Cell Related miRNAs: Novel Potential Targets for Metastatic Prostate Cancer
Bibliographic record
Abstract
Globally Prostate Cancer is the second most commonly diagnosed and sixth leading cause of Cancer mortalities in men worldwide but currently there is no cure for metastatic castration-resistant prostate cancer (CRPC). Chemoresistance and metastasis are the main causes of treatment resistance and mortality in Prostate Cancer patients. Although several advances have been made to control yet there is an urgent need to investigate the mechanisms and pathways for chemoresistance and prostate cancer (PCa) metastasis. Cancer stem cells (CSCs), a sub-population of cancer cells characterised by self-renewal and tumor initiation, have gained intense attention as they not only play a crucial role in cancer relapse but also contribute substantially to chemoresistance. Contributing to the role of CSCs are the miRNAs which are known key regulators of the posttranscriptional regulation of genes involved in a wide array of biological processes including tumorigenesis. The altered expressions of miRNAs have been associated with not only with tumor development but also with invasion, angiogenesis, drug resistance, and metastasis. Thus identification of signature miRNA associated with EMT and CSCs would provide a novel therapeutic strategy for the improvement of current treatment thus leading to increase in patient survival.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".