Circ_KATNAL1 regulates prostate cancer cell growth and invasiveness through the miR-145-3p/WISP1 pathway
Bibliographic record
Abstract
Prostate cancer (PCa) is the second leading cause of death in men, and current studies have shown that circular RNAs (circRNAs) play important roles in its occurrence and development. Detection of circRNAs in PCa cells showed that circ_KATNAL1 is down-regulated, mainly located in the cytoplasm, and contains multiple binding sites of miR-145-3p, which is an anticancer miRNA. RNA immunoprecipitation with anti-AGO2 antibody, RNA pull-down assays with biotin-labeled circ_KATNAL1 probe or an miR-145-3p mimic, and dual luciferase reporter gene assays confirmed that circ_KATNAL1 binds directly to miR-145-3p in cells, and that WISP1, which is highly expressed in many types of tumors, is an important target gene of miR-145-3p. Circ_KATNAL1 and miR-145-3p promote each other’s expression, and down-regulate the expression of the target gene WISP1. Both circ_KATNAL1 and miR-145-3p inhibit cell proliferation, invasiveness, and migration, down-regulate the expression of MMP-2 and MMP-9, promote cell apoptosis and the activation of caspase-3, caspase-8, caspase-9, and PARP, whereas WISP1 has the opposite effect, and the above-mentioned functions of circ_KATNAL1 were achieved through the miR-145-3p/WISP1 pathway. Therefore, circ_KATNAL1 plays an anticancer role in PCa cells through the miR-145-3p/WISP1 pathway, which could be an important target for the diagnosis and treatment of PCa.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".