MiR-204 inhibits hepatocellular cancer drug resistance and metastasis through targeting NUAK1
Bibliographic record
Abstract
Liver cancer is a leading cause of cancer-related deaths globally. Tumor response rate of liver cancer patients towards systemic chemotherapy is low and chemoresistance can easily develop. Identifying novel molecules that can repress drug resistance and metastasis of liver cancer will facilitate the development of new therapeutic strategies. The aim of this study is to determine the roles of NUAK1 and miR-204 in the drug resistance and metastasis of liver cancer and to reveal their relationship. We found that NUAK1 was increased in the tumor of primary liver cancer. Knockdown of NUAK1 significantly inhibited cell growth and migration. Moreover, NUAK1 was the direct downstream target of miR-204, and there was clinical relevance between miR-204 down-regulation and NUAK1 up-regulation in liver cancer. Furthermore, we found that miR-204 increased drug sensitivity by down-regulating NUAK1 expression. Based on these results, we identified miR-204 as a tumor suppressor by inhibiting NUAK1 expression in liver cancer, indicating both miR-204 and NUAK1 may act as promising targets for liver cancer therapy.
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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) | 0.002 | 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".