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Record W2915716416 · doi:10.1139/bcb-2018-0354

MiR-204 inhibits hepatocellular cancer drug resistance and metastasis through targeting NUAK1

2019· article· en· W2915716416 on OpenAlexvenueno aff
Yuhui Yu, Yongsheng Wang, Xiangying Xiao, Wei Cheng, Liqiang Hu, Weiyun Yao, Zhangxuan Qian, Wei Wu

Bibliographic record

VenueBiochemistry and Cell Biology · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsnot available
Fundersnot available
KeywordsLiver cancerMetastasisCancer researchCancerMedicineHepatocellular carcinomaInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score0.921

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.226
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations46
Published2019
Admission routes1
Has abstractyes

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