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Record W4394544049 · doi:10.6084/m9.figshare.8052740

MicroRNA-10b controls the metastasis and proliferation of colorectal cancer cells by regulating Krüppel-like factor 4

2019· dataset· en· W4394544049 on OpenAlexaff
Yue Xie, Jing Zhao, Yanling Liang, Min Chen, Yihong Luo, Xiaobing Cui, Bo Jiang, Liang Peng, Xinying Wang

Bibliographic record

VenueFigshare · 2019
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicKruppel-like factors research
Canadian institutionsUniversity of British ColumbiaJewish General Hospital
Fundersnot available
KeywordsKrüppelColorectal cancermicroRNAMetastasisCancer researchOncologyBiologyMedicineInternal medicineCancerGeneticsTranscription factorGene

Abstract

fetched live from OpenAlex

Mir-10b has been reported as a key regulator of metastasis in many human tumours. Moreover, it has also been regarded as a prognostic marker and therapeutic target of colorectal cancer (CRC). Whether miR-10b could affect the metastasis and proliferation of CRC is unclear. MiR-10b expression was detected by qPCR in human CRC tissues and cell line, Luciferase activity was employed for miR-10b binding to the 3`UTR of KLF4, Genes expression were examined by western blot, and mRNA by qPCR. PI and Annexin V staining were used to evaluate the cell cycle and apoptosis. Cell proliferation was detected with MTT, and cell migration and invasion were performed with Transwell assay. We found that miR-10b expression was up-regulated in metastatic CRC tissues and cell lines. Inhibition of miR-10b prevented cancer cell metastasis and growth by inducing cell-cycle arrest and apoptosis <i>in vitro</i>. Moreover, we found that KLF4 was a direct target of miR-10b. MiR-10b inhibitor led to the up-regulation of E-cadherin expression and the down-regulation of cyclin D1, which were partly abrogated after silencing KLF4.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.045
Threshold uncertainty score1.000

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)0.0140.000

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 head0.026
GPT teacher head0.300
Teacher spread0.273 · 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.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2019
Admission routes1
Has abstractyes

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