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Record W2156733926 · doi:10.1159/000369669

MiR-378 Promotes the Migration of Liver Cancer Cells by Down-Regulating Fus Expression

2014· article· en· W2156733926 on OpenAlexaff
Ji‐chun Ma, Jiang Lin, Jun Qian, Qian Wei, Jiayu Yin, Burton B. Yang, Qin Tang, Xingxing Chen, Xiang‐mei Wen, Hong Guo, Zhaoqun Deng

Bibliographic record

VenueCellular Physiology and Biochemistry · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsHealth Sciences CentreSunnybrook Health Science Centre
Funders“333 Project” of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsCell migrationMetastasisCellCancer researchCell growthTransfectionBiologyDownregulation and upregulationmicroRNALiver cancerCancerCell culturePathologyCell biologyMedicineGene

Abstract

fetched live from OpenAlex

BACKGROUND: miR-378 regulates osteoblast differentiation and participates in tumor cell self-renewal and chemo-resistance. However, the function of miR-378 in liver cancer cell migration has not been reported to date. METHODS: miR-378 expression was examined using real-time quantitative PCR. HepG2 cell migration and liver cell invasion were examined using wound-healing and cell invasion assays. Additionally, HepG2 cell metastasis was analyzed in nude mice. RESULTS: miR-378 over-expression enhances HepG2 cell proliferation, migration and liver cell invasion. Typical metastatic lesions were found in the livers of mice injected with miR-378-transfected cells, and high levels of the CMV promoter were detected in the nodules, indicating that miR-378 promoted the metastasis of the tumor cells to the liver. We also demonstrated that miR-378 down-regulated Fus expression. CONCLUSIONS: Our results suggested that miR-378 enhanced cell migration and metastasis by down-regulating Fus expression.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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)0.0020.001

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.

Opus teacher head0.004
GPT teacher head0.203
Teacher spread0.198 · 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 source (direct Gemma or distilled Codex), 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

Citations64
Published2014
Admission routes1
Has abstractyes

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