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Record W2991430029 · doi:10.1139/bcb-2019-0111

MiR-539 inhibits the malignant behavior of breast cancer cells by targeting SP1

2019· article· en· W2991430029 on OpenAlexvenueno aff
Fenglin Cai, Luhong Chen, Yuting Sun, Chunlan He, Deyuan Fu, Jinhai Tang

Bibliographic record

VenueBiochemistry and Cell Biology · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsnot available
FundersNational Key Research and Development Program of ChinaNatural Science Foundation of Jiangsu ProvinceGovernment of Jiangsu Province
KeywordsBreast cancerGene knockdownCancer researchmicroRNAApoptosisMetastasisCancerMedicineCancer cellCell growthEpithelial–mesenchymal transitionBiologyInternal medicineGene

Abstract

fetched live from OpenAlex

The aberrant expression of microRNAs (miRNAs) is involved in the initiation and progression of human cancers. In our study, we found that miR-539 was down-regulated in breast cancer tissues and cell lines. Decreased expression of miR-539 was significantly associated with lymph node metastasis in patients with breast cancer. Overexpression of miR-539 inhibited the proliferation and promoted apoptosis of breast cancer cells. Moreover, highly expressed miR-539 significantly suppressed the epithelial-mesenchymal transition (EMT) and sensitized cells to cisplatin treatment. Mechanistically, miR-539 was found to target the specificity protein 1 (SP1) and down-regulated the expression of SP1 in breast cancer cells. Knockdown of miR-539 consistently increased the expression of SP1. The expression of miR-539 in breast cancer tissues was negatively correlated with the expression of SP1. Restoration of SP1 significantly attenuated the inhibitory effect of miR-539 on the proliferation of breast cancer cells. Taken together, our results indicate that miR-539 has a tumor suppressive role in breast cancer via targeting SP1, suggesting miR-539 as a promising target for the diagnosis of breast cancer.

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 score0.006
Threshold uncertainty score0.498

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 head0.004
GPT teacher head0.213
Teacher spread0.210 · 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

Citations31
Published2019
Admission routes1
Has abstractyes

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