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Record W3013529030 · doi:10.3390/cancers12040795

Tetraspanin CD9 is Regulated by miR-518f-5p and Functions in Breast Cell Migration and In Vivo Tumor Growth

2020· article· en· W3013529030 on OpenAlexaff
Danielle R. Bond, Richard G. S. Kahl, Joshua S. Brzozowski, Helen Jankowski, Crystal Naudin, Mamta Pariyar, Kelly A. Avery‐Kiejda, Christopher J. Scarlett, Claude Boucheix, William J. Muller, Leonie K. Ashman, Murray J. Cairns, Séverine Roselli, Judith Weidenhofer

Bibliographic record

VenueCancers · 2020
Typearticle
Languageen
FieldMedicine
TopicCell Adhesion Molecules Research
Canadian institutionsMcGill University
FundersCancer Institute NSWHunter Medical Research InstituteHunter Cancer Research Alliance
KeywordsCancer researchMetastasisBreast cancerTetraspaninCell growthCancerCell migrationmicroRNAProstate cancerBiologyMedicineCellInternal medicineGene

Abstract

fetched live from OpenAlex

Breast cancer is the most commonly diagnosed and the second leading cause of cancer-related mortality among women worldwide. miR-518f-5p has been shown to modulate the expression of the metastasis suppressor CD9 in prostate cancer. However, the role of miR-518f-5p and CD9 in breast cancer is unknown. Therefore, this study aimed to elucidate the role of miR-518f-5p and the mechanisms responsible for decreased CD9 expression in breast cancer, as well as the role of CD9 in de novo tumor formation and metastasis. miR-518f-5p function was assessed using migration, adhesion, and proliferation assays. miR-518f-5p was overexpressed in breast cancer cell lines that displayed significantly lower CD9 expression as well as less endogenous CD9 3'UTR activity, as assessed using qPCR and dual luciferase assays. Transfection of miR-518f-5p significantly decreased CD9 protein expression and increased breast cell migration in vitro. Cd9 deletion in the MMTV/PyMT mouse model impaired tumor growth, but had no effect on tumor initiation or metastasis. Therefore, inhibition of miR-518f-5p may restore CD9 expression and aid in the treatment of breast cancer metastasis.

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 score0.342
Threshold uncertainty score0.402

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 head0.011
GPT teacher head0.244
Teacher spread0.233 · 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

Citations19
Published2020
Admission routes1
Has abstractyes

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