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Record W2952464260 · doi:10.1002/ijc.32368

MiR‐16‐1‐3p and miR‐16‐2‐3p possess strong tumor suppressive and antimetastatic properties in osteosarcoma

2019· article· en· W2952464260 on OpenAlexaff
Vadim Maximov, Rania Akkawi, Saleh Khawaled, Zaidoun Salah, Ahlam Barhoum, Omer Or, Marco Galasso, Kyle C. Kurek, Eylon Yavin, Rami I. Aqeilan

Bibliographic record

VenueInternational Journal of Cancer · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
FundersLady Davis Fellowship Trust, Hebrew University of JerusalemHebrew University of JerusalemIsrael Cancer Research Fund
KeywordsOsteosarcomaCancer researchMedicineInternal medicineOncology

Abstract

fetched live from OpenAlex

Osteosarcoma (OS) is an aggressive malignancy affecting mostly children and adolescents. MicroRNAs (miRNAs) play important roles in OS development and progression. Here we found that miR-16-1-3p and miR-16-2-3p "passenger" strands, as well as the "lead" miR-16-5p strand, are frequently downregulated and possess strong tumor suppressive functions in human OS. Furthermore, we report different although strongly overlapping functions for miR-16-1-3p and miR-16-2-3p in OS cells. Ectopic expression of these miRNAs affected primary tumor growth, metastasis seeding and chemoresistance and invasiveness of human OS cells. Loss-of-function experiments verified tumor suppressive functions of these miRNAs at endogenous levels of expression. Using RNA immunoprecipitation (RIP) assays, we identify direct targets of miR-16-1-3p and miR-16-2-3p in OS cells. Moreover, validation experiments identified FGFR2 as a direct target for miR-16-1-3p and miR-16-2-3p. Overall, our findings underscore the importance of passenger strand miRNAs, at least some, in osteosarcomagenesis.

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 score0.236
Threshold uncertainty score0.372

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 head0.009
GPT teacher head0.278
Teacher spread0.269 · 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

Citations47
Published2019
Admission routes1
Has abstractyes

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