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Record W1854469936 · doi:10.18632/oncotarget.717

MicroRNA miR-98 inhibits tumor angiogenesis and invasion by targeting activin receptor-like kinase-4 and matrix metalloproteinase-11

2012· article· en· W1854469936 on OpenAlexafffundabout
Vinayakumar Siragam, Zina Jeyapalan Rutnam, Weining Yang, Ling Fang, Linlin Luo, Xiangling Yang, M Li, Zhaoqun Deng, Jun Qian, Chun Peng, Burton B. Yang

Bibliographic record

VenueOncotarget · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsYork UniversityUniversity of TorontoHealth Sciences CentreSunnybrook Health Science Centre
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsAngiogenesisCarcinogenesisCancer researchmicroRNACell growthCancerBiologyMedicineInternal medicineGenetics

Abstract

fetched live from OpenAlex

// Vinayakumar Siragam 1 , 4,* Zina Jeyapalan Rutnam 1 , 4,* , Weining Yang 2 , Ling Fang 1 , 4 , Linlin Luo 2 , Xiangling Yang 1 , 4 , Minhui Li 1 , 4 , Zhaoqun Deng 1 , 4 , Jun Qian 3 , Chun Peng 2 , and Burton B. Yang 1 , 4 1 Sunnybrook Research Institute, Sunnybrook Health Sciences Centre 2 Departments of Biology, York University 3 The Affiliated People’s Hospital of Jiangsu University, Zhenjiang City, China 4 Department of Laboratory Medicine and Pathobiology, University of Toronto * denotes equal contribution Correspondence: Burton B. Yang, email: // Keywords : microRNA, miR-98, angiogenesis, tumorigenesis, invasion Received : October 22, 2012, Accepted : November 02, 2012, Published : November 06, 2012 Abstract Angiogenesis and invasion are essential processes for solid tumor growth and dissemination. The tumor development process can be dependent on the activation of a series of signaling pathways, including growth factor-activated pathways. MicroRNAs have been shown to be critical for tumorigenesis, but their roles in cancer angiogenesis, invasion and other signaling pathways important for tumor development are still unclear in the context of tumor biology. We investigated the role of microRNA miR-98 in regulating tumor growth, invasion, and angiogenesis using a highly aggressive breast cancer model in vitro and in vitro. We found that the expression of miR-98 inhibited breast cancer cell proliferation, survival, tumor growth, invasion, and angiogenesis. Conversely, inhibition of endogenous miR-98 promoted cell proliferation, survival, tumor growth, invasion, and angiogenesis. It appeared that miR-98 inhibited angiogenesis by modulating endothelial cell activities including cell spreading, cell invasion and tubule formation. Interestingly, miR-98 reduced the expression of ALK4 and MMP11, both of which were potential targets of miR-98. Transfection of an anti-miR-98 construct increased the expression of both targets. We confirmed that mir-98 targeted the 3’-untranslated regions of ALK4 and MMP11. Finally, ALK4- and MMP11-specific siRNAs inhibited breast cancer cell proliferation, survival, and angiogenesis. Rescue experiments with ALK4 and MMP11 constructs reversed the anti-proliferative, anti-invasive and anti-angiogenic effects of miR-98. Our findings define a regulatory role of miR-98 in tumor angiogenesis and invasion through repressed ALK4 and MMP11 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 score0.001
Threshold uncertainty score0.005

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.232
Teacher spread0.224 · 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

Citations133
Published2012
Admission routes3
Has abstractyes

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