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Record W2335964622 · doi:10.1038/ncomms11169

A miR-192-EGR1-HOXB9 regulatory network controls the angiogenic switch in cancer

2016· article· en· W2335964622 on OpenAlexafffund
Sherry Y. Wu, Rajesha Rupaimoole, Fangrong Shen, Sunila Pradeep, Chad V. Pecot, Cristina Ivan, Archana S. Nagaraja, Kshipra M. Gharpure, Elizabeth Pham, Hiroto Hatakeyama, Michael H. McGuire, Monika Haemmerle, Viviana Vidal‐Anaya, Courtney Olsen, Cristian Rodriguez‐Aguayo, Justyna Filant, Ehsan A. Ehsanipour, Shelley M. Herbrich, Sourindra N. Maiti, Li Huang, Ji Hoon Kim, Xinna Zhang, Hee‐Dong Han, Guillermo N. Armaiz-Peña, Elena G. Seviour, Sue Tucker, Min Zhang, Da Yang, Laurence J.N. Cooper, Rouba Ali‐Fehmi, Menashe Bar‐Eli, Ju‐Seog Lee, Prahlad T. Ram, Keith Baggerly, Gabriel Lopez‐Berestein, Mien‐Chie Hung, Anil K. Sood

Bibliographic record

VenueNature Communications · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsSunnybrook HospitalSunnybrook Health Science Centre
FundersNational Center for Advancing Translational SciencesNational Institute of General Medical SciencesNational Cancer InstituteCanadian Institutes of Health ResearchDeutsche ForschungsgemeinschaftOvarian Cancer CanadaCancer Prevention and Research Institute of TexasU.S. Department of Health and Human ServicesNational Institutes of HealthOvarian Cancer Research FundRussell and Diana Hawkins Family FoundationRGK FoundationOvarian Cancer Research Fund AllianceU.S. Department of Defense
KeywordsAngiogenesismicroRNACancer researchRegulatorDownregulation and upregulationIn vivoCancerSuppressorIn vitroBiologyMedicineInternal medicineBiochemistryGene

Abstract

fetched live from OpenAlex

A deeper mechanistic understanding of tumour angiogenesis regulation is needed to improve current anti-angiogenic therapies. Here we present evidence from systems-based miRNA analyses of large-scale patient data sets along with in vitro and in vivo experiments that miR-192 is a key regulator of angiogenesis. The potent anti-angiogenic effect of miR-192 stems from its ability to globally downregulate angiogenic pathways in cancer cells through regulation of EGR1 and HOXB9. Low miR-192 expression in human tumours is predictive of poor clinical outcome in several cancer types. Using 1,2-dioleoyl-sn-glycero-3-phosphatidylcholine (DOPC) nanoliposomes, we show that miR-192 delivery leads to inhibition of tumour angiogenesis in multiple ovarian and renal tumour models, resulting in tumour regression and growth inhibition. This anti-angiogenic and anti-tumour effect is more robust than that observed with an anti-VEGF antibody. Collectively, these data identify miR-192 as a central node in tumour angiogenesis and support the use of miR-192 in an anti-angiogenesis therapy.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.865
Threshold uncertainty score0.317

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 science0.0010.000
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 head0.012
GPT teacher head0.289
Teacher spread0.277 · 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

Citations119
Published2016
Admission routes2
Has abstractyes

Explore more

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