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Record W4281252081 · doi:10.3390/cancers14102540

Angiopoietin-1 Upregulates Cancer Cell Motility in Colorectal Cancer Liver Metastases through Actin-Related Protein 2/3

2022· article· en· W4281252081 on OpenAlexafffund
Miran Rada, Audrey Kapelanski‐Lamoureux, Migmar Tsamchoe, Stephanie Petrillo, Anthoula Lazaris, Peter Metrakos

Bibliographic record

VenueCancers · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAngiogenesis and VEGF in Cancer
Canadian institutionsMcGill University Health Centre
FundersMcGill University Health CentreMcGill University
KeywordsIn vivoCancer researchMotilityPI3K/AKT/mTOR pathwayIn vitroMetastasisCancerChemistryColorectal cancerProtein kinase BCell biologyMedicineInternal medicineSignal transductionBiologyBiochemistry

Abstract

fetched live from OpenAlex

Resistance to anti-angiogenic therapy is a major challenge in the treatment of colorectal cancer liver metastases (CRCLMs). Vessel co-option has been identified as a key contributor to anti-angiogenic therapy resistance in CRCLMs. Recently, we identified a positive correlation between the expression of Angiopoietin1 (Ang1) in the liver and the development of vessel co-opting CRCLM lesions in vivo. However, the mechanisms underlying its stimulation of vessel co-option are unclear. Herein, we demonstrated Ang1 as a positive regulator of actin-related protein 2/3 (ARP2/3) expression in cancer cells, in vitro and in vivo, which is known to be essential for the formation of vessel co-option in CRCLM. Significantly, Ang1-dependent ARP2/3 expression was impaired in the cancer cells upon Tie2 or PI3K/AKT inhibition in vitro. Taken together, our results suggest novel mechanisms by which Ang1 confers the development of vessel co-option in CRCLM, which, targeting this pathway, may serve as promising therapeutic targets to overcome the development of vessel co-option in CRCLM.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.055
Threshold uncertainty score1.000

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)0.0030.000

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 head0.014
GPT teacher head0.262
Teacher spread0.248 · 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.

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
Published2022
Admission routes2
Has abstractyes

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