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Record W2115492288 · doi:10.1111/pcmr.12399

The role of the metastasis suppressor gene <scp>KAI</scp>1 in melanoma angiogenesis

2015· article· en· W2115492288 on OpenAlexafffund
Yun Tang, Madhuri Bhandaru, Yabin Cheng, Jing Lü, Gang Li, Christopher J. Ong

Bibliographic record

VenuePigment Cell & Melanoma Research · 2015
Typearticle
Languageen
FieldMedicine
TopicCell Adhesion Molecules Research
Canadian institutionsVancouver Coastal Health Research InstituteUniversity of British Columbia
FundersCanadian Cancer Society Research InstituteCanadian Institutes of Health ResearchTerry Fox Research InstituteProstate Cancer Canada
KeywordsAngiogenesisMatrigelCancer researchMelanomaProtein kinase BMetastasisCD31Downregulation and upregulationCell migrationNeovascularizationUmbilical veinBiologyCell biologyPhosphorylationIn vitroMedicineCancerInternal medicineGene

Abstract

fetched live from OpenAlex

The tetraspan protein KAI1 (CD82) has been previously shown to have important roles in cell migration, invasion, and melanoma prognosis. In this study, we investigated the role of KAI1 regarding melanoma angiogenesis. KAI1 overexpression strongly suppressed the growth of the human umbilical vein endothelial cells and their tubular structure formation in vitro. Also, KAI1 was able to inhibit both interleukin-6 (IL-6) and VEGF at mRNA and protein levels. Using nude mice in the in vivo study, we showed that KAI1, through the regulation of ING4, inhibited blood vessel formation in matrigel plugs along with the downregulation of IL-6 and VEGF, and the recruitment of CD31-positive cells. Finally, we found that KAI1 was able to suppress the activity of a serine/threonine kinase Akt by suppressing Akt phosphorylation (Ser473). Taken together, our results suggested that KAI1 was able to suppress melanoma angiogenesis by downregulating IL-6 and VEGF expression, and the restoration of KAI1 functionality offered a new approach in human melanoma treatment.

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)0.006
metaresearch head score (Gemma)0.002
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 score0.095
Threshold uncertainty score0.773

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.056
GPT teacher head0.330
Teacher spread0.274 · 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

Citations13
Published2015
Admission routes2
Has abstractyes

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