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Record W2081011802 · doi:10.1158/1538-7445.am2011-1644

Abstract 1644: siRNA targeting of cell cycle kinase Wee1 inhibits hepatocullar carconima growth <i>in vitro</i> and <i>in vivo</i>

2011· article· en· W2081011802 on OpenAlexaff
Yun‐Han Lee, Jesper B. Andersen, Adam D. Judge, Daekwan Seo, Chiara Raggi, Marquardt U. Jens, Elizabeth A. Conner, Ian MacLachlan, Valentina M. Factor, Snorri S. Thorgeirsson

Bibliographic record

VenueCancer Research · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsArbutus Biopharma (Canada)
Fundersnot available
KeywordsWee1Cell cycleCancer researchGene silencingBiologySmall interfering RNACell cycle checkpointCell growthGene knockdownKinaseCyclin-dependent kinase 1ApoptosisChemistryMolecular biologyCell biologyCell cultureTransfectionGeneBiochemistryGenetics

Abstract

fetched live from OpenAlex

Abstract Development of targeted therapy for hepatocellular carcinoma (HCC) remains a major challenge. Previously studies have shown that protein level and kinase activity of Wee1 are significantly elevated in HCC compared with surrounding cirrhotic tissues, although the underlying mechanisms are still unknown. Under normal conditions, Wee1 kinase plays an important role in maintaining G2 arrest through the inhibitory phosphorylation of cdc2 on Tyr-15. In the present study, we explored the possibility of Wee1 being a potential therapeutic target for HCC. To inactivate Wee1, three Wee1-specific small interfering (si) RNAs (Wee1-1, Wee1-2 and Wee1-3) were tested for growth inhibition in HCC cell lines as determined by MTT assay, FACS analysis and microscopy. To obtain insights into molecular changes caused by Wee1 silencing, global changes in gene expression were examined by illumina microarray. For in vivo evaluation of Wee1 as a therapeutic target, we employed orthotopic xenograft model using luciferase-expressing HCC reporter cell lines Huh7- and HepG2-luc+ and stable-nucleic-acid-lipid-particle (SNALP) as an optimal carrier of siRNA into liver. Among the tested siRNA molecules, the Wee1-2siRNA was the most effective in inhibiting Huh7 and HepG2 cell growth (80% and 84%, respectively) which was paralleled by a similar decrease in the levels of target mRNA and protein. Wee1 knockdown by siRNA also caused a block in cell cycle progression and induced apoptosis of HCC cells. The comparison of gene expression profiles in HepG2 cells treated with either control siRNA or Wee1-2siRNA identified 506 differentially expressed genes (P < 0.05 by bootstrap t-test). Genes functionally involved in cell proliferation, such as cdk2, cyclin B1, and Akt1, were down-regulated while cell cycle inhibitor p21 and tumor suppressor TSC2 were up-regulated. Western blotting showed that Wee1 silencing significantly increased the expression of p53 and p21 and decreased cyclin D1 protein levels in Wee1-deficient HepG2 cells, which could contribute to cell cycle arrest and induction of apoptosis. Wee1 5/6, a modified variant of Wee1-2siRNA, was then selected for in vivo application based on the growth inhibitory effect and minimal induction of unwanted immune response. Systemic delivery of Wee1 5/6 variant by SNALP significantly suppressed both Huh7 and HepG2 tumor growth in orthotopic xenograft model. In addition, administration of Wee1 5/6 SNALP increased the survival of mice bearing HepG2-derived tumors bearing mice in a dose-dependent manner. Taken together, these results indicate that Wee1 maybe an attractive molecular target for systemic HCC therapy. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 1644. doi:10.1158/1538-7445.AM2011-1644

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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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)0.0020.001

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 head0.034
GPT teacher head0.306
Teacher spread0.272 · 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

Citations0
Published2011
Admission routes1
Has abstractyes

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