MétaCan
Menu
Back to cohort

TCTP Silencing in Ovarian Cancer Cells Results in Actin Cytoskeleton Remodeling and Motility Increase

2015· article· en· W2223962288 on OpenAlexvenueno aff
Yianzhu Liu, Li Zhang, Neelam Tejpal, Jacek Z. Kubiak, Rafik M. Ghobrial, Li X, Małgorzata Kloc

Bibliographic record

VenueJournal of Analytical Oncology · 2015
Typearticle
Languageen
FieldMedicine
TopicCancer, Stress, Anesthesia, and Immune Response
Canadian institutionsnot available
Fundersnot available
KeywordsMotilityCell biologyGene silencingActin cytoskeletonActinCytoskeletonBiologySmall interfering RNACellCell migrationCancer cellCancer researchCell cultureCancerGeneticsTransfectionGene

Abstract

fetched live from OpenAlex

Translationally Controlled Tumor-associated Protein (TCTP) plays a role in a plethora of normal and cancer cell functions including cell cycle progression, cell growth and metastasis. Our previous studies showed that TCTP interacts with cellular cytoskeleton and is localized, in cell-type specific manner, on actin filaments in various types of ovarian cancer cells. Here we used small interfering RNA (siRNA) for silencing TCTP expression in human ovarian surface epithelial noncancerous cell line HIO180, ovarian carcinoma cell lines SKOV3 and OVCAR3 and analyzed effect of TCTP silencing on actin cytoskeleton and cell motility. We show that a down regulation of TCTP caused dramatic restructuring and redistribution of actin filaments in HIO180, SKOV3 and OVCAR3 cells and resulted in cell motility increase. This previously unidentified dependence of actin cytoskeleton remodeling and cell motility on TCTP level might be responsible for high metastatic potential and aggressiveness of ovarian cancer cells and will help to pinpoint novel targets for anticancer therapies..

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 score0.005

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.049
GPT teacher head0.357
Teacher spread0.308 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Analytical OncologySame topicCancer, Stress, Anesthesia, and Immune ResponseFrench-language works237,207