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Record W2318574019 · doi:10.1158/1538-7445.am10-1052

Abstract 1052: RNAi-mediated mutual regulation of FGF-2 and its antisense gene, NUDT6, in C6 glioma cells.

2010· article· en· W2318574019 on OpenAlexaff
Mark Baguma‐Nibasheka, Paul R. Murphy

Bibliographic record

VenueCancer Research · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsDalhousie University
Fundersnot available
KeywordsGene knockdownRNA interferenceBiologyCell cycleMolecular biologyCell growthFibroblast growth factorFlow cytometryCell biologyCellChemistryCell cultureGeneRNAGeneticsReceptor

Abstract

fetched live from OpenAlex

Abstract Basic fibroblast growth factor (FGF-2) is a potent wide-spectrum antiapoptotic mitogen, and the dysregulation of its expression is associated with cell proliferation and immortalization in a wide variety of tumors. We have previously reported that FGF-2 and NUDT6, the product of its antisense gene, are co-localized and inversely expressed in C6 glioma cells, and that constitutive overexpression of NUDT6 suppresses cellular FGF-2 expression, nuclear translocation, and the resumption of cell cycles (Baguma-Nibasheka et al., Mol Cell Endocrinol, 267:127-136, 2007). For the current report, we investigated the role of the endogenous RNAi machinery in mediating the post-transcriptional effects of NUDT6 on FGF-2 expression. FGF-2 knockdown was accompanied by a significant (>3 fold) increase in NUDT6 mRNA, and vice versa. Similar converse effects were observed on the cognate protein levels, as assessed by confocal immunoflourescence, Western blotting, and flow cytometry, indicating that these two transcripts may be mutually regulatory. Remarkably, knockdown of either transcript reduced cell proliferation and inhibited S-phase re-entry following serum deprivation (Table 1), indicating that both FGF-2 and NUDT6 may be involved in the regulation of cell cycle progression. Supported by the CIHR and NSERC.Table 1.S-Phase Percentages1 of C6 Cells following Knockdown and G0 Arrest.Hours after Return to 10% FBS Medium0361224Control siRNA4.8 ± 0.516.8 ± 1.321.6 ± 1.822.4 ± 1.123.0 ± 1.5NUDT6 siRNA4.1 ± 0.510.6 ± 1.3*14.2 ± 1.6*16.7 ± 1.8*19.8 ± 0.8FGF-2 siRNA3.5 ± 0.55.9 ± 0.8*6.9 ± 1.2*11.2 ± 1.1*14.5 ± 1.0*1mean ± SEM. * = significantly different from control; p<0.05, n=3. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 1052.

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 score0.003
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
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.036
GPT teacher head0.360
Teacher spread0.323 · 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
Published2010
Admission routes1
Has abstractyes

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