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Record W1984901059 · doi:10.1158/1538-7445.am2011-4917

Abstract 4917: PRKCE and other genetic networks in osteosarcoma metastasis

2011· article· en· W1984901059 on OpenAlexaff
Atta Goudarzi, Nalan Gökgöz, MONTY GILL, Jay S. Wunder, Irene L. Andrulis

Bibliographic record

VenueCancer Research · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMolecular Biology Techniques and Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsOsteosarcomaMetastasisGeneMedicineCancerInteractomeOncologyCancer researchBiologyInternal medicineGenetics

Abstract

fetched live from OpenAlex

Abstract Osteosarcoma (OS) is the most common malignant bone cancer with approximately 1, 000 new cases reported annually in North America. Unfortunately OS has a poor 5-year survival rate (∼60%), and this is attributable largely to its propensity towards pulmonary metastasis: roughly a fifth (20%) of newly diagnosed cases are accompanied by macroscopic pulmonary metastases, and it is estimated that four fifths (80%) of all cases have undetectable microscopic metastases at diagnosis. The aim of the present study was to identify and characterize genetic networks that contribute to the metastasis of OS. Gene-expression profiling was performed on a cohort of 63 primary OS tumor samples: 46 with undetectable metastasis at diagnosis (‘localized’), and 17 that had detectable pulmonary metastasis at time of diagnosis (‘metastatic’). The relative abundance of transcripts from approximately 9, 000 genes was measured. This information was overlain on the Pathway Commons human interactome, and two independent methods of supervised network analysis were performed. Those networks with significantly differential gene expression, or ‘activity’ (Chuang et al. Mol. Sys. Biol. 2007), and those networks with significantly differential internal gene correlation, or ‘organization’ (Taylor et al. Nature Biotech. 2009) were discovered. Networks were assigned to biological processes using Gene Ontology Slim Generic. Real-time PCR was performed in vitro and in vivo to corroborate and validate the initial findings. The supervised network analysis has elucidated the 43 most significantly differentially activated, and 11 most significantly differentially organized networks in metastatic OS. One candidate differentially activated network, comprising RASGRP3, PRKCE and GNB2, was selected for follow-up. In vitro analysis in a panel of paired OS cell lines has corroborated the findings of the profiling screen: RASGRP3 was found to be under-expressed in some highly metastatic sub-lines, and PRKCE was found to be over-expressed in some highly metastatic sub-lines. Independent validation of PRKCE expression in the original tumor cohort has confirmed the increased expression in metastatic samples. A less stringent inspection of the network results reveals global trends in network aberrations: differentially activated networks are commonly found in transport, translation and protein modification processes, while dis-organized networks are commonly found in metabolism, signaling, transcription and organization processes. The study here described has revealed significant network aberrations in metastatic OS primary tumours. Additionally, several high-confidence networks have been identified for detailed follow-up. Subsequent in vitro and in vivo expression analysis has confirmed that one network is differentially expressed in metastatic OS. Knockdown assays are ongoing to demonstrate the impact of this network on OS metastatic progression. 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 4917. doi:10.1158/1538-7445.AM2011-4917

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.087
GPT teacher head0.391
Teacher spread0.304 · 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 designNot applicable
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".

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Citations0
Published2011
Admission routes1
Has abstractyes

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