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Record W2950489885 · doi:10.1073/pnas.1814044115

Oncogenic role of SFRP2 in p53-mutant osteosarcoma development via autocrine and paracrine mechanism

2018· article· en· W2950489885 on OpenAlexaff
Huensuk Kim, Seungyeul Yoo, Ruoji Zhou, An Xu, Jeffrey M. Bernitz, Ye Yuan, Andreia Gomes, Michael G. Daniel, Jie Su, Elizabeth G. Demicco, Jun Zhu, Kateri Moore, Dung‐Fang Lee, Ihor R. Lemischka, Christoph Schaniel

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHippo pathway signaling and YAP/TAZ
Canadian institutionsMount Sinai Hospital
FundersNational Institute of General Medical SciencesNational Institutes of HealthNational Cancer InstituteIcahn School of Medicine at Mount SinaiCancer Prevention and Research Institute of TexasNew York State Department of Health
KeywordsParacrine signallingAutocrine signallingWnt signaling pathwayCancer researchFOXM1BiologyCYR61Beta-cateninAngiogenesisCancerGeneticsSignal transductionCell cycleGrowth factorCell culture

Abstract

fetched live from OpenAlex

Significance Li–Fraumeni syndrome is a rare disorder caused by germline TP53 mutations, predisposing patients to early-onset cancers, including osteosarcoma (OS). Here we demonstrate that strong expression of SFRP2, a reported WNT antagonist, in OS patient samples correlates with poor survival and that SFRP2 overexpression suppresses normal osteoblast differentiation, promotes OS features, and facilitates angiogenesis via autocrine and paracrine mechanisms in an induced pluripotent stem cell disease model. We show that these SFRP2-mediated phenotypes are canonical WNT/β-catenin independent and are mediated through induction of oncogenes such as FOXM1 and CYR61. We further demonstrate that inhibition of SFRP2, FOXM1, or CYR61 represses tumorigenesis. Our data suggest that inhibition of SFRP2 should be explored clinically as a strategy for treatment patients with p53 mutation-associated OS.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.017
Threshold uncertainty score0.215

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.020
GPT teacher head0.277
Teacher spread0.258 · 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

Citations53
Published2018
Admission routes1
Has abstractyes

Explore more

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