MétaCan
Menu
Back to cohort
Record W2113264717 · doi:10.1093/carcin/bgq034

Secreted protein acidic and rich in cysteine-induced cellular senescence in colorectal cancers in response to irinotecan is mediated by P53

2010· article· en· W2113264717 on OpenAlexafffund
Justin M. Chan, Shirley Ho, Isabella T. Tai

Bibliographic record

VenueCarcinogenesis · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Research and Treatments
Canadian institutionsUniversity of British ColumbiaBC Cancer Agency
FundersCanadian Institutes of Health Research
KeywordsIrinotecanSenescenceColorectal cancerCysteineCancer researchCellular senescenceInternal medicineChemistryBiologyMedicineOncologyCancerBiochemistryEnzymePhenotypeGene

Abstract

fetched live from OpenAlex

Cellular senescence is another mechanism that can be exploited to achieve better chemosensitivity and greater tumor regression. Unlike apoptosis, cellular senescence can be induced at much lower concentrations of chemotherapy that are better tolerated by patients. We previously revealed that secreted protein acidic and rich in cysteine (SPARC), a matricellular protein, may function as a modulator of chemotherapy sensitivity by enhancing apoptosis. Here, we examine the effects of SPARC on cellular senescence in the presence of chemotherapy. Cellular senescence is induced only in sensitive colorectal cancer (CRC) cells with low concentrations of irinotecan (CPT-11). However, CPT-11-resistant cells exposed to endogenous or exogenous SPARC can also be triggered into cellular senescence. This induction is associated with higher levels of p16(INK4A) and phosphorylated p53. Knock down of p16(INK4A) reduces drug-induced senescence in all cells, but knock down and overexpression of p53 modulates senescence only in cells exposed to SPARC. Furthermore, treatment of mice with SPARC and CPT-11 leads to significantly increased cellular senescence and tumor regression. The chemosensitizing effects of SPARC in CRCs are, therefore, probably mediated in part by activating cellular senescence.

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)0.000
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 score0.034
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.008
GPT teacher head0.253
Teacher spread0.245 · 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

Citations21
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueCarcinogenesisSame topicCancer Research and TreatmentsFrench-language works237,207