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Record W2416506714 · doi:10.1080/01635581.2016.1180411

Luteolin Impacts on the DNA Damage Pathway in Oral Squamous Cell Carcinoma

2016· article· en· W2416506714 on OpenAlexfundno aff
Kellen Cristine Tjioe, Denise Tostes Oliveira, Julie Gavard

Bibliographic record

VenueNutrition and Cancer · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA Repair Mechanisms
Canadian institutionsnot available
FundersInstitute of Cancer ResearchInstitut National Du CancerLigue Contre le Cancer
KeywordsLuteolinCancer researchCancerCancer cellHaCaTCisplatinCytotoxic T cellPharmacologyDNA damageEpidermoid carcinomaChemistryBiologyIn vitroMedicineFlavonoidBiochemistryInternal medicineDNAChemotherapy

Abstract

fetched live from OpenAlex

Oral squamous cell carcinoma (OSCC) exhibited high chemoresistance to current treatments. Here we aimed at identifying and repositioning approved drugs that could be selectively toxic toward OSCC cells. Through a cell-based drug screening of 1,280 chemical molecules, we selected compounds lethal to oral cancer SCC-25 cells, while sparing normal keratinocyte HaCaT cells. Within the chemical library, the natural flavonoid luteolin was identified as a potent cytotoxic agent against oral cancer cells in vitro, along with metixene hydrochloride and nitazoxanide. Of note, they exhibit low toxicity and high efficiency compared to the standard-of-care, such as cisplatin and the epidermal growth factor receptor inhibitor tyrphostin. From a molecular standpoint, luteolin causes phosphorylation of ataxia telangiectasia mutated (ATM) and H2AX in a DNA repair pathway and can be efficiently combined with a checkpoint kinase (CHK) pharmacological inhibitor. Thus, luteolin emerges as a potent cytotoxic and/or adjuvant therapy in oral cancer, as it is a natural compound presenting better effects in vitro compared to conventional chemotherapeutic agents. Future in vivo exploration is next required to provide the proof-of-concept that luteolin could be an efficient anticancer molecule.

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 score0.035
Threshold uncertainty score0.177

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.012
GPT teacher head0.240
Teacher spread0.228 · 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

Citations25
Published2016
Admission routes1
Has abstractyes

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