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Record W4362665503 · doi:10.3390/ph16040557

Overexpression of Krüppel-Like Factor 9 Enhances the Antitumor Properties of Paclitaxel in Malignant Melanoma-Derived Cell Lines

2023· article· en· W4362665503 on OpenAlexaff
Mohammed O. Altonsy, George X. Song‐Zhao, Mahmoud Mostafa, P. Régine Mydlarski

Bibliographic record

VenuePharmaceuticals · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicKruppel-like factors research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPaclitaxelMelanomaCancer researchMedicineApoptosisChemotherapyCell cultureMAPK/ERK pathwayPharmacologyKinaseOncologyBiologyInternal medicine

Abstract

fetched live from OpenAlex

Over the past decade, the treatment of metastatic melanoma has improved significantly due to the development of innovative therapies, such as drugs that target the BRAF/MAPK kinase pathway and the PD-1 pathway. However, these therapies do not work for all patients, highlighting the need for additional research on the pathophysiology of melanoma. Paclitaxel is a chemotherapeutic agent used when first-line treatments are unsuccessful; however, its efficacy is limited. Since Krüppel-like factor 9 (KLF9) (antioxidant repressor) is downregulated in melanoma, we propose that restoring KLF9 levels may sensitize malignant melanoma to chemotherapeutic agents, such as paclitaxel. We used adenovirus overexpression and siRNA technologies to assess the role of KLF9 in mediating the response of malignant melanoma-derived cell lines RPMI-7951 and A375 to paclitaxel treatment. We found that increasing KLF9 levels potentiates the effectiveness of paclitaxel, as shown by apoptotic parameters such as decreased cell viability, pro-caspase-3 activation, increased number of annexin V-positive cells, and reduction in nuclear proliferation marker (KI67). These results suggest that KLF9 may be a potential target for improving chemotherapeutic response in melanoma.

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 score0.002
Threshold uncertainty score0.432

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 head0.070
GPT teacher head0.356
Teacher spread0.287 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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