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Record W4293104491 · doi:10.3390/curroncol29080462

Palbociclib Induces the Apoptosis of Lung Squamous Cell Carcinoma Cells via RB-Independent STAT3 Phosphorylation

2022· article· en· W4293104491 on OpenAlexvenueno aff
Wenjing Xiang, Wanchen Qi, Huayu Li, Jia Sun, Chao Dong, Haojie Ou, Bing Liu

Bibliographic record

VenueCurrent Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicAdvanced Breast Cancer Therapies
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsPalbociclibCancer researchMedicineCyclin-dependent kinaseApoptosisTyrosine kinaseCell cycleBiologyCancerInternal medicineReceptorBreast cancer

Abstract

fetched live from OpenAlex

Lung squamous cell carcinoma (LUSC) treatment response is poor and treatment alternatives are limited. Palbociclib, a cyclin-dependent kinase (CDK) 4/6 inhibitor, has recently been approved for hormone receptor-positive breast cancer patients and applied in multiple preclinical models, but its use for LUSC therapy remains elusive. Here, we investigated whether palbociclib induced cell apoptosis and dissected the underlying mechanism in LUSC. We found that palbociclib induced LUSC cell apoptosis through inhibition of Src tyrosine kinase/signal transducers and activators of transcription 3 (STAT3). Interestingly, palbociclib reduced STAT3 signaling in LUSC cells interfered by retinoblastoma tumor-suppressor gene (RB), suggesting that pro-apoptosis effect of palbociclib was independent of classic CDK4/6-RB signaling. Furthermore, palbociclib could suppress IL-1β and IL-6 expression, and therefore blocked Src/STAT3 signaling, which were rescued by either recombinant human IL-1β or IL-6. Moreover, Myc mediated the sensitivity of LUSC cells to palbociclib. Our discoveries demonstrated that palbociclib induces apoptosis of LUSC cells through the Src/STAT3 axis in an RB-independent manner, and provided a reliable experimental basis of clinical studies in LUSC patients.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.630
Threshold uncertainty score0.683

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.041
GPT teacher head0.340
Teacher spread0.299 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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