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Record W4402708987 · doi:10.1158/1538-7755.disp24-c111

Abstract C111: Characterization of kisspeptin receptor signaling pathways in cervical cancer: Unveiling novel mechanisms and therapeutic potentials

2024· article· en· W4402708987 on OpenAlexaff
Deisy Yurley Rodríguez-Sarmiento, Pedro Henrique Scarpelli-Pereira, Michel Bouvier

Bibliographic record

VenueCancer Epidemiology Biomarkers & Prevention · 2024
Typearticle
Languageen
FieldNeuroscience
TopicNeuropeptides and Animal Physiology
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsKisspeptinCervical cancerMedicineSignal transductionCancer researchCancerReceptorNeuroscienceInternal medicineBiologyCell biology

Abstract

fetched live from OpenAlex

Abstract Introduction: This study delves into the complex signaling mechanisms of the kisspeptin system in cervical cancer, aiming to unravel the intricate interactions between kisspeptin and its receptor, as well as their roles in modulating cancer progression. Given the emerging significance of the kisspeptin system in tumor biology, our research seeks to illuminate how kisspeptin signaling influences cellular behaviors critical to cancer development, such as proliferation and migration. By exploring the nuanced effects of kisspeptin receptor activation, this study contributes to a deeper understanding of its therapeutic potential and underscores the importance of kisspeptin pathways in the broader context of cancer research. Methods: Was employed Bioluminiscence Resonance Energy Transfer (BRET) assays to explore the activation of the kisspeptin receptor in cervical cancer cell lines, using synthesized analogs of kisspeptin-10 for detailed signaling analysis. Functional effects were assessed through proliferation and migration assays. Additionally, we evaluated the activation or reduction in phosphorylation of specific kinases involved in tumoral processes, providing a nuanced understanding of the signaling mechanisms at play. Results: Our data demonstrate that activation of the kisspeptin receptor substantially modulates cellular proliferation and motility in cervical cancer cell lines. We uncovered novel activation of the Gz protein by kisspeptin-10, which is integral to the kisspeptin receptor function, and identified an upregulation of signaling pathways critical for cell survival and metastasis. Importantly, we observed activation of kinases such as Chk2, c-Jun, p70 S6 kinase, and RSK 1/2/3, as well as members of the STAT family, following kisspeptin-10 stimulation. This kinase activation underscores the multifaceted role of kisspeptin in tumor progression, suggesting its involvement in both pro-oncogenic and tumor-suppressive pathways, and provides direct insights into the mechanistic underpinnings of kisspeptin effect on cancer cell behavior. Conclusion: In conclusion, our study elucidates the complex role of kisspeptin-10 in cervical cancer, demonstrating its ability to modulate cellular proliferation and motility through activation of the kisspeptin receptor. The novel activation of the Gz protein and subsequent upregulation of signaling pathways associated with cell survival and metastasis highlight the intricate involvement of kisspeptin in cancer progression. Furthermore, the activation of key kinases such as Chk2, c-Jun, p70 S6 kinase, RSK 1/2/3, and STAT family members upon kisspeptin-10 stimulation reveals a dual-faceted influence on tumor dynamics, promoting both pro-oncogenic and tumor-suppressive mechanisms. These findings provide significant insights into the molecular actions of kisspeptin in cervical cancer, offering potential targets for therapeutic intervention and a deeper understanding of its role in tumor biology. Citation Format: Deisy Y. Rodríguez-Sarmiento, Pedro H. Scarpelli-Pereira, Michel Bouvier. Characterization of kisspeptin receptor signaling pathways in cervical cancer: Unveiling novel mechanisms and therapeutic potentials [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C111.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

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)0.0030.001

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 head0.091
GPT teacher head0.341
Teacher spread0.250 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2024
Admission routes1
Has abstractyes

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