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Record W7005929389

The role of the metastasis suppressor gene «KISS1» in uveal melanoma

2014· other· en· W7005929389 on OpenAlexvenueno aff

Bibliographic record

VenueLibrary and Archives Canada (Government of Canada) · 2014
Typeother
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDevelopmental Biology and Gene Regulation
Canadian institutionsnot available
Fundersnot available
KeywordsMetastasis Suppressor GeneMelanomaMetastasisMetastasis suppressorCancerDiseaseDownregulation and upregulationSuppressorTumor suppressor gene
DOInot available

Abstract

fetched live from OpenAlex

Uveal Melanoma (UM) is the most common intraocular tumor in adults. Liver metastasis is the leading cause of death in patients affected by this disease and in approximately 40% of the cases, metastasis occurs 10 years after initial diagnosis. Tumor dormancy has been considered as a leading theory for the delay of the manifestation of metastatic disease and it has been the subject of numerous studies, which includes investigating metastasis suppressor genes (MSG). Studies have shown that the MSG KISS1 plays a role in various human malignancies, including melanoma, and it seems to be involved in the dormancy phase of the metastatic cascade. Previous studies from our laboratory reported that loss of KISS1 expression is related to worse prognosis in UM. In this light, mechanisms that involve increase of KISS1 expression are of interest for UM researchers. Interestingly, pathways involved in UM progression include upregulation of c-KIT, a cell surface molecule normally found in melanoma cells. This process seems to occur at the same time that KISS1 is downregulated and overt metastasis become clinically detectable. Therefore, we verified if inhibition of c-KIT could be related to KISS1 expression in metastatic UM. In addition, considering that a newly identified class of small non coding RNAs (miRNAs) are master regulators of gene expression, we sought to investigate if miRNAs were involved in metastasis of UM. Therefore, the objective of this thesis was to identify mechanisms that increase the expression of KISS1 and to better understand UM metastasis. To address these questions, we used a c-KIT inhibitor, imatinib mesylate (IM), and miRNAs in this study. Human UM cell lines with different metastatic potential showed increased levels of KISS1, by real-time reverse transcriptase polymerase chain reaction (RT-PCR), after treatment with IM. Notably, an increase in KISS1 was observed at the protein level in a dose response manner when the most aggressive UM cell line (92.1) was treated with different concentrations of IM. Increased levels of KISS1 expression were confirmed in vivo using an experimental animal model (albino rabbits). Using a miRNA array, we were able to identify miRNAs with prometastatic and antimetastatic effects in different UM cell lines and under diverse conditions. The miRNAs 10a, 10b, 21, and let-7 were upregulated in a liver metastatic cell line compared to the primary UM cell line from the same patient. Additionally, the UM cell line with aggressive potential transfected with KISS1 showed decreased expression of the miR-221 and increased expression of miR-146 compared to the non-transfected control. Similar results were obtained when the same cell line was treated with IM. In order to translate these results to a clinical setting, in situ hybridization of miR-221 was performed in 15 human UM FFPE tissues; increased expression of miR-221 was positively correlated with metastasis in UM. In conclusion, KISS1 was upregulated following treatment with IM. In addition, down regulation of miR-221 was found in all miRNA arrays with increases in KISS1 and treatment with IM. To the best of our knowledge, this is the first study to show that treatment with a c-KIT inhibitor causes an upregulation of KISS1, and that miR-221 may promote metastasis in UM. Consequently, induction of KISS1 expression downregulates miR-221 and should be considered as potential targets for adjuvant therapy in UM metastasis.

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: Other · Consensus signal: none
Teacher disagreement score0.759
Threshold uncertainty score0.955

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 head0.001
GPT teacher head0.132
Teacher spread0.131 · 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
GenreOther

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
Published2014
Admission routes1
Has abstractyes

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