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Record W4298111048 · doi:10.3389/fcell.2022.896297

Loss of the fructose transporter SLC2A5 inhibits cancer cell migration

2022· article· en· W4298111048 on OpenAlexafffund
Jody Groenendyk, Konstantin Stoletov, Tautvydas Paškevičius, Wenjuan Li, Ning Dai, Myriam Pujol, Erin Busaan, Hoi Hei Ng, Aristeidis E. Boukouris, Bruno Saleme, Alois Haromy, Kaisa Cui, H. Miao, Yanan Yan, Rui Zhang, Evangelos D. Michelakis, Xing‐Zhen Chen, John D. Lewis, Jingfeng Tang, Luis B. Agellon, Marek Michalak

Bibliographic record

VenueFrontiers in Cell and Developmental Biology · 2022
Typearticle
Languageen
FieldMedicine
TopicDiet, Metabolism, and Disease
Canadian institutionsMcGill UniversityUniversity of Alberta
FundersCanadian Institutes of Health ResearchMultiple Sclerosis SocietyNatural Sciences and Engineering Research Council of CanadaMultiple Sclerosis Society of CanadaChildren's Health Research Institute
KeywordsCancerMetastasisMotilityCancer cellBiologyCancer researchGene silencingCell migrationCellCell biologyGeneGenetics

Abstract

fetched live from OpenAlex

Metastasis is the primary cause of cancer patient death and the elevation of SLC2A5 gene expression is often observed in metastatic cancer cells. Here we evaluated the importance of SLC2A5 in cancer cell motility by silencing its gene. We discovered that CRISPR/Cas9-mediated inactivation of the SLC2A5 gene inhibited cancer cell proliferation and migration in vitro as well as metastases in vivo in several animal models. Moreover, SLC2A5-attenuated cancer cells exhibited dramatic alterations in mitochondrial architecture and localization, uncovering the importance of SLC2A5 in directing mitochondrial function for cancer cell motility and migration. The direct association of increased abundance of SLC2A5 in cancer cells with metastatic risk in several types of cancers identifies SLC2A5 as an important therapeutic target to reduce or prevent cancer 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.302
Threshold uncertainty score0.299

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 head0.007
GPT teacher head0.216
Teacher spread0.209 · 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 designObservational
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

Citations12
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueFrontiers in Cell and Developmental BiologySame topicDiet, Metabolism, and DiseaseFrench-language works237,207