MétaCan
Menu
Back to cohort
Record W934854646 · doi:10.1038/srep12357

IRGM1 enhances B16 melanoma cell metastasis through PI3K-Rac1 mediated epithelial mesenchymal transition

2015· article· en· W934854646 on OpenAlexaff
Linlu Tian, Lixian Li, Wenjing Xing, Rui Li, Dong Xiao, Yanran Fu, Changcong Gu, Xize Guo, Yulong Jia, Guangyou Wang, Jing‐Hua Wang, Bo Li, Huan Ren, Hongwei Xu

Bibliographic record

VenueScientific Reports · 2015
Typearticle
Languageen
FieldMedicine
TopicCancer Cells and Metastasis
Canadian institutionsMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsRAC1MelanomaEpithelial–mesenchymal transitionMetastasisCell migrationCancer researchCell biologyCellBiologyChemistryCancerSignal transductionBiochemistry

Abstract

fetched live from OpenAlex

Melanoma is one of the most aggressive skin cancers and is well known for its high metastatic rate. Studies have shown that epithelial mesenchymal transition (EMT) is essential for melanoma cell metastasis. However, the molecular mechanisms underlying EMT are still not fully understood. We have shown that IRGM1, a member of immunity-related GTPase family that regulates immune cell motility, is highly expressed by melanoma cells. The current study aimed to explore whether and how IRGM1 may regulate melanoma cell metastasis. To test this, we modified IRGM1 expression in B16 melanoma cells. We found that over-expression of IRGM1 substantially enhanced pulmonary metastasis in vivo. In keeping with that, knocking-in IRGM1 strongly enhanced while knocking-down IRGM1 impaired B16 cell migration and invasion ability in vitro. Interestingly, we observed that IRGM1 enhanced F-actin polymerization and triggers epithelial mesenchymal transition (EMT) through a mechanism involved in PIK3CA mediated Rac1 activation. Together, these data reveals a novel molecular mechanism that involved in melanoma 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 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 score0.001
Threshold uncertainty score0.005

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)0.0010.000

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 head0.033
GPT teacher head0.281
Teacher spread0.248 · 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

Citations27
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueScientific ReportsSame topicCancer Cells and MetastasisFrench-language works237,207