MétaCan
Menu
Back to cohort
Record W4285742571 · doi:10.3389/fcell.2022.881297

The Tumor Suppressor Kinase LKB1: Metabolic Nexus

2022· review· en· W4285742571 on OpenAlexafffund
Mohammed Bourouh, Paola A. Marignani

Bibliographic record

VenueFrontiers in Cell and Developmental Biology · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism, Diabetes, and Cancer
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Cancer SocietyDalhousie UniversityCancer Research Society
KeywordsCancer researchAMPKBiologySTK11CancerProtein kinase AKinaseCell biologyKRASColorectal cancerGenetics

Abstract

fetched live from OpenAlex

Liver kinase B1 (LKB1) is a multitasking tumor suppressor kinase that is implicated in multiple malignancies such as lung, gastrointestinal, pancreatic, and breast. LKB1 was first identified as the gene responsible for Peutz-Jeghers syndrome (PJS) characterized by hamartomatous polyps and oral mucotaneous pigmentation. LKB1 functions to activate AMP-activated protein kinase (AMPK) during energy stress to shift metabolic processes from active anabolic pathways to active catabolic pathways to generate ATP. Genetic loss or inactivation of LKB1 promotes metabolic reprogramming and metabolic adaptations of cancer cells that fuel increased growth and division rates. As a result, LKB1 loss is associated with increased aggressiveness and treatment options for patients with LKB1 mutant tumors are limited. Recently, there has been new insights into the role LKB1 has on metabolic regulation and the identification of potential vulnerabilities in LKB1 mutant tumors. In this review, we discuss the tumor suppressive role of LKB1 and the impact LKB1 loss has on metabolic reprograming in cancer cells, with a focus on lung cancer. We also discuss potential therapeutic avenues to treat malignancies associated with LKB1 loss by targeting aberrant metabolic pathways associated with LKB1 loss.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.976
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.016
GPT teacher head0.257
Teacher spread0.241 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations36
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueFrontiers in Cell and Developmental BiologySame topicMetabolism, Diabetes, and CancerFrench-language works237,207