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Record W1583029732 · doi:10.1002/jcb.25159

Oncogenic and Therapeutic Targeting of PTEN Loss in Bone Malignancies

2015· review· en· W1583029732 on OpenAlex
Yongming Xi, Yan Chen

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Cellular Biochemistry · 2015
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPI3K/AKT/mTOR signaling in cancer
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsPTENCancer researchPI3K/AKT/mTOR pathwayLoss of heterozygosityCarcinogenesisBiologyTumor suppressor geneCancerProtein kinase BOsteosarcomaLoss functionSuppressorSignal transductionGeneCell biologyPhenotypeGenetics

Abstract

fetched live from OpenAlex

Being a tumor suppressor, PTEN functions as a dual-specificity protein and phospholipid phosphatase and regulates a variety of cellular processes and signal transduction pathways. Loss of PTEN function has been detected frequently in different forms of cancers, such as breast, prostate and lung cancer, gastric and colon cancer, skin cancer, as well as endometrial carcinoma. In this review, we provide a summary of PTEN and its role in bone malignancies including bone metastases, multiple myeloma, and osteosarcoma, etc. We highlight the importance of PTEN loss leading to activation of the oncogenic PI3K/Akt/mTOR pathway in tumorigenesis and progression, which can be attributed to both genetic and non-genetic alterations involving gene mutation, loss of heterozygosity, promoter hypermethylation, and microRNA mediated negative regulation. We also discuss the emerging therapeutic applications targeting PTEN loss for the treatment of these bone malignant diseases.

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.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.502
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.025
GPT teacher head0.303
Teacher spread0.278 · 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